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1. Joint-label fusion brain atlases for dementia research in Down syndrome.

Author: Queder, Nazek, Queder, Nazek, Phelan, Michael J, Taylor, Lisa, Tustison, Nicholas, Doran, Eric, Hom, Christy, Nguyen, Dana, Lai, Florence, Pulsifer, Margaret, Price, Julie, Kreisl, William C, Rosas, Herminia D, Krinsky-McHale, Sharon, Brickman, Adam M, Yassa, Michael A, Schupf, Nicole, Silverman, Wayne, Lott, Ira T, Head, Elizabeth, Mapstone, Mark, Keator, David B, Alzheimer's Biomarkers Consortium, Queder, Nazek, Queder, Nazek, Phelan, Michael J, Taylor, Lisa, Tustison, Nicholas, Doran, Eric, Hom, Christy, Nguyen, Dana, Lai, Florence, Pulsifer, Margaret, Price, Julie, Kreisl, William C, Rosas, Herminia D, Krinsky-McHale, Sharon, Brickman, Adam M, Yassa, Michael A, Schupf, Nicole, Silverman, Wayne, Lott, Ira T, Head, Elizabeth, Mapstone, Mark, Keator, David B, and Alzheimer's Biomarkers Consortium
Abstract: Research suggests a link between Alzheimer's Disease in Down Syndrome (DS) and the overproduction of amyloid plaques. Using Positron Emission Tomography (PET) we can assess the in-vivo regional amyloid load using several available ligands. To measure amyloid distributions in specific brain regions, a brain atlas is used. A popular method of creating a brain atlas is to segment a participant's structural Magnetic Resonance Imaging (MRI) scan. Acquiring an MRI is often challenging in intellectually-imparied populations because of contraindications or data exclusion due to significant motion artifacts or incomplete sequences related to general discomfort. When an MRI cannot be acquired, it is typically replaced with a standardized brain atlas derived from neurotypical populations (i.e. healthy individuals without DS) which may be inappropriate for use in DS. In this project, we create a series of disease and diagnosis-specific (cognitively stable (CS-DS), mild cognitive impairment (MCI-DS), and dementia (DEM-DS)) probabilistic group atlases of participants with DS and evaluate their accuracy of quantifying regional amyloid load compared to the individually-based MRI segmentations. Further, we compare the diagnostic-specific atlases with a probabilistic atlas constructed from similar-aged cognitively-stable neurotypical participants. We hypothesized that regional PET signals will best match the individually-based MRI segmentations by using DS group atlases that aligns with a participant's disorder and disease status (e.g. DS and MCI-DS). Our results vary by brain region but generally show that using a disorder-specific atlas in DS better matches the individually-based MRI segmentations than using an atlas constructed from cognitively-stable neurotypical participants. We found no additional benefit of using diagnose-specific atlases matching disease status. All atlases are made publicly available for the research community.HighlightDown syndrome (DS) joint-label-fusion a
Published: 2022

2. QU-BraTS : MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

Author: Mehta, Raghav, Filos, Angelos, Baid, Ujjwal, Sako, Chiharu, McKinley, Richard, Rebsamen, Michael, Dätwyler, Katrin, Meier, Raphael, Radojewski, Piotr, Murugesan, Gowtham Krishnan, Nalawade, Sahil, Ganesh, Chandan, Wagner, Ben, Yu, Fang F., Fei, Baowei, Madhuranthakam, Ananth J., Maldjian, Joseph A., Daza, Laura, Gómez, Catalina, Arbeláez, Pablo, Dai, Chengliang, Wang, Shuo, Reynaud, Hadrien, Mo, Yuanhan, Angelini, Elsa, Guo, Yike, Bai, Wenjia, Banerjee, Subhashis, Pei, Linmin, AK, Murat, Rosas-González, Sarahi, Zemmoura, Ilyess, Tauber, Clovis, Vu, Minh Hoang, Nyholm, Tufve, Löfstedt, Tommy, Ballestar, Laura Mora, Vilaplana, Veronica, McHugh, Hugh, Talou, Gonzalo Maso, Wang, Alan, Patel, Jay, Chang, Ken, Hoebel, Katharina, Gidwani, Mishka, Arun, Nishanth, Gupta, Sharut, Aggarwal, Mehak, Singh, Praveer, Gerstner, Elizabeth R., Kalpathy-Cramer, Jayashree, Boutry, Nicolas, Huard, Alexis, Vidyaratne, Lasitha, Rahman, Md Monibor, Iftekharuddin, Khan M., Chazalon, Joseph, Puybareau, Elodie, Tochon, Guillaume, Ma, Jun, Cabezas, Mariano, Llado, Xavier, Oliver, Arnau, Valencia, Liliana, Valverde, Sergi, Amian, Mehdi, Soltaninejad, Mohammadreza, Myronenko, Andriy, Hatamizadeh, Ali, Feng, Xue, Dou, Quan, Tustison, Nicholas, Meyer, Craig, Shah, Nisarg A., Talbar, Sanjay, Weber, Marc-André, Mahajan, Abhishek, Jakab, Andras, Wiest, Roland, Fathallah-Shaykh, Hassan M., Nazeri, Arash, Milchenko, Mikhail, Marcus, Daniel, Kotrotsou, Aikaterini, Colen, Rivka, Freymann, John, Kirby, Justin, Davatzikos, Christos, Menze, Bjoern, Bakas, Spyridon, Gal, Yarin, Arbel, Tal, Mehta, Raghav, Filos, Angelos, Baid, Ujjwal, Sako, Chiharu, McKinley, Richard, Rebsamen, Michael, Dätwyler, Katrin, Meier, Raphael, Radojewski, Piotr, Murugesan, Gowtham Krishnan, Nalawade, Sahil, Ganesh, Chandan, Wagner, Ben, Yu, Fang F., Fei, Baowei, Madhuranthakam, Ananth J., Maldjian, Joseph A., Daza, Laura, Gómez, Catalina, Arbeláez, Pablo, Dai, Chengliang, Wang, Shuo, Reynaud, Hadrien, Mo, Yuanhan, Angelini, Elsa, Guo, Yike, Bai, Wenjia, Banerjee, Subhashis, Pei, Linmin, AK, Murat, Rosas-González, Sarahi, Zemmoura, Ilyess, Tauber, Clovis, Vu, Minh Hoang, Nyholm, Tufve, Löfstedt, Tommy, Ballestar, Laura Mora, Vilaplana, Veronica, McHugh, Hugh, Talou, Gonzalo Maso, Wang, Alan, Patel, Jay, Chang, Ken, Hoebel, Katharina, Gidwani, Mishka, Arun, Nishanth, Gupta, Sharut, Aggarwal, Mehak, Singh, Praveer, Gerstner, Elizabeth R., Kalpathy-Cramer, Jayashree, Boutry, Nicolas, Huard, Alexis, Vidyaratne, Lasitha, Rahman, Md Monibor, Iftekharuddin, Khan M., Chazalon, Joseph, Puybareau, Elodie, Tochon, Guillaume, Ma, Jun, Cabezas, Mariano, Llado, Xavier, Oliver, Arnau, Valencia, Liliana, Valverde, Sergi, Amian, Mehdi, Soltaninejad, Mohammadreza, Myronenko, Andriy, Hatamizadeh, Ali, Feng, Xue, Dou, Quan, Tustison, Nicholas, Meyer, Craig, Shah, Nisarg A., Talbar, Sanjay, Weber, Marc-André, Mahajan, Abhishek, Jakab, Andras, Wiest, Roland, Fathallah-Shaykh, Hassan M., Nazeri, Arash, Milchenko, Mikhail, Marcus, Daniel, Kotrotsou, Aikaterini, Colen, Rivka, Freymann, John, Kirby, Justin, Davatzikos, Christos, Menze, Bjoern, Bakas, Spyridon, Gal, Yarin, and Arbel, Tal
Abstract: Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS
Published: 2022

3. QU-BraTS : MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

Author: Mehta, Raghav, Filos, Angelos, Baid, Ujjwal, Sako, Chiharu, McKinley, Richard, Rebsamen, Michael, Dätwyler, Katrin, Meier, Raphael, Radojewski, Piotr, Murugesan, Gowtham Krishnan, Nalawade, Sahil, Ganesh, Chandan, Wagner, Ben, Yu, Fang F., Fei, Baowei, Madhuranthakam, Ananth J., Maldjian, Joseph A., Daza, Laura, Gómez, Catalina, Arbeláez, Pablo, Dai, Chengliang, Wang, Shuo, Reynaud, Hadrien, Mo, Yuanhan, Angelini, Elsa, Guo, Yike, Bai, Wenjia, Banerjee, Subhashis, Pei, Linmin, AK, Murat, Rosas-González, Sarahi, Zemmoura, Ilyess, Tauber, Clovis, Vu, Minh Hoang, Nyholm, Tufve, Löfstedt, Tommy, Ballestar, Laura Mora, Vilaplana, Veronica, McHugh, Hugh, Talou, Gonzalo Maso, Wang, Alan, Patel, Jay, Chang, Ken, Hoebel, Katharina, Gidwani, Mishka, Arun, Nishanth, Gupta, Sharut, Aggarwal, Mehak, Singh, Praveer, Gerstner, Elizabeth R., Kalpathy-Cramer, Jayashree, Boutry, Nicolas, Huard, Alexis, Vidyaratne, Lasitha, Rahman, Md Monibor, Iftekharuddin, Khan M., Chazalon, Joseph, Puybareau, Elodie, Tochon, Guillaume, Ma, Jun, Cabezas, Mariano, Llado, Xavier, Oliver, Arnau, Valencia, Liliana, Valverde, Sergi, Amian, Mehdi, Soltaninejad, Mohammadreza, Myronenko, Andriy, Hatamizadeh, Ali, Feng, Xue, Dou, Quan, Tustison, Nicholas, Meyer, Craig, Shah, Nisarg A., Talbar, Sanjay, Weber, Marc-André, Mahajan, Abhishek, Jakab, Andras, Wiest, Roland, Fathallah-Shaykh, Hassan M., Nazeri, Arash, Milchenko, Mikhail, Marcus, Daniel, Kotrotsou, Aikaterini, Colen, Rivka, Freymann, John, Kirby, Justin, Davatzikos, Christos, Menze, Bjoern, Bakas, Spyridon, Gal, Yarin, Arbel, Tal, Mehta, Raghav, Filos, Angelos, Baid, Ujjwal, Sako, Chiharu, McKinley, Richard, Rebsamen, Michael, Dätwyler, Katrin, Meier, Raphael, Radojewski, Piotr, Murugesan, Gowtham Krishnan, Nalawade, Sahil, Ganesh, Chandan, Wagner, Ben, Yu, Fang F., Fei, Baowei, Madhuranthakam, Ananth J., Maldjian, Joseph A., Daza, Laura, Gómez, Catalina, Arbeláez, Pablo, Dai, Chengliang, Wang, Shuo, Reynaud, Hadrien, Mo, Yuanhan, Angelini, Elsa, Guo, Yike, Bai, Wenjia, Banerjee, Subhashis, Pei, Linmin, AK, Murat, Rosas-González, Sarahi, Zemmoura, Ilyess, Tauber, Clovis, Vu, Minh Hoang, Nyholm, Tufve, Löfstedt, Tommy, Ballestar, Laura Mora, Vilaplana, Veronica, McHugh, Hugh, Talou, Gonzalo Maso, Wang, Alan, Patel, Jay, Chang, Ken, Hoebel, Katharina, Gidwani, Mishka, Arun, Nishanth, Gupta, Sharut, Aggarwal, Mehak, Singh, Praveer, Gerstner, Elizabeth R., Kalpathy-Cramer, Jayashree, Boutry, Nicolas, Huard, Alexis, Vidyaratne, Lasitha, Rahman, Md Monibor, Iftekharuddin, Khan M., Chazalon, Joseph, Puybareau, Elodie, Tochon, Guillaume, Ma, Jun, Cabezas, Mariano, Llado, Xavier, Oliver, Arnau, Valencia, Liliana, Valverde, Sergi, Amian, Mehdi, Soltaninejad, Mohammadreza, Myronenko, Andriy, Hatamizadeh, Ali, Feng, Xue, Dou, Quan, Tustison, Nicholas, Meyer, Craig, Shah, Nisarg A., Talbar, Sanjay, Weber, Marc-André, Mahajan, Abhishek, Jakab, Andras, Wiest, Roland, Fathallah-Shaykh, Hassan M., Nazeri, Arash, Milchenko, Mikhail, Marcus, Daniel, Kotrotsou, Aikaterini, Colen, Rivka, Freymann, John, Kirby, Justin, Davatzikos, Christos, Menze, Bjoern, Bakas, Spyridon, Gal, Yarin, and Arbel, Tal
Abstract: Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS
Published: 2022

4. QU-BraTS : MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

Author: Mehta, Raghav, Filos, Angelos, Baid, Ujjwal, Sako, Chiharu, McKinley, Richard, Rebsamen, Michael, Dätwyler, Katrin, Meier, Raphael, Radojewski, Piotr, Murugesan, Gowtham Krishnan, Nalawade, Sahil, Ganesh, Chandan, Wagner, Ben, Yu, Fang F., Fei, Baowei, Madhuranthakam, Ananth J., Maldjian, Joseph A., Daza, Laura, Gómez, Catalina, Arbeláez, Pablo, Dai, Chengliang, Wang, Shuo, Reynaud, Hadrien, Mo, Yuanhan, Angelini, Elsa, Guo, Yike, Bai, Wenjia, Banerjee, Subhashis, Pei, Linmin, AK, Murat, Rosas-González, Sarahi, Zemmoura, Ilyess, Tauber, Clovis, Vu, Minh Hoang, Nyholm, Tufve, Löfstedt, Tommy, Ballestar, Laura Mora, Vilaplana, Veronica, McHugh, Hugh, Talou, Gonzalo Maso, Wang, Alan, Patel, Jay, Chang, Ken, Hoebel, Katharina, Gidwani, Mishka, Arun, Nishanth, Gupta, Sharut, Aggarwal, Mehak, Singh, Praveer, Gerstner, Elizabeth R., Kalpathy-Cramer, Jayashree, Boutry, Nicolas, Huard, Alexis, Vidyaratne, Lasitha, Rahman, Md Monibor, Iftekharuddin, Khan M., Chazalon, Joseph, Puybareau, Elodie, Tochon, Guillaume, Ma, Jun, Cabezas, Mariano, Llado, Xavier, Oliver, Arnau, Valencia, Liliana, Valverde, Sergi, Amian, Mehdi, Soltaninejad, Mohammadreza, Myronenko, Andriy, Hatamizadeh, Ali, Feng, Xue, Dou, Quan, Tustison, Nicholas, Meyer, Craig, Shah, Nisarg A., Talbar, Sanjay, Weber, Marc-André, Mahajan, Abhishek, Jakab, Andras, Wiest, Roland, Fathallah-Shaykh, Hassan M., Nazeri, Arash, Milchenko, Mikhail, Marcus, Daniel, Kotrotsou, Aikaterini, Colen, Rivka, Freymann, John, Kirby, Justin, Davatzikos, Christos, Menze, Bjoern, Bakas, Spyridon, Gal, Yarin, Arbel, Tal, Mehta, Raghav, Filos, Angelos, Baid, Ujjwal, Sako, Chiharu, McKinley, Richard, Rebsamen, Michael, Dätwyler, Katrin, Meier, Raphael, Radojewski, Piotr, Murugesan, Gowtham Krishnan, Nalawade, Sahil, Ganesh, Chandan, Wagner, Ben, Yu, Fang F., Fei, Baowei, Madhuranthakam, Ananth J., Maldjian, Joseph A., Daza, Laura, Gómez, Catalina, Arbeláez, Pablo, Dai, Chengliang, Wang, Shuo, Reynaud, Hadrien, Mo, Yuanhan, Angelini, Elsa, Guo, Yike, Bai, Wenjia, Banerjee, Subhashis, Pei, Linmin, AK, Murat, Rosas-González, Sarahi, Zemmoura, Ilyess, Tauber, Clovis, Vu, Minh Hoang, Nyholm, Tufve, Löfstedt, Tommy, Ballestar, Laura Mora, Vilaplana, Veronica, McHugh, Hugh, Talou, Gonzalo Maso, Wang, Alan, Patel, Jay, Chang, Ken, Hoebel, Katharina, Gidwani, Mishka, Arun, Nishanth, Gupta, Sharut, Aggarwal, Mehak, Singh, Praveer, Gerstner, Elizabeth R., Kalpathy-Cramer, Jayashree, Boutry, Nicolas, Huard, Alexis, Vidyaratne, Lasitha, Rahman, Md Monibor, Iftekharuddin, Khan M., Chazalon, Joseph, Puybareau, Elodie, Tochon, Guillaume, Ma, Jun, Cabezas, Mariano, Llado, Xavier, Oliver, Arnau, Valencia, Liliana, Valverde, Sergi, Amian, Mehdi, Soltaninejad, Mohammadreza, Myronenko, Andriy, Hatamizadeh, Ali, Feng, Xue, Dou, Quan, Tustison, Nicholas, Meyer, Craig, Shah, Nisarg A., Talbar, Sanjay, Weber, Marc-André, Mahajan, Abhishek, Jakab, Andras, Wiest, Roland, Fathallah-Shaykh, Hassan M., Nazeri, Arash, Milchenko, Mikhail, Marcus, Daniel, Kotrotsou, Aikaterini, Colen, Rivka, Freymann, John, Kirby, Justin, Davatzikos, Christos, Menze, Bjoern, Bakas, Spyridon, Gal, Yarin, and Arbel, Tal
Abstract: Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS
Published: 2022

5. Posterior white matter hyperintensities are associated with reduced medial temporal lobe subregional integrity and long-term memory in older adults.

Author: Rizvi, Batool, Rizvi, Batool, Sathishkumar, Mithra, Kim, Soyun, Márquez, Freddie, Granger, Steven J, Larson, Myra S, Miranda, Blake A, Hollearn, Martina K, McMillan, Liv, Nan, Bin, Tustison, Nicholas J, Lao, Patrick J, Brickman, Adam M, Greenia, Dana, Corrada, Maria M, Kawas, Claudia H, Yassa, Michael A, Rizvi, Batool, Rizvi, Batool, Sathishkumar, Mithra, Kim, Soyun, Márquez, Freddie, Granger, Steven J, Larson, Myra S, Miranda, Blake A, Hollearn, Martina K, McMillan, Liv, Nan, Bin, Tustison, Nicholas J, Lao, Patrick J, Brickman, Adam M, Greenia, Dana, Corrada, Maria M, Kawas, Claudia H, and Yassa, Michael A
Abstract: White matter hyperintensities are a marker of small vessel cerebrovascular disease that are strongly related to cognition in older adults. Similarly, medial temporal lobe atrophy is well-documented in aging and Alzheimer's disease and is associated with memory decline. Here, we assessed the relationship between lobar white matter hyperintensities, medial temporal lobe subregional volumes, and hippocampal memory in older adults. We collected MRI scans in a sample of 139 older adults without dementia (88 females, mean age (SD) = 76.95 (10.61)). Participants were administered the Rey Auditory Verbal Learning Test (RAVLT). Regression analyses tested for associations among medial temporal lobe subregional volumes, regional white matter hyperintensities and memory, while adjusting for age, sex, and education and correcting for multiple comparisons. Increased occipital white matter hyperintensities were related to worse RAVLT delayed recall performance, and to reduced CA1, dentate gyrus, perirhinal cortex (Brodmann area 36), and parahippocampal cortex volumes. These medial temporal lobe subregional volumes were related to delayed recall performance. The association of occipital white matter hyperintensities with delayed recall performance was fully mediated statistically only by perirhinal cortex volume. These results suggest that white matter hyperintensities may be associated with memory decline through their impact on medial temporal lobe atrophy. These findings provide new insights into the role of vascular pathologies in memory loss in older adults and suggest that future studies should further examine the neural mechanisms of these relationships in longitudinal samples.
Published: 2022

6. Joint-label fusion brain atlases for dementia research in Down syndrome.

Author: Queder, Nazek, Queder, Nazek, Phelan, Michael J, Taylor, Lisa, Tustison, Nicholas, Doran, Eric, Hom, Christy, Nguyen, Dana, Lai, Florence, Pulsifer, Margaret, Price, Julie, Kreisl, William C, Rosas, Herminia D, Krinsky-McHale, Sharon, Brickman, Adam M, Yassa, Michael A, Schupf, Nicole, Silverman, Wayne, Lott, Ira T, Head, Elizabeth, Mapstone, Mark, Keator, David B, Alzheimer's Biomarkers Consortium, Queder, Nazek, Queder, Nazek, Phelan, Michael J, Taylor, Lisa, Tustison, Nicholas, Doran, Eric, Hom, Christy, Nguyen, Dana, Lai, Florence, Pulsifer, Margaret, Price, Julie, Kreisl, William C, Rosas, Herminia D, Krinsky-McHale, Sharon, Brickman, Adam M, Yassa, Michael A, Schupf, Nicole, Silverman, Wayne, Lott, Ira T, Head, Elizabeth, Mapstone, Mark, Keator, David B, and Alzheimer's Biomarkers Consortium
Abstract: Research suggests a link between Alzheimer's Disease in Down Syndrome (DS) and the overproduction of amyloid plaques. Using Positron Emission Tomography (PET) we can assess the in-vivo regional amyloid load using several available ligands. To measure amyloid distributions in specific brain regions, a brain atlas is used. A popular method of creating a brain atlas is to segment a participant's structural Magnetic Resonance Imaging (MRI) scan. Acquiring an MRI is often challenging in intellectually-imparied populations because of contraindications or data exclusion due to significant motion artifacts or incomplete sequences related to general discomfort. When an MRI cannot be acquired, it is typically replaced with a standardized brain atlas derived from neurotypical populations (i.e. healthy individuals without DS) which may be inappropriate for use in DS. In this project, we create a series of disease and diagnosis-specific (cognitively stable (CS-DS), mild cognitive impairment (MCI-DS), and dementia (DEM-DS)) probabilistic group atlases of participants with DS and evaluate their accuracy of quantifying regional amyloid load compared to the individually-based MRI segmentations. Further, we compare the diagnostic-specific atlases with a probabilistic atlas constructed from similar-aged cognitively-stable neurotypical participants. We hypothesized that regional PET signals will best match the individually-based MRI segmentations by using DS group atlases that aligns with a participant's disorder and disease status (e.g. DS and MCI-DS). Our results vary by brain region but generally show that using a disorder-specific atlas in DS better matches the individually-based MRI segmentations than using an atlas constructed from cognitively-stable neurotypical participants. We found no additional benefit of using diagnose-specific atlases matching disease status. All atlases are made publicly available for the research community.HighlightDown syndrome (DS) joint-label-fusion a
Published: 2022

7. QU-BraTS : MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

Author: Mehta, Raghav, Filos, Angelos, Baid, Ujjwal, Sako, Chiharu, McKinley, Richard, Rebsamen, Michael, Dätwyler, Katrin, Meier, Raphael, Radojewski, Piotr, Murugesan, Gowtham Krishnan, Nalawade, Sahil, Ganesh, Chandan, Wagner, Ben, Yu, Fang F., Fei, Baowei, Madhuranthakam, Ananth J., Maldjian, Joseph A., Daza, Laura, Gómez, Catalina, Arbeláez, Pablo, Dai, Chengliang, Wang, Shuo, Reynaud, Hadrien, Mo, Yuanhan, Angelini, Elsa, Guo, Yike, Bai, Wenjia, Banerjee, Subhashis, Pei, Linmin, AK, Murat, Rosas-González, Sarahi, Zemmoura, Ilyess, Tauber, Clovis, Vu, Minh Hoang, Nyholm, Tufve, Löfstedt, Tommy, Ballestar, Laura Mora, Vilaplana, Veronica, McHugh, Hugh, Talou, Gonzalo Maso, Wang, Alan, Patel, Jay, Chang, Ken, Hoebel, Katharina, Gidwani, Mishka, Arun, Nishanth, Gupta, Sharut, Aggarwal, Mehak, Singh, Praveer, Gerstner, Elizabeth R., Kalpathy-Cramer, Jayashree, Boutry, Nicolas, Huard, Alexis, Vidyaratne, Lasitha, Rahman, Md Monibor, Iftekharuddin, Khan M., Chazalon, Joseph, Puybareau, Elodie, Tochon, Guillaume, Ma, Jun, Cabezas, Mariano, Llado, Xavier, Oliver, Arnau, Valencia, Liliana, Valverde, Sergi, Amian, Mehdi, Soltaninejad, Mohammadreza, Myronenko, Andriy, Hatamizadeh, Ali, Feng, Xue, Dou, Quan, Tustison, Nicholas, Meyer, Craig, Shah, Nisarg A., Talbar, Sanjay, Weber, Marc-André, Mahajan, Abhishek, Jakab, Andras, Wiest, Roland, Fathallah-Shaykh, Hassan M., Nazeri, Arash, Milchenko, Mikhail, Marcus, Daniel, Kotrotsou, Aikaterini, Colen, Rivka, Freymann, John, Kirby, Justin, Davatzikos, Christos, Menze, Bjoern, Bakas, Spyridon, Gal, Yarin, Arbel, Tal, Mehta, Raghav, Filos, Angelos, Baid, Ujjwal, Sako, Chiharu, McKinley, Richard, Rebsamen, Michael, Dätwyler, Katrin, Meier, Raphael, Radojewski, Piotr, Murugesan, Gowtham Krishnan, Nalawade, Sahil, Ganesh, Chandan, Wagner, Ben, Yu, Fang F., Fei, Baowei, Madhuranthakam, Ananth J., Maldjian, Joseph A., Daza, Laura, Gómez, Catalina, Arbeláez, Pablo, Dai, Chengliang, Wang, Shuo, Reynaud, Hadrien, Mo, Yuanhan, Angelini, Elsa, Guo, Yike, Bai, Wenjia, Banerjee, Subhashis, Pei, Linmin, AK, Murat, Rosas-González, Sarahi, Zemmoura, Ilyess, Tauber, Clovis, Vu, Minh Hoang, Nyholm, Tufve, Löfstedt, Tommy, Ballestar, Laura Mora, Vilaplana, Veronica, McHugh, Hugh, Talou, Gonzalo Maso, Wang, Alan, Patel, Jay, Chang, Ken, Hoebel, Katharina, Gidwani, Mishka, Arun, Nishanth, Gupta, Sharut, Aggarwal, Mehak, Singh, Praveer, Gerstner, Elizabeth R., Kalpathy-Cramer, Jayashree, Boutry, Nicolas, Huard, Alexis, Vidyaratne, Lasitha, Rahman, Md Monibor, Iftekharuddin, Khan M., Chazalon, Joseph, Puybareau, Elodie, Tochon, Guillaume, Ma, Jun, Cabezas, Mariano, Llado, Xavier, Oliver, Arnau, Valencia, Liliana, Valverde, Sergi, Amian, Mehdi, Soltaninejad, Mohammadreza, Myronenko, Andriy, Hatamizadeh, Ali, Feng, Xue, Dou, Quan, Tustison, Nicholas, Meyer, Craig, Shah, Nisarg A., Talbar, Sanjay, Weber, Marc-André, Mahajan, Abhishek, Jakab, Andras, Wiest, Roland, Fathallah-Shaykh, Hassan M., Nazeri, Arash, Milchenko, Mikhail, Marcus, Daniel, Kotrotsou, Aikaterini, Colen, Rivka, Freymann, John, Kirby, Justin, Davatzikos, Christos, Menze, Bjoern, Bakas, Spyridon, Gal, Yarin, and Arbel, Tal
Abstract: Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS
Published: 2022

8. QU-BraTS : MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

Author: Mehta, Raghav, Filos, Angelos, Baid, Ujjwal, Sako, Chiharu, McKinley, Richard, Rebsamen, Michael, Dätwyler, Katrin, Meier, Raphael, Radojewski, Piotr, Murugesan, Gowtham Krishnan, Nalawade, Sahil, Ganesh, Chandan, Wagner, Ben, Yu, Fang F., Fei, Baowei, Madhuranthakam, Ananth J., Maldjian, Joseph A., Daza, Laura, Gómez, Catalina, Arbeláez, Pablo, Dai, Chengliang, Wang, Shuo, Reynaud, Hadrien, Mo, Yuanhan, Angelini, Elsa, Guo, Yike, Bai, Wenjia, Banerjee, Subhashis, Pei, Linmin, AK, Murat, Rosas-González, Sarahi, Zemmoura, Ilyess, Tauber, Clovis, Vu, Minh Hoang, Nyholm, Tufve, Löfstedt, Tommy, Ballestar, Laura Mora, Vilaplana, Veronica, McHugh, Hugh, Talou, Gonzalo Maso, Wang, Alan, Patel, Jay, Chang, Ken, Hoebel, Katharina, Gidwani, Mishka, Arun, Nishanth, Gupta, Sharut, Aggarwal, Mehak, Singh, Praveer, Gerstner, Elizabeth R., Kalpathy-Cramer, Jayashree, Boutry, Nicolas, Huard, Alexis, Vidyaratne, Lasitha, Rahman, Md Monibor, Iftekharuddin, Khan M., Chazalon, Joseph, Puybareau, Elodie, Tochon, Guillaume, Ma, Jun, Cabezas, Mariano, Llado, Xavier, Oliver, Arnau, Valencia, Liliana, Valverde, Sergi, Amian, Mehdi, Soltaninejad, Mohammadreza, Myronenko, Andriy, Hatamizadeh, Ali, Feng, Xue, Dou, Quan, Tustison, Nicholas, Meyer, Craig, Shah, Nisarg A., Talbar, Sanjay, Weber, Marc-André, Mahajan, Abhishek, Jakab, Andras, Wiest, Roland, Fathallah-Shaykh, Hassan M., Nazeri, Arash, Milchenko, Mikhail, Marcus, Daniel, Kotrotsou, Aikaterini, Colen, Rivka, Freymann, John, Kirby, Justin, Davatzikos, Christos, Menze, Bjoern, Bakas, Spyridon, Gal, Yarin, Arbel, Tal, Mehta, Raghav, Filos, Angelos, Baid, Ujjwal, Sako, Chiharu, McKinley, Richard, Rebsamen, Michael, Dätwyler, Katrin, Meier, Raphael, Radojewski, Piotr, Murugesan, Gowtham Krishnan, Nalawade, Sahil, Ganesh, Chandan, Wagner, Ben, Yu, Fang F., Fei, Baowei, Madhuranthakam, Ananth J., Maldjian, Joseph A., Daza, Laura, Gómez, Catalina, Arbeláez, Pablo, Dai, Chengliang, Wang, Shuo, Reynaud, Hadrien, Mo, Yuanhan, Angelini, Elsa, Guo, Yike, Bai, Wenjia, Banerjee, Subhashis, Pei, Linmin, AK, Murat, Rosas-González, Sarahi, Zemmoura, Ilyess, Tauber, Clovis, Vu, Minh Hoang, Nyholm, Tufve, Löfstedt, Tommy, Ballestar, Laura Mora, Vilaplana, Veronica, McHugh, Hugh, Talou, Gonzalo Maso, Wang, Alan, Patel, Jay, Chang, Ken, Hoebel, Katharina, Gidwani, Mishka, Arun, Nishanth, Gupta, Sharut, Aggarwal, Mehak, Singh, Praveer, Gerstner, Elizabeth R., Kalpathy-Cramer, Jayashree, Boutry, Nicolas, Huard, Alexis, Vidyaratne, Lasitha, Rahman, Md Monibor, Iftekharuddin, Khan M., Chazalon, Joseph, Puybareau, Elodie, Tochon, Guillaume, Ma, Jun, Cabezas, Mariano, Llado, Xavier, Oliver, Arnau, Valencia, Liliana, Valverde, Sergi, Amian, Mehdi, Soltaninejad, Mohammadreza, Myronenko, Andriy, Hatamizadeh, Ali, Feng, Xue, Dou, Quan, Tustison, Nicholas, Meyer, Craig, Shah, Nisarg A., Talbar, Sanjay, Weber, Marc-André, Mahajan, Abhishek, Jakab, Andras, Wiest, Roland, Fathallah-Shaykh, Hassan M., Nazeri, Arash, Milchenko, Mikhail, Marcus, Daniel, Kotrotsou, Aikaterini, Colen, Rivka, Freymann, John, Kirby, Justin, Davatzikos, Christos, Menze, Bjoern, Bakas, Spyridon, Gal, Yarin, and Arbel, Tal
Abstract: Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS
Published: 2022

9. Longitudinal Mapping of Cortical Thickness Measurements: An Alzheimer's Disease Neuroimaging Initiative-Based Evaluation Study.

Author: Tustison, Nicholas J, Seong, Joon-Kyung1, Tustison, Nicholas J, Holbrook, Andrew J, Avants, Brian B, Roberts, Jared M, Cook, Philip A, Reagh, Zachariah M, Duda, Jeffrey T, Stone, James R, Gillen, Daniel L, Yassa, Michael A, Alzheimer’s Disease Neuroimaging Initiative, Tustison, Nicholas J, Seong, Joon-Kyung1, Tustison, Nicholas J, Holbrook, Andrew J, Avants, Brian B, Roberts, Jared M, Cook, Philip A, Reagh, Zachariah M, Duda, Jeffrey T, Stone, James R, Gillen, Daniel L, Yassa, Michael A, and Alzheimer’s Disease Neuroimaging Initiative
Abstract: Longitudinal studies of development and disease in the human brain have motivated the acquisition of large neuroimaging data sets and the concomitant development of robust methodological and statistical tools for quantifying neurostructural changes. Longitudinal-specific strategies for acquisition and processing have potentially significant benefits including more consistent estimates of intra-subject measurements while retaining predictive power. Using the first phase of the Alzheimer's Disease Neuroimaging Initiative (ADNI-1) data, comprising over 600 subjects with multiple time points from baseline to 36 months, we evaluate the utility of longitudinal FreeSurfer and Advanced Normalization Tools (ANTs) surrogate thickness values in the context of a linear mixed-effects (LME) modeling strategy. Specifically, we estimate the residual variability and between-subject variability associated with each processing stream as it is known from the statistical literature that minimizing the former while simultaneously maximizing the latter leads to greater scientific interpretability in terms of tighter confidence intervals in calculated mean trends, smaller prediction intervals, and narrower confidence intervals for determining cross-sectional effects. This strategy is evaluated over the entire cortex, as defined by the Desikan-Killiany-Tourville labeling protocol, where comparisons are made with the cross-sectional and longitudinal FreeSurfer processing streams. Subsequent linear mixed effects modeling for identifying diagnostic groupings within the ADNI cohort is provided as supporting evidence for the utility of the proposed ANTs longitudinal framework which provides unbiased structural neuroimage processing and competitive to superior power for longitudinal structural change detection.
Published: 2019

10. QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

Author: Mehta, Raghav, Filos, Angelos, Baid, Ujjwal, Sako, Chiharu, McKinley, Richard, Rebsamen, Michael, Datwyler, Katrin, Meier, Raphael, Radojewski, Piotr, Murugesan, Gowtham Krishnan, Nalawade, Sahil, Ganesh, Chandan, Wagner, Ben, Yu, Fang F., Fei, Baowei, Madhuranthakam, Ananth J., Maldjian, Joseph A., Daza, Laura, Gomez, Catalina, Arbelaez, Pablo, Dai, Chengliang, Wang, Shuo, Reynaud, Hadrien, Mo, Yuan-han, Angelini, Elsa, Guo, Yike, Bai, Wenjia, Banerjee, Subhashis, Pei, Lin-min, AK, Murat, Rosas-Gonzalez, Sarahi, Zemmoura, Ilyess, Tauber, Clovis, Vu, Minh H., Nyholm, Tufve, Lofstedt, Tommy, Ballestar, Laura Mora, Vilaplana, Veronica, McHugh, Hugh, Talou, Gonzalo Maso, Wang, Alan, Patel, Jay, Chang, Ken, Hoebel, Katharina, Gidwani, Mishka, Arun, Nishanth, Gupta, Sharut, Aggarwal, Mehak, Singh, Praveer, Gerstner, Elizabeth R., Kalpathy-Cramer, Jayashree, Boutry, Nicolas, Huard, Alexis, Vidyaratne, Lasitha, Rahman, Md Monibor, Iftekharuddin, Khan M., Chazalon, Joseph, Puybareau, Elodie, Tochon, Guillaume, Ma, Jun, Cabezas, Mariano, Llado, Xavier, Oliver, Arnau, Valencia, Liliana, Valverde, Sergi, Amian, Mehdi, Soltaninejad, Mohammadreza, Myronenko, Andriy, Hatamizadeh, Ali, Feng, Xue, Dou, Quan, Tustison, Nicholas, Meyer, Craig, Shah, Nisarg A., Talbar, Sanjay, Weber, Marc-Andre, Mahajan, Abhishek, Jakab, Andras, Wiest, Roland, Fathallah-Shaykh, Hassan M., Nazeri, Arash, Milchenko1, Mikhail, Marcus, Daniel, Kotrotsou, Aikaterini, Colen, Rivka, Freymann, John, Kirby, Justin, Davatzikos, Christos, Menze, Bjoern, Bakas, Spyridon, Gal, Yarin, Arbel, Tal, Mehta, Raghav, Filos, Angelos, Baid, Ujjwal, Sako, Chiharu, McKinley, Richard, Rebsamen, Michael, Datwyler, Katrin, Meier, Raphael, Radojewski, Piotr, Murugesan, Gowtham Krishnan, Nalawade, Sahil, Ganesh, Chandan, Wagner, Ben, Yu, Fang F., Fei, Baowei, Madhuranthakam, Ananth J., Maldjian, Joseph A., Daza, Laura, Gomez, Catalina, Arbelaez, Pablo, Dai, Chengliang, Wang, Shuo, Reynaud, Hadrien, Mo, Yuan-han, Angelini, Elsa, Guo, Yike, Bai, Wenjia, Banerjee, Subhashis, Pei, Lin-min, AK, Murat, Rosas-Gonzalez, Sarahi, Zemmoura, Ilyess, Tauber, Clovis, Vu, Minh H., Nyholm, Tufve, Lofstedt, Tommy, Ballestar, Laura Mora, Vilaplana, Veronica, McHugh, Hugh, Talou, Gonzalo Maso, Wang, Alan, Patel, Jay, Chang, Ken, Hoebel, Katharina, Gidwani, Mishka, Arun, Nishanth, Gupta, Sharut, Aggarwal, Mehak, Singh, Praveer, Gerstner, Elizabeth R., Kalpathy-Cramer, Jayashree, Boutry, Nicolas, Huard, Alexis, Vidyaratne, Lasitha, Rahman, Md Monibor, Iftekharuddin, Khan M., Chazalon, Joseph, Puybareau, Elodie, Tochon, Guillaume, Ma, Jun, Cabezas, Mariano, Llado, Xavier, Oliver, Arnau, Valencia, Liliana, Valverde, Sergi, Amian, Mehdi, Soltaninejad, Mohammadreza, Myronenko, Andriy, Hatamizadeh, Ali, Feng, Xue, Dou, Quan, Tustison, Nicholas, Meyer, Craig, Shah, Nisarg A., Talbar, Sanjay, Weber, Marc-Andre, Mahajan, Abhishek, Jakab, Andras, Wiest, Roland, Fathallah-Shaykh, Hassan M., Nazeri, Arash, Milchenko1, Mikhail, Marcus, Daniel, Kotrotsou, Aikaterini, Colen, Rivka, Freymann, John, Kirby, Justin, Davatzikos, Christos, Menze, Bjoern, Bakas, Spyridon, Gal, Yarin, and Arbel, Tal
Abstract: Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at: https://github.com/RagMeh11/QU-BraTS., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA): https://www.melba-journal.org/papers/2022:026.html
Published: 2021
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11. The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

Author: Baheti, Bhakti, Chakrabarty, Satrajit, Akbari, Hamed, Bilello, Michel, Wiestler, Benedikt, Schwarting, Julian, Calabrese, Evan, Rudie, Jeffrey, Abidi, Syed, Mousa, Mina, Villanueva-Meyer, Javier, Fields, Brandon K. K., Kofler, Florian, Shinohara, Russell Takeshi, Iglesias, Juan Eugenio, Mok, Tony C. W., Chung, Albert C. S., Wodzinski, Marek, Jurgas, Artur, Marini, Niccolo, Atzori, Manfredo, Muller, Henning, Grobroehmer, Christoph, Siebert, Hanna, Hansen, Lasse, Heinrich, Mattias P., Canalini, Luca, Klein, Jan, Gerken, Annika, Heldmann, Stefan, Hering, Alessa, Hahn, Horst K., Meng, Mingyuan, Bi, Lei, Feng, Dagan, Kim, Jinman, Zeineldin, Ramy A., Karar, Mohamed E., Mathis-Ullrich, Franziska, Burgert, Oliver, Abderezaei, Javid, Pionteck, Aymeric, Chopra, Agamdeep, Kurt, Mehmet, Yan, Kewei, Yan, Yonghong, Tang, Zhe, Ma, Jianqiang, Nasser, Sahar Almahfouz, Kurian, Nikhil Cherian, Meena, Mohit, Shamsi, Saqib, Sethi, Amit, Tustison, Nicholas J., Avants, Brian B., Cook, Philip, Gee, James C., Tian, Lin, Greer, Hastings, Niethammer, Marc, Hoopes, Andrew, Hoffmann, Malte, Dalca, Adrian V., Christodoulidis, Stergios, Estiene, Theo, Vakalopoulou, Maria, Paragios, Nikos, Marcus, Daniel S., Davatzikos, Christos, Sotiras, Aristeidis, Menze, Bjoern, Bakas, Spyridon, Waldmannstetter, Diana, Baheti, Bhakti, Chakrabarty, Satrajit, Akbari, Hamed, Bilello, Michel, Wiestler, Benedikt, Schwarting, Julian, Calabrese, Evan, Rudie, Jeffrey, Abidi, Syed, Mousa, Mina, Villanueva-Meyer, Javier, Fields, Brandon K. K., Kofler, Florian, Shinohara, Russell Takeshi, Iglesias, Juan Eugenio, Mok, Tony C. W., Chung, Albert C. S., Wodzinski, Marek, Jurgas, Artur, Marini, Niccolo, Atzori, Manfredo, Muller, Henning, Grobroehmer, Christoph, Siebert, Hanna, Hansen, Lasse, Heinrich, Mattias P., Canalini, Luca, Klein, Jan, Gerken, Annika, Heldmann, Stefan, Hering, Alessa, Hahn, Horst K., Meng, Mingyuan, Bi, Lei, Feng, Dagan, Kim, Jinman, Zeineldin, Ramy A., Karar, Mohamed E., Mathis-Ullrich, Franziska, Burgert, Oliver, Abderezaei, Javid, Pionteck, Aymeric, Chopra, Agamdeep, Kurt, Mehmet, Yan, Kewei, Yan, Yonghong, Tang, Zhe, Ma, Jianqiang, Nasser, Sahar Almahfouz, Kurian, Nikhil Cherian, Meena, Mohit, Shamsi, Saqib, Sethi, Amit, Tustison, Nicholas J., Avants, Brian B., Cook, Philip, Gee, James C., Tian, Lin, Greer, Hastings, Niethammer, Marc, Hoopes, Andrew, Hoffmann, Malte, Dalca, Adrian V., Christodoulidis, Stergios, Estiene, Theo, Vakalopoulou, Maria, Paragios, Nikos, Marcus, Daniel S., Davatzikos, Christos, Sotiras, Aristeidis, Menze, Bjoern, Bakas, Spyridon, and Waldmannstetter, Diana
Abstract: Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. Quantitative evaluation and ranking were based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency analysis, and test-time instance optimization per-case basis as a post-processing step. The top-ranked method attained the MEE at or below that of the inter-rater variability for approximately 60% of the evaluated landmarks, underscoring the scope for further accuracy and robustness improvements, especially relative to human experts. The aim of BraTS-Reg is to continue to serve as an active resource for research, with the data and online evaluation tools accessible at https://bratsreg.github.io/.
Published: 2021

12. Characterizing Gas Exchange Physiology in Healthy Young Electronic-Cigarette Users with Hyperpolarized 129Xe MRI: A Pilot Study

Author: He,Mu, Qing,Kun, Tustison,Nicholas J, Beaulac,Zach, King,Tabitha W, Huff,Thomas B, Paige,Mikell, Earasi,Kranthikiran, Nunoo-Asare,Roselove, Struchen,Sarah, Burdick,Marie, Zhang,Zhimin, Ropp,Alan, Miller,Grady W, Patrie,James T, Mata,Jaime F, Mugler 3rd,John P, Shim,Yun Micheal, He,Mu, Qing,Kun, Tustison,Nicholas J, Beaulac,Zach, King,Tabitha W, Huff,Thomas B, Paige,Mikell, Earasi,Kranthikiran, Nunoo-Asare,Roselove, Struchen,Sarah, Burdick,Marie, Zhang,Zhimin, Ropp,Alan, Miller,Grady W, Patrie,James T, Mata,Jaime F, Mugler 3rd,John P, and Shim,Yun Micheal
Abstract: Mu He,1 Kun Qing,2,3 Nicholas J Tustison,2 Zach Beaulac,4 Tabitha W King,5 Thomas B Huff,5 Mikell Paige,4 Kranthikiran Earasi,1 Roselove Nunoo-Asare,1 Sarah Struchen,2 Marie Burdick,1 Zhimin Zhang,1 Alan Ropp,2 Grady W Miller,2 James T Patrie,6 Jaime F Mata,2 John P Mugler 3rd,2 Yun Michael Shim1,2 1Department of Medicine, Division of Pulmonary and Critical Care, University of Virginia, Charlottesville, VA, USA; 2Department of Radiology & Medical Imaging, University of Virginia, Charlottesville, VA, USA; 3Radiation Oncology, Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA; 4Department of Chemistry, George Mason University, Fairfax, VA, USA; 5Shared Research Instrumentation Facility, George Mason University, Manassas, VA, USA; 6School of Public Health, University of Virginia, Charlottesville, VA, USACorrespondence: Yun Michael ShimDepartment of Medicine, Division of Pulmonary and Critical Care, University of Virginia, PO Box 800546, Charlottesville, VA, 22908-0546, USAEmail yss6n@virginia.edu
Published: 2021

13. Interpretable, similarity-driven multi-view embeddings from high-dimensional biomedical data

Author: Avants, Brian B., Tustison, Nicholas J., Stone, James R., Avants, Brian B., Tustison, Nicholas J., and Stone, James R.
Abstract: Similarity-driven multi-view linear reconstruction (SiMLR) is an algorithm that exploits inter-modality relationships to transform large scientific datasets into smaller, more well-powered and interpretable low-dimensional spaces. SiMLR contributes a novel objective function for identifying joint signal, regularization based on sparse matrices representing prior within-modality relationships and an implementation that permits application to joint reduction of large data matrices, each of which may have millions of entries. We demonstrate that SiMLR outperforms closely related methods on supervised learning problems in simulation data, a multi-omics cancer survival prediction dataset and multiple modality neuroimaging datasets. Taken together, this collection of results shows that SiMLR may be applied with default parameters to joint signal estimation from disparate modalities and may yield practically useful results in a variety of application domains., Comment: accepted to Nature Computational Science
Published: 2020

14. Anterolateral entorhinal cortex thickness as a new biomarker for early detection of Alzheimer's disease

Author: Holbrook, Andrew J, Holbrook, Andrew J, Tustison, Nicholas J, Marquez, Freddie, Roberts, Jared, Yassa, Michael A, Gillen, Daniel L, Initiative§, for the Alzheimer's Disease Neuroimaging, Holbrook, Andrew J, Holbrook, Andrew J, Tustison, Nicholas J, Marquez, Freddie, Roberts, Jared, Yassa, Michael A, Gillen, Daniel L, and Initiative§, for the Alzheimer's Disease Neuroimaging
Abstract: IntroductionLoss of entorhinal cortex (EC) layer II neurons represents the earliest Alzheimer's disease (AD) lesion in the brain. Research suggests differing functional roles between two EC subregions, the anterolateral EC (aLEC) and the posteromedial EC (pMEC).MethodsWe use joint label fusion to obtain aLEC and pMEC cortical thickness measurements from serial magnetic resonance imaging scans of 775 ADNI-1 participants (219 healthy; 380 mild cognitive impairment; 176 AD) and use linear mixed-effects models to analyze longitudinal associations among cortical thickness, disease status, and cognitive measures.ResultsGroup status is reliably predicted by aLEC thickness, which also exhibits greater associations with cognitive outcomes than does pMEC thickness. Change in aLEC thickness is also associated with cerebrospinal fluid amyloid and tau levels.DiscussionThinning of aLEC is a sensitive structural biomarker that changes over short durations in the course of AD and tracks disease severity-it is a strong candidate biomarker for detection of early AD.
Published: 2020

15. Multi-site meta-analysis of morphometry

Author: Jahanshad, Neda, Faskowitz, Joshua, Roshchupkin, Gennady, Hibar, Derrek P., Gutman, Boris A., Tustison, Nicholas J., Adams, Hieab H.H., Niessen, Wiro J., Vernooij, Meike W., Ikram, M. Arfan, Zwiers, Marcel P., Vasquez, Alejandro Arias, Franke, Barbara, Kroll, Jennifer L., Mwangi, Benson, Soares, Jair C., Ing, Alex, Desrivieres, Sylvane, Schumann, Gunter, Hansell, Narelle K., De Zubicaray, Greig I., McMahon, Katie L., Martin, Nicholas G., Wright, Margaret J., Thompson, Paul M., Jahanshad, Neda, Faskowitz, Joshua, Roshchupkin, Gennady, Hibar, Derrek P., Gutman, Boris A., Tustison, Nicholas J., Adams, Hieab H.H., Niessen, Wiro J., Vernooij, Meike W., Ikram, M. Arfan, Zwiers, Marcel P., Vasquez, Alejandro Arias, Franke, Barbara, Kroll, Jennifer L., Mwangi, Benson, Soares, Jair C., Ing, Alex, Desrivieres, Sylvane, Schumann, Gunter, Hansell, Narelle K., De Zubicaray, Greig I., McMahon, Katie L., Martin, Nicholas G., Wright, Margaret J., and Thompson, Paul M.
Abstract: Genome-wide association studies GWAS link full genome data to a handful of traits. However, in neuroimaging studies, there is an almost unlimited number of traits that can be extracted for full image-wide big data analyses. Large populations are needed to achieve the necessary power to detect statistically significant effects, emphasizing the need to pool data across multiple studies. Neuroimaging consortia, e.g., ENIGMA and CHARGE, are now analyzing MRI data from over 30,000 individuals. Distributed processing protocols extract harmonized features at each site, and pool together only the cohort statistics using meta analysis to avoid data sharing. To date, such MRI projects have focused on single measures such as hippocampal volume, yet voxelwise analyses e.g., tensor-based morphometry; TBM may help better localize statistical effects. This can lead to 1013 tests for GWAS and become underpowered. We developed an analytical framework for multi-site TBM by performing multi-channel registration to cohort-specific templates. Our results highlight the reliability of the method and the added power over alternative options while preserving single site specificity and opening the doors for well-powered image-wide genome-wide discoveries.
Published: 2019

16. A randomized, double-blind, placebo-controlled trial of lamotrigine for prescription corticosteroid effects on the human hippocampus.

Author: Brown, E Sherwood, Brown, E Sherwood, Sayed, Nasreen, Choi, Changho, Tustison, Nicholas, Roberts, Jared, Yassa, Michael A, Van Enkevort, Erin, Nakamura, Alyson, Ivleva, Elena I, Sunderajan, Prabha, Khan, David A, Vazquez, Miguel, McEwen, Bruce, Kulikova, Alexandra, Frol, Alan B, Holmes, Traci, Brown, E Sherwood, Brown, E Sherwood, Sayed, Nasreen, Choi, Changho, Tustison, Nicholas, Roberts, Jared, Yassa, Michael A, Van Enkevort, Erin, Nakamura, Alyson, Ivleva, Elena I, Sunderajan, Prabha, Khan, David A, Vazquez, Miguel, McEwen, Bruce, Kulikova, Alexandra, Frol, Alan B, and Holmes, Traci
Abstract: In animals, stress and corticosteroid excess are associated with decreases in memory performance and hippocampal volume that may be prevented with agents that decrease glutamate release. Humans also demonstrate changes in memory and hippocampus with corticosteroids. In this report the effects of glutamate-release inhibitor lamotrigine on hippocampal structure and memory were examined in people receiving medically needed prescription corticosteroid therapy. A total of 54 outpatient adults (n = 28 women) receiving chronic (≥ 6 months) oral corticosteroid therapy were randomized to lamotrigine or placebo for 48 weeks. Declarative memory was assessed using the Rey Auditory Verbal Learning Test (RAVLT); structural magnetic resonance imaging (MRI) as well as single-voxel proton MR spectroscopy (1HMRS) focused on hippocampus were obtained at baseline and week 48. Utilizing a mixed-model approach, structural and biochemical data were examined by separate ANOVAs, and memory was assessed with a multi-level longitudinal model. RAVLT total scores demonstrated significantly better declarative memory performance with lamotrigine than placebo (p = 0.047). Hippocampal subfield volumes were not significantly different between the treatment groups. In summary, lamotrigine was associated with less decline in declarative memory performance than placebo in corticosteroid-treated patients. Findings suggest that, in humans as well as in animal models, glutamate release inhibitors may attenuate some of the effects on the human memory associated with corticosteroids.
Published: 2019

17. The Relationship Between Cumulative Exogenous Corticosteroid Exposure and Volumes of Hippocampal Subfields and Surrounding Structures.

Author: Nguyen, Duc M, Nguyen, Duc M, Yassa, Michael A, Tustison, Nicholas J, Roberts, Jared M, Kulikova, Alexandra, Nakamura, Alyson, Ivleva, Elena I, Van Enkevort, Erin, Brown, E Sherwood, Nguyen, Duc M, Nguyen, Duc M, Yassa, Michael A, Tustison, Nicholas J, Roberts, Jared M, Kulikova, Alexandra, Nakamura, Alyson, Ivleva, Elena I, Van Enkevort, Erin, and Brown, E Sherwood
Abstract: Purpose/backgroundGlucocorticoids are a class of hormones that include naturally occurring cortisol and corticosterone, as well as prescription drugs commonly used to manage inflammatory, autoimmune, and allergic conditions. Adverse effects, including neuropsychiatric symptoms, are common. The hippocampus appears to be especially sensitive to the effects of glucocorticoids. However, to our knowledge, no studies to date have examined hippocampal subfields in humans receiving glucocorticoids. We examined patients on chronic glucocorticoid regimens to determine relationships between dose and duration of treatment, and hippocampal subfields, and related regions volumes.Methods/proceduresThe study included adult men and women receiving at least 5 mg daily of prednisone equivalents for at least 6 months. Volumes of brain regions were measured via magnetic resonance imaging. A multivariate general linear model was used for analysis, with brain volumes as dependent variables and age, sex, and cumulative corticosteroid exposure, as predictors.Findings/resultsThe study population consisted of 81 adult outpatients (43 male) on corticosteroids (mean dose, 7.88 mg; mean duration, 76.75 months). Cumulative glucocorticoid exposure was negatively associated with left and right hippocampal dentate gyrus/CA3 volume. In subsequent subgroup analysis, this association held true for the age group older than the median age of 46 years but not for the younger age group.Implications/conclusionsThis finding is consistent with previous studies showing detrimental effects of elevated glucocorticoids on the hippocampus but further suggests that the dentate gyrus and CA3 regions are particularly vulnerable to those effects, which is consistent with animal models of chronic stress but has not been previously demonstrated in humans.
Published: 2019

18. A randomized trial of an NMDA receptor antagonist for reversing corticosteroid effects on the human hippocampus.

Author: Brown, E Sherwood, Brown, E Sherwood, Kulikova, Alexandra, Van Enkevort, Erin, Nakamura, Alyson, Ivleva, Elena I, Tustison, Nicholas J, Roberts, Jared, Yassa, Michael A, Choi, Changho, Frol, Alan, Khan, David A, Vazquez, Miguel, Holmes, Traci, Malone, Kendra, Brown, E Sherwood, Brown, E Sherwood, Kulikova, Alexandra, Van Enkevort, Erin, Nakamura, Alyson, Ivleva, Elena I, Tustison, Nicholas J, Roberts, Jared, Yassa, Michael A, Choi, Changho, Frol, Alan, Khan, David A, Vazquez, Miguel, Holmes, Traci, and Malone, Kendra
Abstract: Preclinical and clinical research indicates that excess corticosteroid is associated with adverse effects on the hippocampus. Animal model data suggest that N-methyl-D-aspartate (NMDA) receptor antagonists may block corticosteroid effect on the hippocampus. This translational clinical trial investigated the effect of memantine vs. placebo on hippocampal subfield volume in humans receiving chronic corticosteroid therapy. Men and women (N = 46) receiving chronic prescription corticosteroid therapy were randomized to memantine or placebo in a double-blind, crossover design (two 24-week treatment periods, separated by a 4-week washout) for 52 weeks. Structural magnetic resonance imaging was obtained at baseline and after each treatment. Data were analyzed using repeated measures analysis of variance. Mean corticosteroid dose was 7.69 ± 6.41 mg/day and mean duration 4.90 ± 5.61 years. Controlling for baseline volumes, the left DG/CA3 region was significantly larger following memantine than placebo (p = .011). The findings suggest that an NMDA receptor antagonist attenuates corticosteroid effect in the same hippocampal subfields in humans as in animal models. This finding has both mechanistic and clinical implications. Attenuation of the effect of corticosteroids on the human DG/CA3 region implicates the NMDA receptor in human hippocampal volume losses with corticosteroids. In addition, by suggesting a drug class that may, at least in part, block the effects of corticosteroids on the human DG/CA3 subfield, these results may have clinical relevance for people receiving prescription corticosteroids, as well as to those with cortisol elevations due to medical or psychiatric conditions.
Published: 2019

19. Functional Imbalance of Anterolateral Entorhinal Cortex and Hippocampal Dentate/CA3 Underlies Age-Related Object Pattern Separation Deficits.

Author: Reagh, Zachariah M, Reagh, Zachariah M, Noche, Jessica A, Tustison, Nicholas J, Delisle, Derek, Murray, Elizabeth A, Yassa, Michael A, Reagh, Zachariah M, Reagh, Zachariah M, Noche, Jessica A, Tustison, Nicholas J, Delisle, Derek, Murray, Elizabeth A, and Yassa, Michael A
Abstract: The entorhinal cortex (EC) is among the earliest brain areas to deteriorate in Alzheimer's disease (AD). However, the extent to which functional properties of the EC are altered in the aging brain, even in the absence of clinical symptoms, is not understood. Recent human fMRI studies have identified a functional dissociation within the EC, similar to what is found in rodents. Here, we used high-resolution fMRI to identify a specific hypoactivity in the anterolateral EC (alEC) commensurate with major behavioral deficits on an object pattern separation task in asymptomatic older adults. Only subtle deficits were found in a comparable spatial condition, with no associated differences in posteromedial EC between young and older adults. We additionally linked this condition to dentate/CA3 hyperactivity, and the ratio of activity between the regions was associated with object mnemonic discrimination impairment. These results provide novel evidence of alEC-dentate/CA3 circuit dysfunction in cognitively normal aged humans.
Published: 2018

20. Functional Imbalance of Anterolateral Entorhinal Cortex and Hippocampal Dentate/CA3 Underlies Age-Related Object Pattern Separation Deficits.

Author: Reagh, Zachariah M, Reagh, Zachariah M, Noche, Jessica A, Tustison, Nicholas J, Delisle, Derek, Murray, Elizabeth A, Yassa, Michael A, Reagh, Zachariah M, Reagh, Zachariah M, Noche, Jessica A, Tustison, Nicholas J, Delisle, Derek, Murray, Elizabeth A, and Yassa, Michael A
Abstract: The entorhinal cortex (EC) is among the earliest brain areas to deteriorate in Alzheimer's disease (AD). However, the extent to which functional properties of the EC are altered in the aging brain, even in the absence of clinical symptoms, is not understood. Recent human fMRI studies have identified a functional dissociation within the EC, similar to what is found in rodents. Here, we used high-resolution fMRI to identify a specific hypoactivity in the anterolateral EC (alEC) commensurate with major behavioral deficits on an object pattern separation task in asymptomatic older adults. Only subtle deficits were found in a comparable spatial condition, with no associated differences in posteromedial EC between young and older adults. We additionally linked this condition to dentate/CA3 hyperactivity, and the ratio of activity between the regions was associated with object mnemonic discrimination impairment. These results provide novel evidence of alEC-dentate/CA3 circuit dysfunction in cognitively normal aged humans.
Published: 2018

21. Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features

Author: Feng, Xue, Tustison, Nicholas, Meyer, Craig, Feng, Xue, Tustison, Nicholas, and Meyer, Craig
Abstract: Accurate segmentation of different sub-regions of gliomas including peritumoral edema, necrotic core, enhancing and non-enhancing tumor core from multimodal MRI scans has important clinical relevance in diagnosis, prognosis and treatment of brain tumors. However, due to the highly heterogeneous appearance and shape, segmentation of the sub-regions is very challenging. Recent development using deep learning models has proved its effectiveness in the past several brain segmentation challenges as well as other semantic and medical image segmentation problems. Most models in brain tumor segmentation use a 2D/3D patch to predict the class label for the center voxel and variant patch sizes and scales are used to improve the model performance. However, it has low computation efficiency and also has limited receptive field. U-Net is a widely used network structure for end-to-end segmentation and can be used on the entire image or extracted patches to provide classification labels over the entire input voxels so that it is more efficient and expect to yield better performance with larger input size. Furthermore, instead of picking the best network structure, an ensemble of multiple models, trained on different dataset or different hyper-parameters, can generally improve the segmentation performance. In this study we propose to use an ensemble of 3D U-Nets with different hyper-parameters for brain tumor segmentation. Preliminary results showed effectiveness of this model. In addition, we developed a linear model for survival prediction using extracted imaging and non-imaging features, which, despite the simplicity, can effectively reduce overfitting and regression errors., Comment: arXiv admin note: text overlap with arXiv:1810.04274 by other authors
Published: 2018

22. Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Author: Bakas, Spyridon, Reyes, Mauricio, Jakab, Andras, Bauer, Stefan, Rempfler, Markus, Crimi, Alessandro, Shinohara, Russell Takeshi, Berger, Christoph, Ha, Sung Min, Rozycki, Martin, Prastawa, Marcel, Alberts, Esther, Lipkova, Jana, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan, Wiest, Roland, Kirschke, Jan, Wiestler, Benedikt, Colen, Rivka, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-Andre, Mahajan, Abhishek, Baid, Ujjwal, Gerstner, Elizabeth, Kwon, Dongjin, Acharya, Gagan, Agarwal, Manu, Alam, Mahbubul, Albiol, Alberto, Albiol, Antonio, Albiol, Francisco J., Alex, Varghese, Allinson, Nigel, Amorim, Pedro H. A., Amrutkar, Abhijit, Anand, Ganesh, Andermatt, Simon, Arbel, Tal, Arbelaez, Pablo, Avery, Aaron, Azmat, Muneeza, B., Pranjal, Bai, W, Banerjee, Subhashis, Barth, Bill, Batchelder, Thomas, Batmanghelich, Kayhan, Battistella, Enzo, Beers, Andrew, Belyaev, Mikhail, Bendszus, Martin, Benson, Eze, Bernal, Jose, Bharath, Halandur Nagaraja, Biros, George, Bisdas, Sotirios, Brown, James, Cabezas, Mariano, Cao, Shilei, Cardoso, Jorge M., Carver, Eric N, Casamitjana, Adrià, Castillo, Laura Silvana, Catà, Marcel, Cattin, Philippe, Cerigues, Albert, Chagas, Vinicius S., Chandra, Siddhartha, Chang, Yi-Ju, Chang, Shiyu, Chang, Ken, Chazalon, Joseph, Chen, Shengcong, Chen, Wei, Chen, Jefferson W, Chen, Zhaolin, Cheng, Kun, Choudhury, Ahana Roy, Chylla, Roger, Clérigues, Albert, Colleman, Steven, Colmeiro, Ramiro German Rodriguez, Combalia, Marc, Costa, Anthony, Cui, Xiaomeng, Dai, Zhenzhen, Dai, Lutao, Daza, Laura Alexandra, Deutsch, Eric, Ding, Changxing, Dong, Chao, Dong, Shidu, Dudzik, Wojciech, Eaton-Rosen, Zach, Egan, Gary, Escudero, Guilherme, Estienne, Théo, Everson, Richard, Fabrizio, Jonathan, Fan, Yong, Fang, Longwei, Feng, Xue, Ferrante, Enzo, Fidon, Lucas, Fischer, Martin, French, Andrew P., Fridman, Naomi, Fu, Huan, Fuentes, David, Gao, Yaozong, Gates, Evan, Gering, David, Gholami, Amir, Gierke, Willi, Glocker, Ben, Gong, Mingming, González-Villá, Sandra, Grosges, T., Guan, Yuanfang, Guo, Sheng, Gupta, Sudeep, Han, Woo-Sup, Han, Il Song, Harmuth, Konstantin, He, Huiguang, Hernández-Sabaté, Aura, Herrmann, Evelyn, Himthani, Naveen, Hsu, Winston, Hsu, Cheyu, Hu, Xiaojun, Hu, Xiaobin, Hu, Yan, Hu, Yifan, Hua, Rui, Huang, Teng-Yi, Huang, Weilin, Van Huffel, Sabine, Huo, Quan, HV, Vivek, Iftekharuddin, Khan M., Isensee, Fabian, Islam, Mobarakol, Jackson, Aaron S., Jambawalikar, Sachin R., Jesson, Andrew, Jian, Weijian, Jin, Peter, Jose, V Jeya Maria, Jungo, Alain, Kainz, B, Kamnitsas, Konstantinos, Kao, Po-Yu, Karnawat, Ayush, Kellermeier, Thomas, Kermi, Adel, Keutzer, Kurt, Khadir, Mohamed Tarek, Khened, Mahendra, Kickingereder, Philipp, Kim, Geena, King, Nik, Knapp, Haley, Knecht, Urspeter, Kohli, Lisa, Kong, Deren, Kong, Xiangmao, Koppers, Simon, Kori, Avinash, Krishnamurthi, Ganapathy, Krivov, Egor, Kumar, Piyush, Kushibar, Kaisar, Lachinov, Dmitrii, Lambrou, Tryphon, Lee, Joon, Lee, Chengen, Lee, Yuehchou, Lee, M, Lefkovits, Szidonia, Lefkovits, Laszlo, Levitt, James, Li, Tengfei, Li, Hongwei, Li, Wenqi, Li, Hongyang, Li, Xiaochuan, Li, Yuexiang, Li, Heng, Li, Zhenye, Li, Xiaoyu, Li, Zeju, Li, XiaoGang, Lin, Zheng-Shen, Lin, Fengming, Lio, Pietro, Liu, Chang, Liu, Boqiang, Liu, Xiang, Liu, Mingyuan, Liu, Ju, Liu, Luyan, Llado, Xavier, Lopez, Marc Moreno, Lorenzo, Pablo Ribalta, Lu, Zhentai, Luo, Lin, Luo, Zhigang, Ma, Jun, Ma, Kai, Mackie, Thomas, Madabushi, Anant, Mahmoudi, Issam, Maier-Hein, Klaus H., Maji, Pradipta, Mammen, CP, Mang, Andreas, Manjunath, B. S., Marcinkiewicz, Michal, McDonagh, S, McKenna, Stephen, McKinley, Richard, Mehl, Miriam, Mehta, Sachin, Mehta, Raghav, Meier, Raphael, Meinel, Christoph, Merhof, Dorit, Meyer, Craig, Miller, Robert, Mitra, Sushmita, Moiyadi, Aliasgar, Molina-Garcia, David, Monteiro, Miguel A. B., Mrukwa, Grzegorz, Myronenko, Andriy, Nalepa, Jakub, Ngo, Thuyen, Nie, Dong, Ning, Holly, Niu, Chen, Nuechterlein, Nicholas K, Oermann, Eric, Oliveira, Arlindo, Oliveira, Diego D. C., Oliver, Arnau, Osman, Alexander F. I., Ou, Yu-Nian, Ourselin, Sebastien, Paragios, Nikos, Park, Moo Sung, Paschke, Brad, Pauloski, J. Gregory, Pawar, Kamlesh, Pawlowski, Nick, Pei, Linmin, Peng, Suting, Pereira, Silvio M., Perez-Beteta, Julian, Perez-Garcia, Victor M., Pezold, Simon, Pham, Bao, Phophalia, Ashish, Piella, Gemma, Pillai, G. N., Piraud, Marie, Pisov, Maxim, Popli, Anmol, Pound, Michael P., Pourreza, Reza, Prasanna, Prateek, Prkovska, Vesna, Pridmore, Tony P., Puch, Santi, Puybareau, Élodie, Qian, Buyue, Qiao, Xu, Rajchl, Martin, Rane, Swapnil, Rebsamen, Michael, Ren, Hongliang, Ren, Xuhua, Revanuru, Karthik, Rezaei, Mina, Rippel, Oliver, Rivera, Luis Carlos, Robert, Charlotte, Rosen, Bruce, Rueckert, Daniel, Safwan, Mohammed, Salem, Mostafa, Salvi, Joaquim, Sanchez, Irina, Sánchez, Irina, Santos, Heitor M., Sartor, Emmett, Schellingerhout, Dawid, Scheufele, Klaudius, Scott, Matthew R., Scussel, Artur A., Sedlar, Sara, Serrano-Rubio, Juan Pablo, Shah, N. Jon, Shah, Nameetha, Shaikh, Mazhar, Shankar, B. Uma, Shboul, Zeina, Shen, Haipeng, Shen, Dinggang, Shen, Linlin, Shen, Haocheng, Shenoy, Varun, Shi, Feng, Shin, Hyung Eun, Shu, Hai, Sima, Diana, Sinclair, M, Smedby, Orjan, Snyder, James M., Soltaninejad, Mohammadreza, Song, Guidong, Soni, Mehul, Stawiaski, Jean, Subramanian, Shashank, Sun, Li, Sun, Roger, Sun, Jiawei, Sun, Kay, Sun, Yu, Sun, Guoxia, Sun, Shuang, Suter, Yannick R, Szilagyi, Laszlo, Talbar, Sanjay, Tao, Dacheng, Teng, Zhongzhao, Thakur, Siddhesh, Thakur, Meenakshi H, Tharakan, Sameer, Tiwari, Pallavi, Tochon, Guillaume, Tran, Tuan, Tsai, Yuhsiang M., Tseng, Kuan-Lun, Tuan, Tran Anh, Turlapov, Vadim, Tustison, Nicholas, Vakalopoulou, Maria, Valverde, Sergi, Vanguri, Rami, Vasiliev, Evgeny, Ventura, Jonathan, Vera, Luis, Vercauteren, Tom, Verrastro, C. A., Vidyaratne, Lasitha, Vilaplana, Veronica, Vivekanandan, Ajeet, Wang, Guotai, Wang, Qian, Wang, Chiatse J., Wang, Weichung, Wang, Duo, Wang, Ruixuan, Wang, Yuanyuan, Wang, Chunliang, Wen, Ning, Wen, Xin, Weninger, Leon, Wick, Wolfgang, Wu, Shaocheng, Wu, Qiang, Wu, Yihong, Xia, Yong, Xu, Yanwu, Xu, Xiaowen, Xu, Peiyuan, Yang, Tsai-Ling, Yang, Xiaoping, Yang, Hao-Yu, Yang, Junlin, Yang, Haojin, Yang, Guang, Yao, Hongdou, Ye, Xujiong, Yin, Changchang, Young-Moxon, Brett, Yu, Jinhua, Yue, Xiangyu, Zhang, Songtao, Zhang, Angela, Zhang, Kun, Zhang, Xuejie, Zhang, Lichi, Zhang, Xiaoyue, Zhang, Yazhuo, Zhang, Lei, Zhang, Jianguo, Zhang, Xiang, Zhang, Tianhao, Zhao, Sicheng, Zhao, Yu, Zhao, Xiaomei, Zhao, Liang, Zheng, Yefeng, Zhong, Liming, Zhou, Chenhong, Zhou, Xiaobing, Zhou, Fan, Zhu, Hongtu, Zhu, Jin, Zhuge, Ying, Zong, Weiwei, Kalpathy-Cramer, Jayashree, Farahani, Keyvan, Davatzikos, Christos, van Leemput, Koen, Menze, Bjoern, Bakas, Spyridon, Reyes, Mauricio, Jakab, Andras, Bauer, Stefan, Rempfler, Markus, Crimi, Alessandro, Shinohara, Russell Takeshi, Berger, Christoph, Ha, Sung Min, Rozycki, Martin, Prastawa, Marcel, Alberts, Esther, Lipkova, Jana, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan, Wiest, Roland, Kirschke, Jan, Wiestler, Benedikt, Colen, Rivka, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-Andre, Mahajan, Abhishek, Baid, Ujjwal, Gerstner, Elizabeth, Kwon, Dongjin, Acharya, Gagan, Agarwal, Manu, Alam, Mahbubul, Albiol, Alberto, Albiol, Antonio, Albiol, Francisco J., Alex, Varghese, Allinson, Nigel, Amorim, Pedro H. A., Amrutkar, Abhijit, Anand, Ganesh, Andermatt, Simon, Arbel, Tal, Arbelaez, Pablo, Avery, Aaron, Azmat, Muneeza, B., Pranjal, Bai, W, Banerjee, Subhashis, Barth, Bill, Batchelder, Thomas, Batmanghelich, Kayhan, Battistella, Enzo, Beers, Andrew, Belyaev, Mikhail, Bendszus, Martin, Benson, Eze, Bernal, Jose, Bharath, Halandur Nagaraja, Biros, George, Bisdas, Sotirios, Brown, James, Cabezas, Mariano, Cao, Shilei, Cardoso, Jorge M., Carver, Eric N, Casamitjana, Adrià, Castillo, Laura Silvana, Catà, Marcel, Cattin, Philippe, Cerigues, Albert, Chagas, Vinicius S., Chandra, Siddhartha, Chang, Yi-Ju, Chang, Shiyu, Chang, Ken, Chazalon, Joseph, Chen, Shengcong, Chen, Wei, Chen, Jefferson W, Chen, Zhaolin, Cheng, Kun, Choudhury, Ahana Roy, Chylla, Roger, Clérigues, Albert, Colleman, Steven, Colmeiro, Ramiro German Rodriguez, Combalia, Marc, Costa, Anthony, Cui, Xiaomeng, Dai, Zhenzhen, Dai, Lutao, Daza, Laura Alexandra, Deutsch, Eric, Ding, Changxing, Dong, Chao, Dong, Shidu, Dudzik, Wojciech, Eaton-Rosen, Zach, Egan, Gary, Escudero, Guilherme, Estienne, Théo, Everson, Richard, Fabrizio, Jonathan, Fan, Yong, Fang, Longwei, Feng, Xue, Ferrante, Enzo, Fidon, Lucas, Fischer, Martin, French, Andrew P., Fridman, Naomi, Fu, Huan, Fuentes, David, Gao, Yaozong, Gates, Evan, Gering, David, Gholami, Amir, Gierke, Willi, Glocker, Ben, Gong, Mingming, González-Villá, Sandra, Grosges, T., Guan, Yuanfang, Guo, Sheng, Gupta, Sudeep, Han, Woo-Sup, Han, Il Song, Harmuth, Konstantin, He, Huiguang, Hernández-Sabaté, Aura, Herrmann, Evelyn, Himthani, Naveen, Hsu, Winston, Hsu, Cheyu, Hu, Xiaojun, Hu, Xiaobin, Hu, Yan, Hu, Yifan, Hua, Rui, Huang, Teng-Yi, Huang, Weilin, Van Huffel, Sabine, Huo, Quan, HV, Vivek, Iftekharuddin, Khan M., Isensee, Fabian, Islam, Mobarakol, Jackson, Aaron S., Jambawalikar, Sachin R., Jesson, Andrew, Jian, Weijian, Jin, Peter, Jose, V Jeya Maria, Jungo, Alain, Kainz, B, Kamnitsas, Konstantinos, Kao, Po-Yu, Karnawat, Ayush, Kellermeier, Thomas, Kermi, Adel, Keutzer, Kurt, Khadir, Mohamed Tarek, Khened, Mahendra, Kickingereder, Philipp, Kim, Geena, King, Nik, Knapp, Haley, Knecht, Urspeter, Kohli, Lisa, Kong, Deren, Kong, Xiangmao, Koppers, Simon, Kori, Avinash, Krishnamurthi, Ganapathy, Krivov, Egor, Kumar, Piyush, Kushibar, Kaisar, Lachinov, Dmitrii, Lambrou, Tryphon, Lee, Joon, Lee, Chengen, Lee, Yuehchou, Lee, M, Lefkovits, Szidonia, Lefkovits, Laszlo, Levitt, James, Li, Tengfei, Li, Hongwei, Li, Wenqi, Li, Hongyang, Li, Xiaochuan, Li, Yuexiang, Li, Heng, Li, Zhenye, Li, Xiaoyu, Li, Zeju, Li, XiaoGang, Lin, Zheng-Shen, Lin, Fengming, Lio, Pietro, Liu, Chang, Liu, Boqiang, Liu, Xiang, Liu, Mingyuan, Liu, Ju, Liu, Luyan, Llado, Xavier, Lopez, Marc Moreno, Lorenzo, Pablo Ribalta, Lu, Zhentai, Luo, Lin, Luo, Zhigang, Ma, Jun, Ma, Kai, Mackie, Thomas, Madabushi, Anant, Mahmoudi, Issam, Maier-Hein, Klaus H., Maji, Pradipta, Mammen, CP, Mang, Andreas, Manjunath, B. S., Marcinkiewicz, Michal, McDonagh, S, McKenna, Stephen, McKinley, Richard, Mehl, Miriam, Mehta, Sachin, Mehta, Raghav, Meier, Raphael, Meinel, Christoph, Merhof, Dorit, Meyer, Craig, Miller, Robert, Mitra, Sushmita, Moiyadi, Aliasgar, Molina-Garcia, David, Monteiro, Miguel A. B., Mrukwa, Grzegorz, Myronenko, Andriy, Nalepa, Jakub, Ngo, Thuyen, Nie, Dong, Ning, Holly, Niu, Chen, Nuechterlein, Nicholas K, Oermann, Eric, Oliveira, Arlindo, Oliveira, Diego D. C., Oliver, Arnau, Osman, Alexander F. I., Ou, Yu-Nian, Ourselin, Sebastien, Paragios, Nikos, Park, Moo Sung, Paschke, Brad, Pauloski, J. Gregory, Pawar, Kamlesh, Pawlowski, Nick, Pei, Linmin, Peng, Suting, Pereira, Silvio M., Perez-Beteta, Julian, Perez-Garcia, Victor M., Pezold, Simon, Pham, Bao, Phophalia, Ashish, Piella, Gemma, Pillai, G. N., Piraud, Marie, Pisov, Maxim, Popli, Anmol, Pound, Michael P., Pourreza, Reza, Prasanna, Prateek, Prkovska, Vesna, Pridmore, Tony P., Puch, Santi, Puybareau, Élodie, Qian, Buyue, Qiao, Xu, Rajchl, Martin, Rane, Swapnil, Rebsamen, Michael, Ren, Hongliang, Ren, Xuhua, Revanuru, Karthik, Rezaei, Mina, Rippel, Oliver, Rivera, Luis Carlos, Robert, Charlotte, Rosen, Bruce, Rueckert, Daniel, Safwan, Mohammed, Salem, Mostafa, Salvi, Joaquim, Sanchez, Irina, Sánchez, Irina, Santos, Heitor M., Sartor, Emmett, Schellingerhout, Dawid, Scheufele, Klaudius, Scott, Matthew R., Scussel, Artur A., Sedlar, Sara, Serrano-Rubio, Juan Pablo, Shah, N. Jon, Shah, Nameetha, Shaikh, Mazhar, Shankar, B. Uma, Shboul, Zeina, Shen, Haipeng, Shen, Dinggang, Shen, Linlin, Shen, Haocheng, Shenoy, Varun, Shi, Feng, Shin, Hyung Eun, Shu, Hai, Sima, Diana, Sinclair, M, Smedby, Orjan, Snyder, James M., Soltaninejad, Mohammadreza, Song, Guidong, Soni, Mehul, Stawiaski, Jean, Subramanian, Shashank, Sun, Li, Sun, Roger, Sun, Jiawei, Sun, Kay, Sun, Yu, Sun, Guoxia, Sun, Shuang, Suter, Yannick R, Szilagyi, Laszlo, Talbar, Sanjay, Tao, Dacheng, Teng, Zhongzhao, Thakur, Siddhesh, Thakur, Meenakshi H, Tharakan, Sameer, Tiwari, Pallavi, Tochon, Guillaume, Tran, Tuan, Tsai, Yuhsiang M., Tseng, Kuan-Lun, Tuan, Tran Anh, Turlapov, Vadim, Tustison, Nicholas, Vakalopoulou, Maria, Valverde, Sergi, Vanguri, Rami, Vasiliev, Evgeny, Ventura, Jonathan, Vera, Luis, Vercauteren, Tom, Verrastro, C. A., Vidyaratne, Lasitha, Vilaplana, Veronica, Vivekanandan, Ajeet, Wang, Guotai, Wang, Qian, Wang, Chiatse J., Wang, Weichung, Wang, Duo, Wang, Ruixuan, Wang, Yuanyuan, Wang, Chunliang, Wen, Ning, Wen, Xin, Weninger, Leon, Wick, Wolfgang, Wu, Shaocheng, Wu, Qiang, Wu, Yihong, Xia, Yong, Xu, Yanwu, Xu, Xiaowen, Xu, Peiyuan, Yang, Tsai-Ling, Yang, Xiaoping, Yang, Hao-Yu, Yang, Junlin, Yang, Haojin, Yang, Guang, Yao, Hongdou, Ye, Xujiong, Yin, Changchang, Young-Moxon, Brett, Yu, Jinhua, Yue, Xiangyu, Zhang, Songtao, Zhang, Angela, Zhang, Kun, Zhang, Xuejie, Zhang, Lichi, Zhang, Xiaoyue, Zhang, Yazhuo, Zhang, Lei, Zhang, Jianguo, Zhang, Xiang, Zhang, Tianhao, Zhao, Sicheng, Zhao, Yu, Zhao, Xiaomei, Zhao, Liang, Zheng, Yefeng, Zhong, Liming, Zhou, Chenhong, Zhou, Xiaobing, Zhou, Fan, Zhu, Hongtu, Zhu, Jin, Zhuge, Ying, Zong, Weiwei, Kalpathy-Cramer, Jayashree, Farahani, Keyvan, Davatzikos, Christos, van Leemput, Koen, and Menze, Bjoern
Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset., Comment: The International Multimodal Brain Tumor Segmentation (BraTS) Challenge
Published: 2018

23. An open benchmark challenge for motion correction of myocardial perfusion MRI

Author: Pontré, Beau, Cowan, Brett R., DiBella, Edward, Kulaseharan, Sancgeetha, Likhite, Devavrat, Noorman, Nils, Tautz, Lennart, Tustison, Nicholas, Wöllny, Gert, Young, Alistair A., Suinesiaputra, Avan, Pontré, Beau, Cowan, Brett R., DiBella, Edward, Kulaseharan, Sancgeetha, Likhite, Devavrat, Noorman, Nils, Tautz, Lennart, Tustison, Nicholas, Wöllny, Gert, Young, Alistair A., and Suinesiaputra, Avan
Abstract: Cardiac magnetic resonance perfusion examinations enable noninvasive quantification of myocardial blood flow. However, motion between frames due to breathing must be corrected for quantitative analysis. Although several methods have been proposed, there is a lack of widely available benchmarks to compare different algorithms. We sought to compare many algorithms from several groups in an open benchmark challenge. Nine clinical studies from two different centers comprising normal and diseased myocardium at both rest and stress were made available for this study. The primary validation measure was regional myocardial blood flow based on the transfer coefficient (K-trans), which was computed using a compartment model and the myocardial perfusion reserve (MPR) index. The ground truth was calculated using contours drawn manually on all frames by a single observer, and visually inspected by a second observer. Six groups participated and 19 different motion correction algorithms were compared. Each method used one of three different motion models: rigid, global affine, or local deformation. The similarity metric also varied with methods employing either sum-of-squared differences, mutual information, or cross correlation. There were no significant differences in K-trans or MPR compared across different motion models or similarity metrics. Compared with the ground truth, only Ktrans for the sum-of-squared differences metric, and for local deformation motion models, had significant bias. In conclusion, the open benchmark enabled evaluation of clinical perfusion indices over a wide range of methods. In particular, there was no benefit of nonrigid registration techniques over the other methods evaluated in this study. The benchmark data and results are available from the Cardiac Atlas Project (www.cardiacatlas.org).
Published: 2017

24. An open benchmark challenge for motion correction of myocardial perfusion MRI

Author: Pontré, Beau, Cowan, Brett R., DiBella, Edward, Kulaseharan, Sancgeetha, Likhite, Devavrat, Noorman, Nils, Tautz, Lennart, Tustison, Nicholas, Wöllny, Gert, Young, Alistair A., Suinesiaputra, Avan, Pontré, Beau, Cowan, Brett R., DiBella, Edward, Kulaseharan, Sancgeetha, Likhite, Devavrat, Noorman, Nils, Tautz, Lennart, Tustison, Nicholas, Wöllny, Gert, Young, Alistair A., and Suinesiaputra, Avan
Abstract: Cardiac magnetic resonance perfusion examinations enable noninvasive quantification of myocardial blood flow. However, motion between frames due to breathing must be corrected for quantitative analysis. Although several methods have been proposed, there is a lack of widely available benchmarks to compare different algorithms. We sought to compare many algorithms from several groups in an open benchmark challenge. Nine clinical studies from two different centers comprising normal and diseased myocardium at both rest and stress were made available for this study. The primary validation measure was regional myocardial blood flow based on the transfer coefficient (K-trans), which was computed using a compartment model and the myocardial perfusion reserve (MPR) index. The ground truth was calculated using contours drawn manually on all frames by a single observer, and visually inspected by a second observer. Six groups participated and 19 different motion correction algorithms were compared. Each method used one of three different motion models: rigid, global affine, or local deformation. The similarity metric also varied with methods employing either sum-of-squared differences, mutual information, or cross correlation. There were no significant differences in K-trans or MPR compared across different motion models or similarity metrics. Compared with the ground truth, only Ktrans for the sum-of-squared differences metric, and for local deformation motion models, had significant bias. In conclusion, the open benchmark enabled evaluation of clinical perfusion indices over a wide range of methods. In particular, there was no benefit of nonrigid registration techniques over the other methods evaluated in this study. The benchmark data and results are available from the Cardiac Atlas Project (www.cardiacatlas.org).
Published: 2017

25. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Author: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Golland, Polina, Lashkari, Danial, Menze, H. Bjoern, Jakab, Andras, Bauer, Stefan, Kalpathy-Cramer, Jayashree, Farahani, Keyvan, Kirby, Justin, Burren, Yuliya, Porz, Nicole, Slotboom, Johannes, Wiest, Roland, Lanczi, Levente, Gerstner, Elizabeth, Weber, Marc-Andre, Arbel, Tal, Avants, Brian B., Ayache, Nicholas, Buendia, Patricia, Collins, D. Louis, Cordier, Nicolas, Corso, Jason J., Criminisi, Antonio, Das, Tilak, Delingette, Herve, Demiralp, Cagatay, Durst, R. Christopher, Dojat, Michel, Doyle, Senan, Festa, Joana, Forbes, Florence, Geremia, Ezequiel, Glocker, Ben, Guo, Xiaotao, Hamamci, Andac, Iftekharuddin, Khan M., Jena, Raj, John, M. Nigel, Konukoglu, Ender, Mariz, Jose Antonio, Meier, Raphael, Pereira, Sergio, Precup, Doina, Price, Stephen J., Raviv, Tammy Riklin, Reza, Syed M. S., Ryan, Michael, Sarikaya, Duygu, Schwartz, Lawrence, Shin, Hoo-Chang, Shotton, Jamie, Silva, Carlos A., Sousa, Nuno, Subbanna, Nagesh K., Szekely, Gabor, Taylor, Thomas J., Thomas, Owen M., Tustison, Nicholas J., Unal, Gozde, Vasseur, Flor, Wintermark, Max, Ye, Dong Hye, Zhao, Liang, Zhao, Binsheng, Zikic, Darko, Prastawa, Marcel, Reyes, Mauricio, Van Leemput, Koen, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Golland, Polina, Lashkari, Danial, Menze, H. Bjoern, Jakab, Andras, Bauer, Stefan, Kalpathy-Cramer, Jayashree, Farahani, Keyvan, Kirby, Justin, Burren, Yuliya, Porz, Nicole, Slotboom, Johannes, Wiest, Roland, Lanczi, Levente, Gerstner, Elizabeth, Weber, Marc-Andre, Arbel, Tal, Avants, Brian B., Ayache, Nicholas, Buendia, Patricia, Collins, D. Louis, Cordier, Nicolas, Corso, Jason J., Criminisi, Antonio, Das, Tilak, Delingette, Herve, Demiralp, Cagatay, Durst, R. Christopher, Dojat, Michel, Doyle, Senan, Festa, Joana, Forbes, Florence, Geremia, Ezequiel, Glocker, Ben, Guo, Xiaotao, Hamamci, Andac, Iftekharuddin, Khan M., Jena, Raj, John, M. Nigel, Konukoglu, Ender, Mariz, Jose Antonio, Meier, Raphael, Pereira, Sergio, Precup, Doina, Price, Stephen J., Raviv, Tammy Riklin, Reza, Syed M. S., Ryan, Michael, Sarikaya, Duygu, Schwartz, Lawrence, Shin, Hoo-Chang, Shotton, Jamie, Silva, Carlos A., Sousa, Nuno, Subbanna, Nagesh K., Szekely, Gabor, Taylor, Thomas J., Thomas, Owen M., Tustison, Nicholas J., Unal, Gozde, Vasseur, Flor, Wintermark, Max, Ye, Dong Hye, Zhao, Liang, Zhao, Binsheng, Zikic, Darko, Prastawa, Marcel, Reyes, Mauricio, and Van Leemput, Koen
Abstract: In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients - manually annotated by up to four raters - and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource., National Center for Research Resources (U.S.) (P41-RR14075), National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01EB013565), National Institute of Biomedical Imaging and Bioengineering (U.S.) (U54-EB005149), National Institutes of Health (U.S.) (P41-RR13218), National Institute of Biomedical Imaging and Bioengineering (U.S.) (P41-EB-015902), National Cancer Institute (U.S.) (R15CA115464)
Published: 2017

26. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Author: Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) [research center], Fonds National de la Recherche - FnR [sponsor], Allen, Genevera, Amoroso, Nicola, Anghel, Catalina, Balagurusamy, Venkat, Bare, Christopher, Beaton, Derek, Bellotti, Roberto, Bennett, David, Boehme, Kevin, Caberlotto, Laura, Campbell, Frederick, Chang, Yu-Chuan, Chen, Beibei, Chen, Chien-Yu, Chien, Ting-Ying, Clark, Tim, Das, Sudeshna, Davatzikos, Christos, Deng, Jieyao, Dillenberger, Donna, Dobson, Richard, Dong, Qilin, Doshi, Jimit, Duma, Denise, Errico, Rosangela, Erus, Guray, Everett, Evan, Fardo, David, Friend, Stephen, Fröhlich, Holger, Gan, Jessica, St George-Hyslop, Peter, Ghosh, Satrajit, Glaab, Enrico, Green, Robert, Guan, Yuanfang, Hong, Ming-Yi, Huang, Chao, Hwang, Jinseub, Ibrahim, Joseph, Inglese, Paolo, Jiang, Qijia, Katsumata, Yurik, Kauwe, Jo, Klein, Arno, Kong, Dehan, Krause, Roland, Lalonde, Emilie, Lauria, Mari, Lee, Eunjee, Lin, Xihui, Liu, Zhandong, Livingstone, Julie, Logsdon, Benjami, Lovestone, Simon, Iyappan, Anandhi, Ma, Tsung-wei, Malhotra, Ashutos, Maxwell, Taylor, Merrill, Emily, Nagorski, John, Alex Namasivayam, Aishwarya, Narayan, Manjari, Naz, Mufassra, Neto, Elias Chaibub, Newhouse, Stephen, Norman, Thea, Nurtdinov, Ramil, Oyang, Yen-Jen, Boutros, Paul, Pawitan, Yudi, Peng, Shengwen, Peters, Mette, Piccolo, Stephen, Praveen, Paurush, Priami, Corrado, Sabelnykova, Veronica, Senger, Philipp, Shen, Xia, Simmons, Andrew, Sotiras, Aristeidis, Stolovitzky, Gustavo, Tangaro, Sabina, Tateo, Andrea, Tung, Yi-An, Tustison, Nicholas, Varol, Erdem, Vradenburg, George, Weiner, Michael, Xiao, Guanghua, Xie, Lei, Xie, Yang, Xu, Jia, Yang, Hojin, Zhan, Xiaowei, Zhou, Yunyun, Zhu, Fan, Zhu, Hongtu, Zhu, Shanfeng, Alzheimer's Disease Neuroimaging Initiative, Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) [research center], Fonds National de la Recherche - FnR [sponsor], Allen, Genevera, Amoroso, Nicola, Anghel, Catalina, Balagurusamy, Venkat, Bare, Christopher, Beaton, Derek, Bellotti, Roberto, Bennett, David, Boehme, Kevin, Caberlotto, Laura, Campbell, Frederick, Chang, Yu-Chuan, Chen, Beibei, Chen, Chien-Yu, Chien, Ting-Ying, Clark, Tim, Das, Sudeshna, Davatzikos, Christos, Deng, Jieyao, Dillenberger, Donna, Dobson, Richard, Dong, Qilin, Doshi, Jimit, Duma, Denise, Errico, Rosangela, Erus, Guray, Everett, Evan, Fardo, David, Friend, Stephen, Fröhlich, Holger, Gan, Jessica, St George-Hyslop, Peter, Ghosh, Satrajit, Glaab, Enrico, Green, Robert, Guan, Yuanfang, Hong, Ming-Yi, Huang, Chao, Hwang, Jinseub, Ibrahim, Joseph, Inglese, Paolo, Jiang, Qijia, Katsumata, Yurik, Kauwe, Jo, Klein, Arno, Kong, Dehan, Krause, Roland, Lalonde, Emilie, Lauria, Mari, Lee, Eunjee, Lin, Xihui, Liu, Zhandong, Livingstone, Julie, Logsdon, Benjami, Lovestone, Simon, Iyappan, Anandhi, Ma, Tsung-wei, Malhotra, Ashutos, Maxwell, Taylor, Merrill, Emily, Nagorski, John, Alex Namasivayam, Aishwarya, Narayan, Manjari, Naz, Mufassra, Neto, Elias Chaibub, Newhouse, Stephen, Norman, Thea, Nurtdinov, Ramil, Oyang, Yen-Jen, Boutros, Paul, Pawitan, Yudi, Peng, Shengwen, Peters, Mette, Piccolo, Stephen, Praveen, Paurush, Priami, Corrado, Sabelnykova, Veronica, Senger, Philipp, Shen, Xia, Simmons, Andrew, Sotiras, Aristeidis, Stolovitzky, Gustavo, Tangaro, Sabina, Tateo, Andrea, Tung, Yi-An, Tustison, Nicholas, Varol, Erdem, Vradenburg, George, Weiner, Michael, Xiao, Guanghua, Xie, Lei, Xie, Yang, Xu, Jia, Yang, Hojin, Zhan, Xiaowei, Zhou, Yunyun, Zhu, Fan, Zhu, Hongtu, Zhu, Shanfeng, and Alzheimer's Disease Neuroimaging Initiative
Published: 2016

27. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Author: Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) [research center], Fonds National de la Recherche - FnR [sponsor], Allen, Genevera, Amoroso, Nicola, Anghel, Catalina, Balagurusamy, Venkat, Bare, Christopher, Beaton, Derek, Bellotti, Roberto, Bennett, David, Boehme, Kevin, Caberlotto, Laura, Campbell, Frederick, Chang, Yu-Chuan, Chen, Beibei, Chen, Chien-Yu, Chien, Ting-Ying, Clark, Tim, Das, Sudeshna, Davatzikos, Christos, Deng, Jieyao, Dillenberger, Donna, Dobson, Richard, Dong, Qilin, Doshi, Jimit, Duma, Denise, Errico, Rosangela, Erus, Guray, Everett, Evan, Fardo, David, Friend, Stephen, Fröhlich, Holger, Gan, Jessica, St George-Hyslop, Peter, Ghosh, Satrajit, Glaab, Enrico, Green, Robert, Guan, Yuanfang, Hong, Ming-Yi, Huang, Chao, Hwang, Jinseub, Ibrahim, Joseph, Inglese, Paolo, Jiang, Qijia, Katsumata, Yurik, Kauwe, Jo, Klein, Arno, Kong, Dehan, Krause, Roland, Lalonde, Emilie, Lauria, Mari, Lee, Eunjee, Lin, Xihui, Liu, Zhandong, Livingstone, Julie, Logsdon, Benjami, Lovestone, Simon, Iyappan, Anandhi, Ma, Tsung-wei, Malhotra, Ashutos, Maxwell, Taylor, Merrill, Emily, Nagorski, John, Alex Namasivayam, Aishwarya, Narayan, Manjari, Naz, Mufassra, Neto, Elias Chaibub, Newhouse, Stephen, Norman, Thea, Nurtdinov, Ramil, Oyang, Yen-Jen, Boutros, Paul, Pawitan, Yudi, Peng, Shengwen, Peters, Mette, Piccolo, Stephen, Praveen, Paurush, Priami, Corrado, Sabelnykova, Veronica, Senger, Philipp, Shen, Xia, Simmons, Andrew, Sotiras, Aristeidis, Stolovitzky, Gustavo, Tangaro, Sabina, Tateo, Andrea, Tung, Yi-An, Tustison, Nicholas, Varol, Erdem, Vradenburg, George, Weiner, Michael, Xiao, Guanghua, Xie, Lei, Xie, Yang, Xu, Jia, Yang, Hojin, Zhan, Xiaowei, Zhou, Yunyun, Zhu, Fan, Zhu, Hongtu, Zhu, Shanfeng, Alzheimer's Disease Neuroimaging Initiative, Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) [research center], Fonds National de la Recherche - FnR [sponsor], Allen, Genevera, Amoroso, Nicola, Anghel, Catalina, Balagurusamy, Venkat, Bare, Christopher, Beaton, Derek, Bellotti, Roberto, Bennett, David, Boehme, Kevin, Caberlotto, Laura, Campbell, Frederick, Chang, Yu-Chuan, Chen, Beibei, Chen, Chien-Yu, Chien, Ting-Ying, Clark, Tim, Das, Sudeshna, Davatzikos, Christos, Deng, Jieyao, Dillenberger, Donna, Dobson, Richard, Dong, Qilin, Doshi, Jimit, Duma, Denise, Errico, Rosangela, Erus, Guray, Everett, Evan, Fardo, David, Friend, Stephen, Fröhlich, Holger, Gan, Jessica, St George-Hyslop, Peter, Ghosh, Satrajit, Glaab, Enrico, Green, Robert, Guan, Yuanfang, Hong, Ming-Yi, Huang, Chao, Hwang, Jinseub, Ibrahim, Joseph, Inglese, Paolo, Jiang, Qijia, Katsumata, Yurik, Kauwe, Jo, Klein, Arno, Kong, Dehan, Krause, Roland, Lalonde, Emilie, Lauria, Mari, Lee, Eunjee, Lin, Xihui, Liu, Zhandong, Livingstone, Julie, Logsdon, Benjami, Lovestone, Simon, Iyappan, Anandhi, Ma, Tsung-wei, Malhotra, Ashutos, Maxwell, Taylor, Merrill, Emily, Nagorski, John, Alex Namasivayam, Aishwarya, Narayan, Manjari, Naz, Mufassra, Neto, Elias Chaibub, Newhouse, Stephen, Norman, Thea, Nurtdinov, Ramil, Oyang, Yen-Jen, Boutros, Paul, Pawitan, Yudi, Peng, Shengwen, Peters, Mette, Piccolo, Stephen, Praveen, Paurush, Priami, Corrado, Sabelnykova, Veronica, Senger, Philipp, Shen, Xia, Simmons, Andrew, Sotiras, Aristeidis, Stolovitzky, Gustavo, Tangaro, Sabina, Tateo, Andrea, Tung, Yi-An, Tustison, Nicholas, Varol, Erdem, Vradenburg, George, Weiner, Michael, Xiao, Guanghua, Xie, Lei, Xie, Yang, Xu, Jia, Yang, Hojin, Zhan, Xiaowei, Zhou, Yunyun, Zhu, Fan, Zhu, Hongtu, Zhu, Shanfeng, and Alzheimer's Disease Neuroimaging Initiative
Published: 2016

28. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Author: Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) [research center], Fonds National de la Recherche - FnR [sponsor], Allen, Genevera, Amoroso, Nicola, Anghel, Catalina, Balagurusamy, Venkat, Bare, Christopher, Beaton, Derek, Bellotti, Roberto, Bennett, David, Boehme, Kevin, Caberlotto, Laura, Campbell, Frederick, Chang, Yu-Chuan, Chen, Beibei, Chen, Chien-Yu, Chien, Ting-Ying, Clark, Tim, Das, Sudeshna, Davatzikos, Christos, Deng, Jieyao, Dillenberger, Donna, Dobson, Richard, Dong, Qilin, Doshi, Jimit, Duma, Denise, Errico, Rosangela, Erus, Guray, Everett, Evan, Fardo, David, Friend, Stephen, Fröhlich, Holger, Gan, Jessica, St George-Hyslop, Peter, Ghosh, Satrajit, Glaab, Enrico, Green, Robert, Guan, Yuanfang, Hong, Ming-Yi, Huang, Chao, Hwang, Jinseub, Ibrahim, Joseph, Inglese, Paolo, Jiang, Qijia, Katsumata, Yurik, Kauwe, Jo, Klein, Arno, Kong, Dehan, Krause, Roland, Lalonde, Emilie, Lauria, Mari, Lee, Eunjee, Lin, Xihui, Liu, Zhandong, Livingstone, Julie, Logsdon, Benjami, Lovestone, Simon, Iyappan, Anandhi, Ma, Tsung-wei, Malhotra, Ashutos, Maxwell, Taylor, Merrill, Emily, Nagorski, John, Alex Namasivayam, Aishwarya, Narayan, Manjari, Naz, Mufassra, Neto, Elias Chaibub, Newhouse, Stephen, Norman, Thea, Nurtdinov, Ramil, Oyang, Yen-Jen, Boutros, Paul, Pawitan, Yudi, Peng, Shengwen, Peters, Mette, Piccolo, Stephen, Praveen, Paurush, Priami, Corrado, Sabelnykova, Veronica, Senger, Philipp, Shen, Xia, Simmons, Andrew, Sotiras, Aristeidis, Stolovitzky, Gustavo, Tangaro, Sabina, Tateo, Andrea, Tung, Yi-An, Tustison, Nicholas, Varol, Erdem, Vradenburg, George, Weiner, Michael, Xiao, Guanghua, Xie, Lei, Xie, Yang, Xu, Jia, Yang, Hojin, Zhan, Xiaowei, Zhou, Yunyun, Zhu, Fan, Zhu, Hongtu, Zhu, Shanfeng, Alzheimer's Disease Neuroimaging Initiative, Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) [research center], Fonds National de la Recherche - FnR [sponsor], Allen, Genevera, Amoroso, Nicola, Anghel, Catalina, Balagurusamy, Venkat, Bare, Christopher, Beaton, Derek, Bellotti, Roberto, Bennett, David, Boehme, Kevin, Caberlotto, Laura, Campbell, Frederick, Chang, Yu-Chuan, Chen, Beibei, Chen, Chien-Yu, Chien, Ting-Ying, Clark, Tim, Das, Sudeshna, Davatzikos, Christos, Deng, Jieyao, Dillenberger, Donna, Dobson, Richard, Dong, Qilin, Doshi, Jimit, Duma, Denise, Errico, Rosangela, Erus, Guray, Everett, Evan, Fardo, David, Friend, Stephen, Fröhlich, Holger, Gan, Jessica, St George-Hyslop, Peter, Ghosh, Satrajit, Glaab, Enrico, Green, Robert, Guan, Yuanfang, Hong, Ming-Yi, Huang, Chao, Hwang, Jinseub, Ibrahim, Joseph, Inglese, Paolo, Jiang, Qijia, Katsumata, Yurik, Kauwe, Jo, Klein, Arno, Kong, Dehan, Krause, Roland, Lalonde, Emilie, Lauria, Mari, Lee, Eunjee, Lin, Xihui, Liu, Zhandong, Livingstone, Julie, Logsdon, Benjami, Lovestone, Simon, Iyappan, Anandhi, Ma, Tsung-wei, Malhotra, Ashutos, Maxwell, Taylor, Merrill, Emily, Nagorski, John, Alex Namasivayam, Aishwarya, Narayan, Manjari, Naz, Mufassra, Neto, Elias Chaibub, Newhouse, Stephen, Norman, Thea, Nurtdinov, Ramil, Oyang, Yen-Jen, Boutros, Paul, Pawitan, Yudi, Peng, Shengwen, Peters, Mette, Piccolo, Stephen, Praveen, Paurush, Priami, Corrado, Sabelnykova, Veronica, Senger, Philipp, Shen, Xia, Simmons, Andrew, Sotiras, Aristeidis, Stolovitzky, Gustavo, Tangaro, Sabina, Tateo, Andrea, Tung, Yi-An, Tustison, Nicholas, Varol, Erdem, Vradenburg, George, Weiner, Michael, Xiao, Guanghua, Xie, Lei, Xie, Yang, Xu, Jia, Yang, Hojin, Zhan, Xiaowei, Zhou, Yunyun, Zhu, Fan, Zhu, Hongtu, Zhu, Shanfeng, and Alzheimer's Disease Neuroimaging Initiative
Published: 2016

29. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Author: Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) [research center], Fonds National de la Recherche - FnR [sponsor], Allen, Genevera, Amoroso, Nicola, Anghel, Catalina, Balagurusamy, Venkat, Bare, Christopher, Beaton, Derek, Bellotti, Roberto, Bennett, David, Boehme, Kevin, Caberlotto, Laura, Campbell, Frederick, Chang, Yu-Chuan, Chen, Beibei, Chen, Chien-Yu, Chien, Ting-Ying, Clark, Tim, Das, Sudeshna, Davatzikos, Christos, Deng, Jieyao, Dillenberger, Donna, Dobson, Richard, Dong, Qilin, Doshi, Jimit, Duma, Denise, Errico, Rosangela, Erus, Guray, Everett, Evan, Fardo, David, Friend, Stephen, Fröhlich, Holger, Gan, Jessica, St George-Hyslop, Peter, Ghosh, Satrajit, Glaab, Enrico, Green, Robert, Guan, Yuanfang, Hong, Ming-Yi, Huang, Chao, Hwang, Jinseub, Ibrahim, Joseph, Inglese, Paolo, Jiang, Qijia, Katsumata, Yurik, Kauwe, Jo, Klein, Arno, Kong, Dehan, Krause, Roland, Lalonde, Emilie, Lauria, Mari, Lee, Eunjee, Lin, Xihui, Liu, Zhandong, Livingstone, Julie, Logsdon, Benjami, Lovestone, Simon, Iyappan, Anandhi, Ma, Tsung-wei, Malhotra, Ashutos, Maxwell, Taylor, Merrill, Emily, Nagorski, John, Alex Namasivayam, Aishwarya, Narayan, Manjari, Naz, Mufassra, Neto, Elias Chaibub, Newhouse, Stephen, Norman, Thea, Nurtdinov, Ramil, Oyang, Yen-Jen, Boutros, Paul, Pawitan, Yudi, Peng, Shengwen, Peters, Mette, Piccolo, Stephen, Praveen, Paurush, Priami, Corrado, Sabelnykova, Veronica, Senger, Philipp, Shen, Xia, Simmons, Andrew, Sotiras, Aristeidis, Stolovitzky, Gustavo, Tangaro, Sabina, Tateo, Andrea, Tung, Yi-An, Tustison, Nicholas, Varol, Erdem, Vradenburg, George, Weiner, Michael, Xiao, Guanghua, Xie, Lei, Xie, Yang, Xu, Jia, Yang, Hojin, Zhan, Xiaowei, Zhou, Yunyun, Zhu, Fan, Zhu, Hongtu, Zhu, Shanfeng, Alzheimer's Disease Neuroimaging Initiative, Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) [research center], Fonds National de la Recherche - FnR [sponsor], Allen, Genevera, Amoroso, Nicola, Anghel, Catalina, Balagurusamy, Venkat, Bare, Christopher, Beaton, Derek, Bellotti, Roberto, Bennett, David, Boehme, Kevin, Caberlotto, Laura, Campbell, Frederick, Chang, Yu-Chuan, Chen, Beibei, Chen, Chien-Yu, Chien, Ting-Ying, Clark, Tim, Das, Sudeshna, Davatzikos, Christos, Deng, Jieyao, Dillenberger, Donna, Dobson, Richard, Dong, Qilin, Doshi, Jimit, Duma, Denise, Errico, Rosangela, Erus, Guray, Everett, Evan, Fardo, David, Friend, Stephen, Fröhlich, Holger, Gan, Jessica, St George-Hyslop, Peter, Ghosh, Satrajit, Glaab, Enrico, Green, Robert, Guan, Yuanfang, Hong, Ming-Yi, Huang, Chao, Hwang, Jinseub, Ibrahim, Joseph, Inglese, Paolo, Jiang, Qijia, Katsumata, Yurik, Kauwe, Jo, Klein, Arno, Kong, Dehan, Krause, Roland, Lalonde, Emilie, Lauria, Mari, Lee, Eunjee, Lin, Xihui, Liu, Zhandong, Livingstone, Julie, Logsdon, Benjami, Lovestone, Simon, Iyappan, Anandhi, Ma, Tsung-wei, Malhotra, Ashutos, Maxwell, Taylor, Merrill, Emily, Nagorski, John, Alex Namasivayam, Aishwarya, Narayan, Manjari, Naz, Mufassra, Neto, Elias Chaibub, Newhouse, Stephen, Norman, Thea, Nurtdinov, Ramil, Oyang, Yen-Jen, Boutros, Paul, Pawitan, Yudi, Peng, Shengwen, Peters, Mette, Piccolo, Stephen, Praveen, Paurush, Priami, Corrado, Sabelnykova, Veronica, Senger, Philipp, Shen, Xia, Simmons, Andrew, Sotiras, Aristeidis, Stolovitzky, Gustavo, Tangaro, Sabina, Tateo, Andrea, Tung, Yi-An, Tustison, Nicholas, Varol, Erdem, Vradenburg, George, Weiner, Michael, Xiao, Guanghua, Xie, Lei, Xie, Yang, Xu, Jia, Yang, Hojin, Zhan, Xiaowei, Zhou, Yunyun, Zhu, Fan, Zhu, Hongtu, Zhu, Shanfeng, and Alzheimer's Disease Neuroimaging Initiative
Published: 2016

30. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Author: Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) [research center], Fonds National de la Recherche - FnR [sponsor], Allen, Genevera, Amoroso, Nicola, Anghel, Catalina, Balagurusamy, Venkat, Bare, Christopher, Beaton, Derek, Bellotti, Roberto, Bennett, David, Boehme, Kevin, Caberlotto, Laura, Campbell, Frederick, Chang, Yu-Chuan, Chen, Beibei, Chen, Chien-Yu, Chien, Ting-Ying, Clark, Tim, Das, Sudeshna, Davatzikos, Christos, Deng, Jieyao, Dillenberger, Donna, Dobson, Richard, Dong, Qilin, Doshi, Jimit, Duma, Denise, Errico, Rosangela, Erus, Guray, Everett, Evan, Fardo, David, Friend, Stephen, Fröhlich, Holger, Gan, Jessica, St George-Hyslop, Peter, Ghosh, Satrajit, Glaab, Enrico, Green, Robert, Guan, Yuanfang, Hong, Ming-Yi, Huang, Chao, Hwang, Jinseub, Ibrahim, Joseph, Inglese, Paolo, Jiang, Qijia, Katsumata, Yurik, Kauwe, Jo, Klein, Arno, Kong, Dehan, Krause, Roland, Lalonde, Emilie, Lauria, Mari, Lee, Eunjee, Lin, Xihui, Liu, Zhandong, Livingstone, Julie, Logsdon, Benjami, Lovestone, Simon, Iyappan, Anandhi, Ma, Tsung-wei, Malhotra, Ashutos, Maxwell, Taylor, Merrill, Emily, Nagorski, John, Alex Namasivayam, Aishwarya, Narayan, Manjari, Naz, Mufassra, Neto, Elias Chaibub, Newhouse, Stephen, Norman, Thea, Nurtdinov, Ramil, Oyang, Yen-Jen, Boutros, Paul, Pawitan, Yudi, Peng, Shengwen, Peters, Mette, Piccolo, Stephen, Praveen, Paurush, Priami, Corrado, Sabelnykova, Veronica, Senger, Philipp, Shen, Xia, Simmons, Andrew, Sotiras, Aristeidis, Stolovitzky, Gustavo, Tangaro, Sabina, Tateo, Andrea, Tung, Yi-An, Tustison, Nicholas, Varol, Erdem, Vradenburg, George, Weiner, Michael, Xiao, Guanghua, Xie, Lei, Xie, Yang, Xu, Jia, Yang, Hojin, Zhan, Xiaowei, Zhou, Yunyun, Zhu, Fan, Zhu, Hongtu, Zhu, Shanfeng, Alzheimer's Disease Neuroimaging Initiative, Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) [research center], Fonds National de la Recherche - FnR [sponsor], Allen, Genevera, Amoroso, Nicola, Anghel, Catalina, Balagurusamy, Venkat, Bare, Christopher, Beaton, Derek, Bellotti, Roberto, Bennett, David, Boehme, Kevin, Caberlotto, Laura, Campbell, Frederick, Chang, Yu-Chuan, Chen, Beibei, Chen, Chien-Yu, Chien, Ting-Ying, Clark, Tim, Das, Sudeshna, Davatzikos, Christos, Deng, Jieyao, Dillenberger, Donna, Dobson, Richard, Dong, Qilin, Doshi, Jimit, Duma, Denise, Errico, Rosangela, Erus, Guray, Everett, Evan, Fardo, David, Friend, Stephen, Fröhlich, Holger, Gan, Jessica, St George-Hyslop, Peter, Ghosh, Satrajit, Glaab, Enrico, Green, Robert, Guan, Yuanfang, Hong, Ming-Yi, Huang, Chao, Hwang, Jinseub, Ibrahim, Joseph, Inglese, Paolo, Jiang, Qijia, Katsumata, Yurik, Kauwe, Jo, Klein, Arno, Kong, Dehan, Krause, Roland, Lalonde, Emilie, Lauria, Mari, Lee, Eunjee, Lin, Xihui, Liu, Zhandong, Livingstone, Julie, Logsdon, Benjami, Lovestone, Simon, Iyappan, Anandhi, Ma, Tsung-wei, Malhotra, Ashutos, Maxwell, Taylor, Merrill, Emily, Nagorski, John, Alex Namasivayam, Aishwarya, Narayan, Manjari, Naz, Mufassra, Neto, Elias Chaibub, Newhouse, Stephen, Norman, Thea, Nurtdinov, Ramil, Oyang, Yen-Jen, Boutros, Paul, Pawitan, Yudi, Peng, Shengwen, Peters, Mette, Piccolo, Stephen, Praveen, Paurush, Priami, Corrado, Sabelnykova, Veronica, Senger, Philipp, Shen, Xia, Simmons, Andrew, Sotiras, Aristeidis, Stolovitzky, Gustavo, Tangaro, Sabina, Tateo, Andrea, Tung, Yi-An, Tustison, Nicholas, Varol, Erdem, Vradenburg, George, Weiner, Michael, Xiao, Guanghua, Xie, Lei, Xie, Yang, Xu, Jia, Yang, Hojin, Zhan, Xiaowei, Zhou, Yunyun, Zhu, Fan, Zhu, Hongtu, Zhu, Shanfeng, and Alzheimer's Disease Neuroimaging Initiative
Published: 2016

31. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease.

Author: Allen, Genevera I, Allen, Genevera I, Amoroso, Nicola, Anghel, Catalina, Balagurusamy, Venkat, Bare, Christopher J, Beaton, Derek, Bellotti, Roberto, Bennett, David A, Boehme, Kevin L, Boutros, Paul C, Caberlotto, Laura, Caloian, Cristian, Campbell, Frederick, Chaibub Neto, Elias, Chang, Yu-Chuan, Chen, Beibei, Chen, Chien-Yu, Chien, Ting-Ying, Clark, Tim, Das, Sudeshna, Davatzikos, Christos, Deng, Jieyao, Dillenberger, Donna, Dobson, Richard JB, Dong, Qilin, Doshi, Jimit, Duma, Denise, Errico, Rosangela, Erus, Guray, Everett, Evan, Fardo, David W, Friend, Stephen H, Fröhlich, Holger, Gan, Jessica, St George-Hyslop, Peter, Ghosh, Satrajit S, Glaab, Enrico, Green, Robert C, Guan, Yuanfang, Hong, Ming-Yi, Huang, Chao, Hwang, Jinseub, Ibrahim, Joseph, Inglese, Paolo, Iyappan, Anandhi, Jiang, Qijia, Katsumata, Yuriko, Kauwe, John SK, Klein, Arno, Kong, Dehan, Krause, Roland, Lalonde, Emilie, Lauria, Mario, Lee, Eunjee, Lin, Xihui, Liu, Zhandong, Livingstone, Julie, Logsdon, Benjamin A, Lovestone, Simon, Ma, Tsung-Wei, Malhotra, Ashutosh, Mangravite, Lara M, Maxwell, Taylor J, Merrill, Emily, Nagorski, John, Namasivayam, Aishwarya, Narayan, Manjari, Naz, Mufassra, Newhouse, Stephen J, Norman, Thea C, Nurtdinov, Ramil N, Oyang, Yen-Jen, Pawitan, Yudi, Peng, Shengwen, Peters, Mette A, Piccolo, Stephen R, Praveen, Paurush, Priami, Corrado, Sabelnykova, Veronica Y, Senger, Philipp, Shen, Xia, Simmons, Andrew, Sotiras, Aristeidis, Stolovitzky, Gustavo, Tangaro, Sabina, Tateo, Andrea, Tung, Yi-An, Tustison, Nicholas J, Varol, Erdem, Vradenburg, George, Weiner, Michael W, Xiao, Guanghua, Xie, Lei, Xie, Yang, Xu, Jia, Yang, Hojin, Zhan, Xiaowei, Zhou, Yunyun, Zhu, Fan, Zhu, Hongtu, Allen, Genevera I, Allen, Genevera I, Amoroso, Nicola, Anghel, Catalina, Balagurusamy, Venkat, Bare, Christopher J, Beaton, Derek, Bellotti, Roberto, Bennett, David A, Boehme, Kevin L, Boutros, Paul C, Caberlotto, Laura, Caloian, Cristian, Campbell, Frederick, Chaibub Neto, Elias, Chang, Yu-Chuan, Chen, Beibei, Chen, Chien-Yu, Chien, Ting-Ying, Clark, Tim, Das, Sudeshna, Davatzikos, Christos, Deng, Jieyao, Dillenberger, Donna, Dobson, Richard JB, Dong, Qilin, Doshi, Jimit, Duma, Denise, Errico, Rosangela, Erus, Guray, Everett, Evan, Fardo, David W, Friend, Stephen H, Fröhlich, Holger, Gan, Jessica, St George-Hyslop, Peter, Ghosh, Satrajit S, Glaab, Enrico, Green, Robert C, Guan, Yuanfang, Hong, Ming-Yi, Huang, Chao, Hwang, Jinseub, Ibrahim, Joseph, Inglese, Paolo, Iyappan, Anandhi, Jiang, Qijia, Katsumata, Yuriko, Kauwe, John SK, Klein, Arno, Kong, Dehan, Krause, Roland, Lalonde, Emilie, Lauria, Mario, Lee, Eunjee, Lin, Xihui, Liu, Zhandong, Livingstone, Julie, Logsdon, Benjamin A, Lovestone, Simon, Ma, Tsung-Wei, Malhotra, Ashutosh, Mangravite, Lara M, Maxwell, Taylor J, Merrill, Emily, Nagorski, John, Namasivayam, Aishwarya, Narayan, Manjari, Naz, Mufassra, Newhouse, Stephen J, Norman, Thea C, Nurtdinov, Ramil N, Oyang, Yen-Jen, Pawitan, Yudi, Peng, Shengwen, Peters, Mette A, Piccolo, Stephen R, Praveen, Paurush, Priami, Corrado, Sabelnykova, Veronica Y, Senger, Philipp, Shen, Xia, Simmons, Andrew, Sotiras, Aristeidis, Stolovitzky, Gustavo, Tangaro, Sabina, Tateo, Andrea, Tung, Yi-An, Tustison, Nicholas J, Varol, Erdem, Vradenburg, George, Weiner, Michael W, Xiao, Guanghua, Xie, Lei, Xie, Yang, Xu, Jia, Yang, Hojin, Zhan, Xiaowei, Zhou, Yunyun, Zhu, Fan, and Zhu, Hongtu
Abstract: Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.
Published: 2016

32. The pediatric template of brain perfusion.

Author: Avants, Brian B, Avants, Brian B, Duda, Jeffrey T, Kilroy, Emily, Krasileva, Kate, Jann, Kay, Kandel, Benjamin T, Tustison, Nicholas J, Yan, Lirong, Jog, Mayank, Smith, Robert, Wang, Yi, Dapretto, Mirella, Wang, Danny JJ, Avants, Brian B, Avants, Brian B, Duda, Jeffrey T, Kilroy, Emily, Krasileva, Kate, Jann, Kay, Kandel, Benjamin T, Tustison, Nicholas J, Yan, Lirong, Jog, Mayank, Smith, Robert, Wang, Yi, Dapretto, Mirella, and Wang, Danny JJ
Abstract: Magnetic resonance imaging (MRI) captures the dynamics of brain development with multiple modalities that quantify both structure and function. These measurements may yield valuable insights into the neural patterns that mark healthy maturation or that identify early risk for psychiatric disorder. The Pediatric Template of Brain Perfusion (PTBP) is a free and public neuroimaging resource that will help accelerate the understanding of childhood brain development as seen through the lens of multiple modality neuroimaging and in relation to cognitive and environmental factors. The PTBP uses cross-sectional and longitudinal MRI to quantify cortex, white matter, resting state functional connectivity and brain perfusion, as measured by Arterial Spin Labeling (ASL), in 120 children 7-18 years of age. We describe the PTBP and show, as a demonstration of validity, that global summary measurements capture the trajectories that demarcate critical turning points in brain maturation. This novel resource will allow a more detailed understanding of the network-level, structural and functional landmarks that are obtained during normal adolescent brain development.
Published: 2015

33. The pediatric template of brain perfusion.

Author: Avants, Brian B, Avants, Brian B, Duda, Jeffrey T, Kilroy, Emily, Krasileva, Kate, Jann, Kay, Kandel, Benjamin T, Tustison, Nicholas J, Yan, Lirong, Jog, Mayank, Smith, Robert, Wang, Yi, Dapretto, Mirella, Wang, Danny JJ, Avants, Brian B, Avants, Brian B, Duda, Jeffrey T, Kilroy, Emily, Krasileva, Kate, Jann, Kay, Kandel, Benjamin T, Tustison, Nicholas J, Yan, Lirong, Jog, Mayank, Smith, Robert, Wang, Yi, Dapretto, Mirella, and Wang, Danny JJ
Abstract: Magnetic resonance imaging (MRI) captures the dynamics of brain development with multiple modalities that quantify both structure and function. These measurements may yield valuable insights into the neural patterns that mark healthy maturation or that identify early risk for psychiatric disorder. The Pediatric Template of Brain Perfusion (PTBP) is a free and public neuroimaging resource that will help accelerate the understanding of childhood brain development as seen through the lens of multiple modality neuroimaging and in relation to cognitive and environmental factors. The PTBP uses cross-sectional and longitudinal MRI to quantify cortex, white matter, resting state functional connectivity and brain perfusion, as measured by Arterial Spin Labeling (ASL), in 120 children 7-18 years of age. We describe the PTBP and show, as a demonstration of validity, that global summary measurements capture the trajectories that demarcate critical turning points in brain maturation. This novel resource will allow a more detailed understanding of the network-level, structural and functional landmarks that are obtained during normal adolescent brain development.
Published: 2015

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33 results on '"Tustison, Nicholas"'

1. Joint-label fusion brain atlases for dementia research in Down syndrome.

2. QU-BraTS : MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

3. QU-BraTS : MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

4. QU-BraTS : MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

5. Posterior white matter hyperintensities are associated with reduced medial temporal lobe subregional integrity and long-term memory in older adults.

6. Joint-label fusion brain atlases for dementia research in Down syndrome.

7. QU-BraTS : MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

8. QU-BraTS : MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

9. Longitudinal Mapping of Cortical Thickness Measurements: An Alzheimer's Disease Neuroimaging Initiative-Based Evaluation Study.

10. QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

11. The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

12. Characterizing Gas Exchange Physiology in Healthy Young Electronic-Cigarette Users with Hyperpolarized 129Xe MRI: A Pilot Study

13. Interpretable, similarity-driven multi-view embeddings from high-dimensional biomedical data

14. Anterolateral entorhinal cortex thickness as a new biomarker for early detection of Alzheimer's disease

15. Multi-site meta-analysis of morphometry

16. A randomized, double-blind, placebo-controlled trial of lamotrigine for prescription corticosteroid effects on the human hippocampus.

17. The Relationship Between Cumulative Exogenous Corticosteroid Exposure and Volumes of Hippocampal Subfields and Surrounding Structures.

18. A randomized trial of an NMDA receptor antagonist for reversing corticosteroid effects on the human hippocampus.

19. Functional Imbalance of Anterolateral Entorhinal Cortex and Hippocampal Dentate/CA3 Underlies Age-Related Object Pattern Separation Deficits.

20. Functional Imbalance of Anterolateral Entorhinal Cortex and Hippocampal Dentate/CA3 Underlies Age-Related Object Pattern Separation Deficits.

21. Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features

22. Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

23. An open benchmark challenge for motion correction of myocardial perfusion MRI

24. An open benchmark challenge for motion correction of myocardial perfusion MRI

25. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

26. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

27. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

28. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

29. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

30. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

31. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease.

32. The pediatric template of brain perfusion.

33. The pediatric template of brain perfusion.

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33 results on '"Tustison, Nicholas"'

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