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1,189 results on '"Yushkevich, Paul A."'

1. Implementation and validation of face de-identification (de-facing) in ADNI4.

Author

Schwarz, Christopher, Choe, Mark, Rossi, Stephanie, Das, Sandhitsu, Ittyerah, Ranjit, Fletcher, Evan, Maillard, Pauline, Singh, Baljeet, Harvey, Danielle, Malone, Ian, Prosser, Lloyd, Senjem, Matthew, Matoush, Leonard, Ward, Chadwick, Prakaashana, Carl, Landau, Susan, Koeppe, Robert, Lee, JiaQie, Decarli, Charles, Weiner, Michael, Jack, Clifford, Jagust, William, Yushkevich, Paul, and Tosun, Duygu

Subjects
ADNI, anonymization, de‐facing, de‐identification, face recognition, Humans, Alzheimer Disease, Magnetic Resonance Imaging, Brain, Neuroimaging, Reproducibility of Results, Face, Algorithms
Abstract

INTRODUCTION: Recent technological advances have increased the risk that de-identified brain images could be re-identified from face imagery. The Alzheimers Disease Neuroimaging Initiative (ADNI) is a leading source of publicly available de-identified brain imaging, who quickly acted to protect participants privacy. METHODS: An independent expert committee evaluated 11 face-deidentification (de-facing) methods and selected four for formal testing. RESULTS: Effects of de-facing on brain measurements were comparable across methods and sufficiently small to recommend de-facing in ADNI. The committee ultimately recommended mri_reface for advantages in reliability, and for some practical considerations. ADNI leadership approved the committees recommendation, beginning in ADNI4. DISCUSSION: ADNI4 de-faces all applicable brain images before subsequent pre-processing, analyses, and public release. Trained analysts inspect de-faced images to confirm complete face removal and complete non-modification of brain. This paper details the history of the algorithm selection process and extensive validation, then describes the production workflows for de-facing in ADNI. HIGHLIGHTS: ADNI is implementing de-facing of MRI and PET beginning in ADNI4. De-facing alters face imagery in brain images to help protect privacy. Four algorithms were extensively compared for ADNI and mri_reface was chosen. Validation confirms mri_reface is robust and effective for ADNI sequences. Validation confirms mri_reface negligibly affects ADNI brain measurements.

Published
2024

2. A (sub)field guide to quality control in hippocampal subfield segmentation on high‐resolution T2‐weighted MRI

Author

Canada, Kelsey L, Mazloum‐Farzaghi, Negar, Rådman, Gustaf, Adams, Jenna N, Bakker, Arnold, Baumeister, Hannah, Berron, David, Bocchetta, Martina, Carr, Valerie A, Dalton, Marshall A, de Flores, Robin, Keresztes, Attila, La Joie, Renaud, Mueller, Susanne G, Raz, Naftali, Santini, Tales, Shaw, Thomas, Stark, Craig EL, Tran, Tammy T, Wang, Lei, Wisse, Laura EM, Wuestefeld, Anika, Yushkevich, Paul A, Olsen, Rosanna K, Daugherty, Ana M, and Group, the Hippocampal Subfields

Subjects
Biological Psychology, Cognitive and Computational Psychology, Psychology, Neurosciences, Bioengineering, Biomedical Imaging, Hippocampus, Humans, Magnetic Resonance Imaging, Quality Control, Image Processing, Computer-Assisted, Reproducibility of Results, Neuroimaging, Hippocampal Subfields Group, MRI, best practices, hippocampal subfields, quality control, segmentation, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

Inquiries into properties of brain structure and function have progressed due to developments in magnetic resonance imaging (MRI). To sustain progress in investigating and quantifying neuroanatomical details in vivo, the reliability and validity of brain measurements are paramount. Quality control (QC) is a set of procedures for mitigating errors and ensuring the validity and reliability of brain measurements. Despite its importance, there is little guidance on best QC practices and reporting procedures. The study of hippocampal subfields in vivo is a critical case for QC because of their small size, inter-dependent boundary definitions, and common artifacts in the MRI data used for subfield measurements. We addressed this gap by surveying the broader scientific community studying hippocampal subfields on their views and approaches to QC. We received responses from 37 investigators spanning 10 countries, covering different career stages, and studying both healthy and pathological development and aging. In this sample, 81% of researchers considered QC to be very important or important, and 19% viewed it as fairly important. Despite this, only 46% of researchers reported on their QC processes in prior publications. In many instances, lack of reporting appeared due to ambiguous guidance on relevant details and guidance for reporting, rather than absence of QC. Here, we provide recommendations for correcting errors to maximize reliability and minimize bias. We also summarize threats to segmentation accuracy, review common QC methods, and make recommendations for best practices and reporting in publications. Implementing the recommended QC practices will collectively improve inferences to the larger population, as well as have implications for clinical practice and public health.

Published
2024

3. Overview of ADNI MRI

Author

Jack, Clifford R, Arani, Arvin, Borowski, Bret J, Cash, Dave M, Crawford, Karen, Das, Sandhitsu R, DeCarli, Charles, Fletcher, Evan, Fox, Nick C, Gunter, Jeffrey L, Ittyerah, Ranjit, Harvey, Danielle J, Jahanshad, Neda, Maillard, Pauline, Malone, Ian B, Nir, Talia M, Reid, Robert I, Reyes, Denise A, Schwarz, Christopher G, Senjem, Matthew L, Thomas, David L, Thompson, Paul M, Tosun, Duygu, Yushkevich, Paul A, Ward, Chadwick P, Weiner, Michael W, and Initiative, Alzheimer's Disease Neuroimaging

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Aging, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Alzheimer's Disease, Acquired Cognitive Impairment, Brain Disorders, Neurodegenerative, Biomedical Imaging, Clinical Research, Dementia, Bioengineering, Humans, Alzheimer Disease, Magnetic Resonance Imaging, Neuroimaging, Brain, ADNI, Alzheimer's disease imaging, Alzheimer's disease MRI, magnetic resonance imaging, Alzheimer's Disease Neuroimaging Initiative, Geriatrics, Clinical sciences, Biological psychology
Abstract

The magnetic resonance imaging (MRI) Core has been operating since Alzheimer's Disease Neuroimaging Initiative's (ADNI) inception, providing 20 years of data including reliable, multi-platform standardized protocols, carefully curated image data, and quantitative measures provided by expert investigators. The overarching purposes of the MRI Core include: (1) optimizing and standardizing MRI acquisition methods, which have been adopted by many multicenter studies and trials worldwide and (2) providing curated images and numeric summary values from relevant MRI sequences/contrasts to the scientific community. Over time, ADNI MRI has become increasingly complex. To remain technically current, the ADNI MRI protocol has changed substantially over the past two decades. The ADNI 4 protocol contains nine different imaging types (e.g., three dimensional [3D] T1-weighted and fluid-attenuated inversion recovery [FLAIR]). Our view is that the ADNI MRI data are a greatly underutilized resource. The purpose of this paper is to educate the scientific community on ADNI MRI methods and content to promote greater awareness, accessibility, and use. HIGHLIGHTS: The MRI Core provides multi-platform standardized protocols, carefully curated image data, and quantitative analysis by expert groups. The ADNI MRI protocol has undergone major changes over the past two decades to remain technically current. As of April 25, 2024, the following numbers of image series are available: 17,141 3D T1w; 6877 FLAIR; 3140 T2/PD; 6623 GRE; 3237 dMRI; 2846 ASL; 2968 TF-fMRI; and 2861 HighResHippo (see Table 1 for abbreviations). As of April 25, 2024, the following numbers of quantitative analyses are available: FreeSurfer 10,997; BSI 6120; tensor based morphometry (TBM) and TBM-SYN 12,019; WMH 9944; dMRI 1913; ASL 925; TF-fMRI NFQ 2992; and medial temporal subregion volumes 2726 (see Table 4 for abbreviations). ADNI MRI is an underutilized resource that could be more useful to the research community.

Published
2024

4. Surface-based parcellation and vertex-wise analysis of ultra high-resolution ex vivo 7 tesla MRI in Alzheimer's disease and related dementias

Author

Khandelwal, Pulkit, Duong, Michael Tran, Levorse, Lisa, Fuentes, Constanza, Denning, Amanda, Trotman, Winifred, Ittyerah, Ranjit, Bahena, Alejandra, Schuck, Theresa, Gabrielyan, Marianna, Prabhakaran, Karthik, Ohm, Daniel, Mizsei, Gabor, Robinson, John, Munoz, Monica, Detre, John, Lee, Edward, Irwin, David, McMillan, Corey, Tisdall, M. Dylan, Das, Sandhitsu, Wolk, David, and Yushkevich, Paul A.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Magnetic resonance imaging (MRI) is the standard modality to understand human brain structure and function in vivo (antemortem). Decades of research in human neuroimaging has led to the widespread development of methods and tools to provide automated volume-based segmentations and surface-based parcellations which help localize brain functions to specialized anatomical regions. Recently ex vivo (postmortem) imaging of the brain has opened-up avenues to study brain structure at sub-millimeter ultra high-resolution revealing details not possible to observe with in vivo MRI. Unfortunately, there has been limited methodological development in ex vivo MRI primarily due to lack of datasets and limited centers with such imaging resources. Therefore, in this work, we present one-of-its-kind dataset of 82 ex vivo T2w whole brain hemispheres MRI at 0.3 mm isotropic resolution spanning Alzheimer's disease and related dementias. We adapted and developed a fast and easy-to-use automated surface-based pipeline to parcellate, for the first time, ultra high-resolution ex vivo brain tissue at the native subject space resolution using the Desikan-Killiany-Tourville (DKT) brain atlas. This allows us to perform vertex-wise analysis in the template space and thereby link morphometry measures with pathology measurements derived from histology. We will open-source our dataset docker container, Jupyter notebooks for ready-to-use out-of-the-box set of tools and command line options to advance ex vivo MRI clinical brain imaging research on the project webpage.

Published
2024

5. Genetic associations with dementia‐related proteinopathy: Application of item response theory

Author

Katsumata, Yuriko, Fardo, David W, Shade, Lincoln MP, Wu, Xian, Karanth, Shama D, Hohman, Timothy J, Schneider, Julie A, Bennett, David A, Farfel, Jose M, Gauthreaux, Kathryn, Mock, Charles, Kukull, Walter A, Abner, Erin L, Nelson, Peter T, Carrillo, Maria, Reiman, Eric M, Chen, Kewei, Masterman, Donna, Green, Robert C, Ho, Carole, Fleisher, Adam, Saykin, Andrew J, Nho, Kwangsik, Apostolova, Liana G, Risacher, Shannon L, Jackson, Jonathan, Forghanian-Arani, Arvin, Borowski, Bret, Ward, Chad, Schwarz, Christopher, Jack, Clifford R, Jones, David, Gunter, Jeff, Kantarci, Kejal, Senjem, Matthew, Vemuri, Prashanthi, Reid, Robert, Petersen, Ronald, Hsiao, John K, Potter, William, Masliah, Eliezer, Ryan, Laurie, Bernard, Marie, Silverberg, Nina, Kormos, Adrienne, Conti, Cat, Veitch, Dallas, Flenniken, Derek, Sacrey, Diana Truran, Choe, Mark, Ashford, Miriam, Chen, Stephanie Rossi, Faber, Kelley, Nudelman, Kelly, Wilme, Kristi, Foroud, Tatiana M, Trojanowki, John Q, Shaw, Leslie M, Korecka, Magdalena, Figurski, Michal, Khachaturian, Zaven, Barnes, Lisa, Malone, Ian, Fox, Nick C, Beckett, Laurel, Weiner, Michael W, Jagust, William, Landau, Susan, Knaack, Alexander, DeCarli, Charles, Harvey, Danielle, Fletcher, Evan, González, Hector, Jin, Chengshi, Tosun‐Turgut, Duygu, Neuhaus, John, Fockler, Juliet, Nosheny, Rachel, Koeppe, Robert A, Yushkevich, Paul A, Das, Sandhitsu, Mathis, Chet, Toga, Arthur W, Zimmerman, Caileigh, Gessert, Devon, Shcrer, Elizabeth, Miller, Garrett, Coker, Godfrey, Jimenez, Gustavo, Salazar, Jennifer, Pizzola, Jeremy, Crawford, Karen, Hergesheimer, Lindsey, Donohue, Michael, and Rafii, Michael

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical Sciences, Neurosciences, Psychology, Acquired Cognitive Impairment, Neurodegenerative, Brain Disorders, Dementia, Genetics, Prevention, Aging, 2.1 Biological and endogenous factors, Aetiology, Neurological, Humans, alpha-Synuclein, TDP-43 Proteinopathies, Proteostasis Deficiencies, DNA-Binding Proteins, Biological Products, Alzheimer Disease, Membrane Proteins, Nerve Tissue Proteins, Alzheimer's Disease Neuroimaging Initiative, National Alzheimer's Coordinating Center, ARHGEF28, Alzheimer's Coordinating Center, Alzheimer's Disease Sequencing Project, Alzheimer's disease neuropathologic changes, Item response theory, Lewy, RGNEF, Religious Orders Study, Rush Memory and Aging Project, SDHAF1, TMEM68, neuropathology, Geriatrics, Clinical sciences, Biological psychology
Abstract

IntroductionAlthough dementia-related proteinopathy has a strong negative impact on public health, and is highly heritable, understanding of the related genetic architecture is incomplete.MethodsWe applied multidimensional generalized partial credit modeling (GPCM) to test genetic associations with dementia-related proteinopathies. Data were analyzed to identify candidate single nucleotide variants for the following proteinopathies: Aβ, tau, α-synuclein, and TDP-43.ResultsFinal included data comprised 966 participants with neuropathologic and WGS data. Three continuous latent outcomes were constructed, corresponding to TDP-43-, Aβ/Tau-, and α-synuclein-related neuropathology endophenotype scores. This approach helped validate known genotype/phenotype associations: for example, TMEM106B and GRN were risk alleles for TDP-43 pathology; and GBA for α-synuclein/Lewy bodies. Novel suggestive proteinopathy-linked alleles were also discovered, including several (SDHAF1, TMEM68, and ARHGEF28) with colocalization analyses and/or high degrees of biologic credibility.DiscussionA novel methodology using GPCM enabled insights into gene candidates for driving misfolded proteinopathies.HighlightsLatent factor scores for proteinopathies were estimated using a generalized partial credit model. The three latent continuous scores corresponded well with proteinopathy severity. Novel genes associated with proteinopathies were identified. Several genes had high degrees of biologic credibility for dementia risk factors.

Published
2024

6. Surface-Based Parcellation and Vertex-wise Analysis of Ultra High-resolution ex vivo 7 tesla MRI in Alzheimer’s disease and related dementias

Author

Khandelwal, Pulkit, Duong, Michael Tran, Levorse, Lisa, Fuentes, Constanza, Denning, Amanda E., Trotman, Winifred, Ittyerah, Ranjit, Bahena, Alejandra, Schuck, Theresa, Gabrielyan, Marianna, Prabhakaran, Karthik, Ohm, Daniel T., Mizsei, Gabor, Robinson, John, Muñoz, Monica, Detre, John A., Lee, Edward B., Irwin, David J., McMillan, Corey, Tisdall, M. Dylan, Das, Sandhitsu R., Wolk, David A., Yushkevich, Paul A., Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bathula, Deepti R., editor, Benet Nirmala, Anoop, editor, Dvornek, Nicha C., editor, Govindarajan, Sindhuja T., editor, Habes, Mohamad, editor, Kumar, Vinod, editor, Nebli, Ahmed, editor, Wolfers, Thomas, editor, and Xiao, Yiming, editor

Published
2025
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8. Association of quantitative histopathology measurements with antemortem medial temporal lobe cortical thickness in the Alzheimer’s disease continuum

Author

Denning, Amanda E., Ittyerah, Ranjit, Levorse, Lisa M., Sadeghpour, Niyousha, Athalye, Chinmayee, Chung, Eunice, Ravikumar, Sadhana, Dong, Mengjin, Duong, Michael Tran, Li, Yue, Ilesanmi, Ademola, Sreepada, Lasya P., Sabatini, Philip, Lowe, MaKayla, Bahena, Alejandra, Zablah, Jamila, Spencer, Barbara E., Watanabe, Ryohei, Kim, Boram, Sørensen, Maja Højvang, Khandelwal, Pulkit, Brown, Christopher, Hrybouski, Stanislau, Xie, Sharon X., de Flores, Robin, Robinson, John L., Schuck, Theresa, Ohm, Daniel T., Arezoumandan, Sanaz, Porta, Sílvia, Detre, John A., Insausti, Ricardo, Wisse, Laura E. M., Das, Sandhitsu R., Irwin, David J., Lee, Edward B., Wolk, David A., and Yushkevich, Paul A.

Published
2024
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9. Postmortem imaging reveals patterns of medial temporal lobe vulnerability to tau pathology in Alzheimer’s disease

Author

Ravikumar, Sadhana, Denning, Amanda E., Lim, Sydney, Chung, Eunice, Sadeghpour, Niyousha, Ittyerah, Ranjit, Wisse, Laura E. M., Das, Sandhitsu R., Xie, Long, Robinson, John L., Schuck, Theresa, Lee, Edward B., Detre, John A., Tisdall, M. Dylan, Prabhakaran, Karthik, Mizsei, Gabor, de Onzono Martin, Maria Mercedes Iñiguez, Arroyo Jiménez, Maria del Mar, Mũnoz, Monica, Marcos Rabal, Maria del Pilar, Cebada Sánchez, Sandra, Delgado González, José Carlos, de la Rosa Prieto, Carlos, Irwin, David J., Wolk, David A., Insausti, Ricardo, and Yushkevich, Paul A.

Published
2024
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11. Regional Deep Atrophy: a Self-Supervised Learning Method to Automatically Identify Regions Associated With Alzheimer's Disease Progression From Longitudinal MRI

Author

Dong, Mengjin, Xie, Long, Das, Sandhitsu R., Wang, Jiancong, Wisse, Laura E. M., deFlores, Robin, Wolk, David A., and Yushkevich, Paul A.

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Artificial Intelligence
Abstract

Longitudinal assessment of brain atrophy, particularly in the hippocampus, is a well-studied biomarker for neurodegenerative diseases, such as Alzheimer's disease (AD). In clinical trials, estimation of brain progressive rates can be applied to track therapeutic efficacy of disease modifying treatments. However, most state-of-the-art measurements calculate changes directly by segmentation and/or deformable registration of MRI images, and may misreport head motion or MRI artifacts as neurodegeneration, impacting their accuracy. In our previous study, we developed a deep learning method DeepAtrophy that uses a convolutional neural network to quantify differences between longitudinal MRI scan pairs that are associated with time. DeepAtrophy has high accuracy in inferring temporal information from longitudinal MRI scans, such as temporal order or relative inter-scan interval. DeepAtrophy also provides an overall atrophy score that was shown to perform well as a potential biomarker of disease progression and treatment efficacy. However, DeepAtrophy is not interpretable, and it is unclear what changes in the MRI contribute to progression measurements. In this paper, we propose Regional Deep Atrophy (RDA), which combines the temporal inference approach from DeepAtrophy with a deformable registration neural network and attention mechanism that highlights regions in the MRI image where longitudinal changes are contributing to temporal inference. RDA has similar prediction accuracy as DeepAtrophy, but its additional interpretability makes it more acceptable for use in clinical settings, and may lead to more sensitive biomarkers for disease monitoring in clinical trials of early AD., Comment: Submitted to NeuroImage for review

Published
2023

12. Automated deep learning segmentation of high-resolution 7 T postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases

Author

Khandelwal, Pulkit, Duong, Michael Tran, Sadaghiani, Shokufeh, Lim, Sydney, Denning, Amanda, Chung, Eunice, Ravikumar, Sadhana, Arezoumandan, Sanaz, Peterson, Claire, Bedard, Madigan, Capp, Noah, Ittyerah, Ranjit, Migdal, Elyse, Choi, Grace, Kopp, Emily, Loja, Bridget, Hasan, Eusha, Li, Jiacheng, Bahena, Alejandra, Prabhakaran, Karthik, Mizsei, Gabor, Gabrielyan, Marianna, Schuck, Theresa, Trotman, Winifred, Robinson, John, Ohm, Daniel, Lee, Edward B., Trojanowski, John Q., McMillan, Corey, Grossman, Murray, Irwin, David J., Detre, John, Tisdall, M. Dylan, Das, Sandhitsu R., Wisse, Laura E. M., Wolk, David A., and Yushkevich, Paul A.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Postmortem MRI allows brain anatomy to be examined at high resolution and to link pathology measures with morphometric measurements. However, automated segmentation methods for brain mapping in postmortem MRI are not well developed, primarily due to limited availability of labeled datasets, and heterogeneity in scanner hardware and acquisition protocols. In this work, we present a high resolution of 135 postmortem human brain tissue specimens imaged at 0.3 mm$^{3}$ isotropic using a T2w sequence on a 7T whole-body MRI scanner. We developed a deep learning pipeline to segment the cortical mantle by benchmarking the performance of nine deep neural architectures, followed by post-hoc topological correction. We then segment four subcortical structures (caudate, putamen, globus pallidus, and thalamus), white matter hyperintensities, and the normal appearing white matter. We show generalizing capabilities across whole brain hemispheres in different specimens, and also on unseen images acquired at 0.28 mm^3 and 0.16 mm^3 isotropic T2*w FLASH sequence at 7T. We then compute localized cortical thickness and volumetric measurements across key regions, and link them with semi-quantitative neuropathological ratings. Our code, Jupyter notebooks, and the containerized executables are publicly available at: https://pulkit-khandelwal.github.io/exvivo-brain-upenn, Comment: Preprint submitted to NeuroImage Project website: https://pulkit-khandelwal.github.io/exvivo-brain-upenn

Published
2023

13. Improved Segmentation of Deep Sulci in Cortical Gray Matter Using a Deep Learning Framework Incorporating Laplace's Equation

Author

Ravikumar, Sadhana, Ittyerah, Ranjit, Lim, Sydney, Xie, Long, Das, Sandhitsu, Khandelwal, Pulkit, Wisse, Laura E. M., Bedard, Madigan L., Robinson, John L., Schuck, Terry, Grossman, Murray, Trojanowski, John Q., Lee, Edward B., Tisdall, M. Dylan, Prabhakaran, Karthik, Detre, John A., Irwin, David J., Trotman, Winifred, Mizsei, Gabor, Artacho-Pérula, Emilio, Martin, Maria Mercedes Iñiguez de Onzono, Jiménez, Maria del Mar Arroyo, Muñoz, Monica, Romero, Francisco Javier Molina, Rabal, Maria del Pilar Marcos, Cebada-Sánchez, Sandra, González, José Carlos Delgado, de la Rosa-Prieto, Carlos, Parada, Marta Córcoles, Wolk, David A., Insausti, Ricardo, and Yushkevich, Paul A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

When developing tools for automated cortical segmentation, the ability to produce topologically correct segmentations is important in order to compute geometrically valid morphometry measures. In practice, accurate cortical segmentation is challenged by image artifacts and the highly convoluted anatomy of the cortex itself. To address this, we propose a novel deep learning-based cortical segmentation method in which prior knowledge about the geometry of the cortex is incorporated into the network during the training process. We design a loss function which uses the theory of Laplace's equation applied to the cortex to locally penalize unresolved boundaries between tightly folded sulci. Using an ex vivo MRI dataset of human medial temporal lobe specimens, we demonstrate that our approach outperforms baseline segmentation networks, both quantitatively and qualitatively., Comment: Accepted at the 28th biennial international conference on Information Processing in Medical Imaging (IPMI 2023)

Published
2023

14. Contributors

Author

Aja-Fernández, Santiago, primary, Alberola-López, Carlos, additional, Altmann, Andre, additional, Bano, Sophia, additional, Christlein, Vincent, additional, Cong, Shan, additional, Conjeti, Sailesh, additional, Cootes, Tim, additional, Curiale, Ariel H., additional, de Bruijne, Marleen, additional, Demirci, Stefanie, additional, de Vos, Bob D., additional, Duncan, James S., additional, Frangi, Alejandro F., additional, Graham, Simon, additional, Heinrich, Mattias, additional, Išgum, Ivana, additional, Lorenzi, Marco, additional, Lu, Cheng, additional, Madabhushi, Anant, additional, Maier, Andreas, additional, Martin, Melissa, additional, Moreau, Thomas, additional, Mullan, Sean, additional, Oguz, Ipek, additional, Paragios, Nikos, additional, Petersen, Jens, additional, Prince, Jerry L., additional, Rajpoot, Nasir, additional, Ramos-Llordén, Gabriel, additional, Ravikumar, Nishant, additional, Schnabel, Julia, additional, Shen, Li, additional, Shinohara, Russell T., additional, Sokooti, Hessam, additional, Sonka, Milan, additional, Sotiras, Aristeidis, additional, Sporring, Jon, additional, Staib, Lawrence H., additional, Staring, Marius, additional, Stoyanov, Danail, additional, Unberath, Mathias, additional, Vegas Sánchez-Ferrero, Gonzalo, additional, Wassermann, Demian, additional, Xia, Yan, additional, Yushkevich, Paul A., additional, Zakeri, Arezoo, additional, Zhang, Honghai, additional, Zhang, Lichun, additional, and Zhang, Miaomiao, additional

Published
2024
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19. Gray Matter Segmentation in Ultra High Resolution 7 Tesla ex vivo T2w MRI of Human Brain Hemispheres

Author

Khandelwal, Pulkit, Sadaghiani, Shokufeh, Duong, Michael Tran, Ravikumar, Sadhana, Lim, Sydney, Arezoumandan, Sanaz, Peterson, Claire, Chung, Eunice, Bedard, Madigan, Capp, Noah, Ittyerah, Ranjit, Migdal, Elyse, Choi, Grace, Kopp, Emily, Loja, Bridget, Hasan, Eusha, Li, Jiacheng, Prabhakaran, Karthik, Mizsei, Gabor, Gabrielyan, Marianna, Schuck, Theresa, Robinson, John, Ohm, Daniel, Lee, Edward, Trojanowski, John Q., McMillan, Corey, Grossman, Murray, Irwin, David, Tisdall, M. Dylan, Das, Sandhitsu R., Wisse, Laura E. M., Wolk, David A., and Yushkevich, Paul A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Ex vivo MRI of the brain provides remarkable advantages over in vivo MRI for visualizing and characterizing detailed neuroanatomy. However, automated cortical segmentation methods in ex vivo MRI are not well developed, primarily due to limited availability of labeled datasets, and heterogeneity in scanner hardware and acquisition protocols. In this work, we present a high resolution 7 Tesla dataset of 32 ex vivo human brain specimens. We benchmark the cortical mantle segmentation performance of nine neural network architectures, trained and evaluated using manually-segmented 3D patches sampled from specific cortical regions, and show excellent generalizing capabilities across whole brain hemispheres in different specimens, and also on unseen images acquired at different magnetic field strength and imaging sequences. Finally, we provide cortical thickness measurements across key regions in 3D ex vivo human brain images. Our code and processed datasets are publicly available at https://github.com/Pulkit-Khandelwal/picsl-ex-vivo-segmentation., Comment: Ex vivo analysis framework (work in progress 2022 at the University of Pennsylvania)

Published
2021

20. NODEO: A Neural Ordinary Differential Equation Based Optimization Framework for Deformable Image Registration

Author

Wu, Yifan, Jiahao, Tom Z., Wang, Jiancong, Yushkevich, Paul A., Hsieh, M. Ani, and Gee, James C.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Deformable image registration (DIR), aiming to find spatial correspondence between images, is one of the most critical problems in the domain of medical image analysis. In this paper, we present a novel, generic, and accurate diffeomorphic image registration framework that utilizes neural ordinary differential equations (NODEs). We model each voxel as a moving particle and consider the set of all voxels in a 3D image as a high-dimensional dynamical system whose trajectory determines the targeted deformation field. Our method leverages deep neural networks for their expressive power in modeling dynamical systems, and simultaneously optimizes for a dynamical system between the image pairs and the corresponding transformation. Our formulation allows various constraints to be imposed along the transformation to maintain desired regularities. Our experiment results show that our method outperforms the benchmarks under various metrics. Additionally, we demonstrate the feasibility to expand our framework to register multiple image sets using a unified form of transformation,which could possibly serve a wider range of applications., Comment: Accepted by the IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) 2022

Published
2021

21. Dissociation of tau pathology and neuronal hypometabolism within the ATN framework of Alzheimer’s disease

Author

Duong, Michael Tran, Das, Sandhitsu R, Lyu, Xueying, Xie, Long, Richardson, Hayley, Xie, Sharon X, Yushkevich, Paul A, Wolk, David A, and Nasrallah, Ilya M

Subjects
Biochemistry and Cell Biology, Biological Sciences, Neurodegenerative, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Acquired Cognitive Impairment, Dementia, Neurosciences, Aging, Brain Disorders, Alzheimer's Disease, 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, Neurological, Alzheimer Disease, Amyloid beta-Peptides, Biomarkers, Brain, Cognitive Dysfunction, Humans, Magnetic Resonance Imaging, Neuroimaging, Positron-Emission Tomography, tau Proteins, Alzheimer’s Disease Neuroimaging Initiative
Abstract

Alzheimer's disease (AD) is defined by amyloid (A) and tau (T) pathologies, with T better correlated to neurodegeneration (N). However, T and N have complex regional relationships in part related to non-AD factors that influence N. With machine learning, we assessed heterogeneity in 18F-flortaucipir vs. 18F-fluorodeoxyglucose positron emission tomography as markers of T and neuronal hypometabolism (NM) in 289 symptomatic patients from the Alzheimer's Disease Neuroimaging Initiative. We identified six T/NM clusters with differing limbic and cortical patterns. The canonical group was defined as the T/NM pattern with lowest regression residuals. Groups resilient to T had less hypometabolism than expected relative to T and displayed better cognition than the canonical group. Groups susceptible to T had more hypometabolism than expected given T and exhibited worse cognitive decline, with imaging and clinical measures concordant with non-AD copathologies. Together, T/NM mismatch reveals distinct imaging signatures with pathobiological and prognostic implications for AD.

Published
2022

22. Deep Label Fusion: A 3D End-to-End Hybrid Multi-Atlas Segmentation and Deep Learning Pipeline

Author

Xie, Long, Wisse, Laura E. M., Wang, Jiancong, Ravikumar, Sadhana, Glenn, Trevor, Luther, Anica, Lim, Sydney, Wolk, David A., and Yushkevich, Paul A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Deep learning (DL) is the state-of-the-art methodology in various medical image segmentation tasks. However, it requires relatively large amounts of manually labeled training data, which may be infeasible to generate in some applications. In addition, DL methods have relatively poor generalizability to out-of-sample data. Multi-atlas segmentation (MAS), on the other hand, has promising performance using limited amounts of training data and good generalizability. A hybrid method that integrates the high accuracy of DL and good generalizability of MAS is highly desired and could play an important role in segmentation problems where manually labeled data is hard to generate. Most of the prior work focuses on improving single components of MAS using DL rather than directly optimizing the final segmentation accuracy via an end-to-end pipeline. Only one study explored this idea in binary segmentation of 2D images, but it remains unknown whether it generalizes well to multi-class 3D segmentation problems. In this study, we propose a 3D end-to-end hybrid pipeline, named deep label fusion (DLF), that takes advantage of the strengths of MAS and DL. Experimental results demonstrate that DLF yields significant improvements over conventional label fusion methods and U-Net, a direct DL approach, in the context of segmenting medial temporal lobe subregions using 3T T1-weighted and T2-weighted MRI. Further, when applied to an unseen similar dataset acquired in 7T, DLF maintains its superior performance, which demonstrates its good generalizability., Comment: 12 pages paper accepted by the international conference of Information Processing in Medical Imaging (IPMI) 2021

Published
2021

23. Image-to-Image Translation Between Tau Pathology and Neuronal Metabolism PET in Alzheimer Disease with Multi-domain Contrastive Learning

Author

Alzheimer’s Disease Neuroimaging Initiative, Duong, Michael Tran, Das, Sandhitsu R., Khandelwal, Pulkit, Lyu, Xueying, Xie, Long, Yushkevich, Paul A., Wolk, David A., Nasrallah, Ilya M., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Abdulkadir, Ahmed, editor, Bathula, Deepti R., editor, Dvornek, Nicha C., editor, Govindarajan, Sindhuja T., editor, Habes, Mohamad, editor, Kumar, Vinod, editor, Leonardsen, Esten, editor, Wolfers, Thomas, editor, and Xiao, Yiming, editor

Published
2023
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24. Tau‐Atrophy Variability Reveals Phenotypic Heterogeneity in Alzheimer's Disease

Author

Das, Sandhitsu R, Lyu, Xueying, Duong, Michael Tran, Xie, Long, McCollum, Lauren, de Flores, Robin, DiCalogero, Michael, Irwin, David J, Dickerson, Bradford C, Nasrallah, Ilya M, Yushkevich, Paul A, Wolk, David A, and Initiative, Alzheimer's Disease Neuroimaging

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Brain Disorders, Alzheimer's Disease, Dementia, Aging, Neurodegenerative, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Acquired Cognitive Impairment, Neurological, Aged, Aged, 80 and over, Alzheimer Disease, Amyloid beta-Peptides, Atrophy, Cognitive Dysfunction, Humans, Male, Neurofibrillary Tangles, Phenotype, tau Proteins, Alzheimer's Disease Neuroimaging Initiative, Neurology & Neurosurgery, Clinical sciences
Abstract

ObjectiveTau neurofibrillary tangles (T) are the primary driver of downstream neurodegeneration (N) and subsequent cognitive impairment in Alzheimer's disease (AD). However, there is substantial variability in the T-N relationship - manifested in higher or lower atrophy than expected for level of tau in a given brain region. The goal of this study was to determine if region-based quantitation of this variability allows for identification of underlying modulatory factors, including polypathology.MethodsCortical thickness (N) and 18 F-Flortaucipir SUVR (T) were computed in 104 gray matter regions from a cohort of cognitively-impaired, amyloid-positive (A+) individuals. Region-specific residuals from a robust linear fit between SUVR and cortical thickness were computed as a surrogate for T-N mismatch. A summary T-N mismatch metric defined using residuals were correlated with demographic and imaging-based modulatory factors, and to partition the cohort into data-driven subgroups.ResultsThe summary T-N mismatch metric correlated with underlying factors such as age and burden of white matter hyperintensity lesions. Data-driven subgroups based on clustering of residuals appear to represent different biologically relevant phenotypes, with groups showing distinct spatial patterns of higher or lower atrophy than expected.InterpretationThese data support the notion that a measure of deviation from a normative relationship between tau burden and neurodegeneration across brain regions in individuals on the AD continuum captures variability due to multiple underlying factors, and can reveal phenotypes, which if validated, may help identify possible contributors to neurodegeneration in addition to tau, which may ultimately be useful for cohort selection in clinical trials. ANN NEUROL 2021;90:751-762.

Published
2021

26. DeepAtrophy: Teaching a Neural Network to Differentiate Progressive Changes from Noise on Longitudinal MRI in Alzheimer's Disease

Author

Dong, Mengjin, Xie, Long, Das, Sandhitsu R., Wang, Jiancong, Wisse, Laura E. M., deFlores, Robin, Wolk, David A., and Yushkevich, Paul

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Neurons and Cognition
Abstract

Volume change measures derived from longitudinal MRI (e.g. hippocampal atrophy) are a well-studied biomarker of disease progression in Alzheimer's Disease (AD) and are used in clinical trials to track the therapeutic efficacy of disease-modifying treatments. However, longitudinal MRI change measures can be confounded by non-biological factors, such as different degrees of head motion and susceptibility artifact between pairs of MRI scans. We hypothesize that deep learning methods applied directly to pairs of longitudinal MRI scans can be trained to differentiate between biological changes and non-biological factors better than conventional approaches based on deformable image registration. To achieve this, we make a simplifying assumption that biological factors are associated with time (i.e. the hippocampus shrinks overtime in the aging population) whereas non-biological factors are independent of time. We then formulate deep learning networks to infer the temporal order of same-subject MRI scans input to the network in arbitrary order; as well as to infer ratios between interscan intervals for two pairs of same-subject MRI scans. In the test dataset, these networks perform better in tasks of temporal ordering (89.3%) and interscan interval inference (86.1%) than a state-of-the-art deformation-based morphometry method ALOHA (76.6% and 76.1% respectively) (Das et al., 2012). Furthermore, we derive a disease progression score from the network that is able to detect a group difference between 58 preclinical AD and 75 beta-amyloid-negative cognitively normal individuals within one year, compared to two years for ALOHA. This suggests that deep learning can be trained to differentiate MRI changes due to biological factors (tissue loss) from changes due to non-biological factors, leading to novel biomarkers that are more sensitive to longitudinal changes at the earliest stages of AD., Comment: Submitted to a journal, IF ~ 6

Published
2020

27. Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical Imaging

Author

Khandelwal, Pulkit and Yushkevich, Paul

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Deep learning models perform best when tested on target (test) data domains whose distribution is similar to the set of source (train) domains. However, model generalization can be hindered when there is significant difference in the underlying statistics between the target and source domains. In this work, we adapt a domain generalization method based on a model-agnostic meta-learning framework to biomedical imaging. The method learns a domain-agnostic feature representation to improve generalization of models to the unseen test distribution. The method can be used for any imaging task, as it does not depend on the underlying model architecture. We validate the approach through a computed tomography (CT) vertebrae segmentation task across healthy and pathological cases on three datasets. Next, we employ few-shot learning, i.e. training the generalized model using very few examples from the unseen domain, to quickly adapt the model to new unseen data distribution. Our results suggest that the method could help generalize models across different medical centers, image acquisition protocols, anatomies, different regions in a given scan, healthy and diseased populations across varied imaging modalities., Comment: Medical Image Computing and Computer Assisted Interventions (MICCAI) 2020 to be presented at DART 2020. Supplementary material and link to code included

Published
2020

29. Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data

Author

Kaddurah-Daouk, Rima, Kueider-Paisley, Alexandra, Doraiswamy, P. Murali, Blach, Colette, Moseley, Arthur, Thompson, Will, St John-Williams, Lisa, Mahmoudiandehkhordi, Siamak, Tenenbaum, Jessica, Welsh-Balmer, Kathleen, Plassman, Brenda, Saykin, Andrew J., Nho, Kwangsik, Risacher, Shannon L., Kastenmüller, Gabi, Arnold, Matthias, Han, Xianlin, Baillie, Rebecca, Knight, Rob, Dorrestein, Pieter, Brewer, James, Mayer, Emeran, Labus, Jennifer, Baldi, Pierre, Gupta, Arpana, Fiehn, Oliver, Barupal, Dinesh, Meikle, Peter, Mazmanian, Sarkis, Rader, Dan, Kling, Mitchel, Shaw, Leslie, Trojanowski, John, van Duijin, Cornelia, Nevado-Holgado, Alejo, Bennett, David, Krishnan, Ranga, Keshavarzian, Ali, Vogt, Robin, Ikram, Arfan, Hankemeier, Thomas, Thiele, Ines, Price, Nathan, Funk, Cory, Baloni, Priyanka, Jia, Wei, Wishart, David, Brinton, Roberta, Chang, Rui, Farrer, Lindsay, Au, Rhoda, Qiu, Wendy, Würtz, Peter, Koal, Therese, Mangravite, Lara, Krumsiek, Jan, Suhre, Karsten, Newman, John, Moreno, Herman, Foroud, Tatania, Sacks, Frank, Jansson, Janet, Weiner, Michael W., Aisen, Paul, Petersen, Ronald, Jack, Clifford R., Jr., Jagust, William, Trojanowki, John Q., Toga, Arthur W., Beckett, Laurel, Green, Robert C., Morris, John C., Perrin, Richard J., Shaw, Leslie M., Khachaturian, Zaven, Carrillo, Maria, Potter, William, Barnes, Lisa, Bernard, Marie, Gonzalez, Hector, Ho, Carole, Hsiao, John K., Jackson, Jonathan, Masliah, Eliezer, Masterman, Donna, Okonkwo, Ozioma, Perrin, Richard, Ryan, Laurie, Silverberg, Nina, Fleisher, Adam, Sacrey, Diana Truran, Fockler, Juliet, Conti, Cat, Veitch, Dallas, Neuhaus, John, Jin, Chengshi, Nosheny, Rachel, Ashford, Miriam, Flenniken, Derek, Kormos, Adrienne, Montine, Tom, Rafii, Michael, Raman, Rema, Jimenez, Gustavo, Donohue, Michael, Gessert, Devon, Salazar, Jennifer, Zimmerman, Caileigh, Cabrera, Yuliana, Walter, Sarah, Miller, Garrett, Coker, Godfrey, Clanton, Taylor, Hergesheimer, Lindsey, Smith, Stephanie, Adegoke, Olusegun, Mahboubi, Payam, Moore, Shelley, Pizzola, Jeremy, Shaffer, Elizabeth, Sloan, Brittany, Harvey, Danielle, Forghanian-Arani, Arvin, Borowski, Bret, Ward, Chad, Schwarz, Christopher, Jones, David, Gunter, Jeff, Kantarci, Kejal, Senjem, Matthew, Vemuri, Prashanthi, Reid, Robert, Fox, Nick C., Malone, Ian, Thompson, Paul, Thomopoulos, Sophia I., Nir, Talia M., Jahanshad, Neda, DeCarli, Charles, Knaack, Alexander, Fletcher, Evan, Tosun-Turgut, Duygu, Chen, Stephanie Rossi, Choe, Mark, Crawford, Karen, Yushkevich, Paul A., Das, Sandhitsu, Koeppe, Robert A., Reiman, Eric M., Chen, Kewei, Mathis, Chet, Landau, Susan, Cairns, Nigel J., Householder, Erin, Franklin, Erin, Bernhardt, Haley, Taylor-Reinwald, Lisa, Korecka, Magdalena, Figurski, Michal, Neu, Scott, Apostolova, Liana G., Shen, Li, Foroud, Tatiana M., Nudelman, Kelly, Faber, Kelley, Wilmes, Kristi, Thal, Leon, Silbert, Lisa C., Lind, Betty, Crissey, Rachel, Kaye, Jeffrey A., Carter, Raina, Dolen, Sara, Quinn, Joseph, Schneider, Lon S., Pawluczyk, Sonia, Becerra, Mauricio, Teodoro, Liberty, Dagerman, Karen, Spann, Bryan M., Vanderswag, Helen, Ziolkowski, Jaimie, Heidebrink, Judith L., Zbizek-Nulph, Lisa, Lord, Joanne L., Mason, Sara S., Albers, Colleen S., Knopman, David, Johnson, Kris, Villanueva-Meyer, Javier, Pavlik, Valory, Pacini, Nathaniel, Lamb, Ashley, Kass, Joseph S., Doody, Rachelle S., Shibley, Victoria, Chowdhury, Munir, Rountree, Susan, Dang, Mimi, Stern, Yaakov, Honig, Lawrence S., Mintz, Akiva, Ances, Beau, Winkfield, David, Carroll, Maria, Stobbs-Cucchi, Georgia, Oliver, Angela, Creech, Mary L., Mintun, Mark A., Schneider, Stacy, Geldmacher, David, Love, Marissa Natelson, Griffith, Randall, Clark, David, Brockington, John, Marson, Daniel, Grossman, Hillel, Goldstein, Martin A., Greenberg, Jonathan, Mitsis, Effie, Shah, Raj C., Lamar, Melissa, Samuels, Patricia, Duara, Ranjan, Greig-Custo, Maria T., Rodriguez, Rosemarie, Albert, Marilyn, Onyike, Chiadi, Farrington, Leonie, Rudow, Scott, Brichko, Rottislav, Kielb, Stephanie, Smith, Amanda, Raj, Balebail Ashok, Fargher, Kristin, Sadowski, Martin, Wisniewski, Thomas, Shulman, Melanie, Faustin, Arline, Rao, Julia, Castro, Karen M., Ulysse, Anaztasia, Chen, Shannon, Sheikh, Mohammed O., Singleton-Garvin, Jamika, Petrella, JeffreyR., James, Olga, Wong, Terence Z., Borges-Neto, Salvador, Karlawish, Jason H., Wolk, David A., Vaishnavi, Sanjeev, Clark, Christopher M., Arnold, Steven E., Smith, Charles D., Jicha, Gregory A., Raslau, Flavius D., Lopez, Oscar L., Oakley, MaryAnn, Simpson, Donna M., Porsteinsson, Anton P., Martin, Kim, Kowalski, Nancy, Keltz, Melanie, Goldstein, Bonnie S., Makino, Kelly M., Ismail, M. Saleem, Brand, Connie, Thai, Gaby, Pierce, Aimee, Yanez, Beatriz, Sosa, Elizabeth, Witbracht, Megan, Kelley, Brendan, Nguyen, Trung, Womack, Kyle, Mathews, Dana, Quiceno, Mary, Levey, Allan I., Lah, James J., Hajjar, Ihab, Cellar, Janet S., Burns, Jeffrey M., Swerdlow, Russell H., Brooks, William M., Silverman, Daniel H.S., Kremen, Sarah, Apostolova, Liana, Tingus, Kathleen, Lu, Po H., Bartzokis, George, Woo, Ellen, Teng, Edmond, Graff-Radford, Neill R., Parfitt, Francine, Poki-Walker, Kim, Farlow, Martin R., Hake, Ann Marie, Matthews, Brandy R., Brosch, Jared R., Herring, Scott, van, Christopher H., Mecca, Adam P., Good, Susan P., MacAvoy, Martha G., Carson, Richard E., Varma, Pradeep, Chertkow, Howard, Vaitekunis, Susan, Hosein, Chris, Black, Sandra, Stefanovic, Bojana, Heyn, Chris (Chinthaka), Robin Hsiung, Ging-Yuek, Kim, Ellen, Mudge, Benita, Sossi, Vesna, Feldman, Howard, Assaly, Michele, Finger, Elizabeth, Pasternak, Stephen, Rachinsky, Irina, Kertesz, Andrew, Drost, Dick, Rogers, John, Grant, Ian, Muse, Brittanie, Rogalski, Emily, Robson, Jordan, Mesulam, M.-Marsel, Kerwin, Diana, Wu, Chuang-Kuo, Johnson, Nancy, Lipowski, Kristine, Weintraub, Sandra, Bonakdarpour, Borna, Pomara, Nunzio, Hernando, Raymundo, Sarrael, Antero, Rosen, Howard J., Miller, Bruce L., Perry, David, Turner, Raymond Scott, Johnson, Kathleen, Reynolds, Brigid, MCCann, Kelly, Poe, Jessica, Sperling, Reisa A., Johnson, Keith A., Marshall, Gad A., Yesavage, Jerome, Taylor, Joy L., Chao, Steven, Coleman, Jaila, White, Jessica D., Lane, Barton, Rosen, Allyson, Tinklenberg, Jared, Belden, Christine M., Atri, Alireza, Clark, Kelly A., Zamrini, Edward, Sabbagh, Marwan, Killiany, Ronald, Stern, Robert, Mez, Jesse, Kowall, Neil, Budson, Andrew E., Obisesan, Thomas O., Ntekim, Oyonumo E., Wolday, Saba, Khan, Javed I., Nwulia, Evaristus, Nadarajah, Sheeba, Lerner, Alan, Ogrocki, Paula, Tatsuoka, Curtis, Fatica, Parianne, Maillard, Pauline, Olichney, John, Carmichael, Owen, Bates, Vernice, Capote, Horacio, Rainka, Michelle, Borrie, Michael, Lee, T.-Y., Bartha, Dr Rob, Johnson, Sterling, Asthana, Sanjay, Carlsson, Cynthia M., Perrin, Allison, Burke, Anna, Scharre, Douglas W., Kataki, Maria, Tarawneh, Rawan, Hart, David, Zimmerman, Earl A., Celmins, Dzintra, Miller, Delwyn D., BolesPonto, Laura L., Smith, Karen Ekstam, Koleva, Hristina, Shim, Hyungsub, Nam, Ki Won, Schultz, Susan K., Williamson, Jeff D., Craft, Suzanne, Cleveland, Jo, Yang, Mia, Sink, Kaycee M., Ott, Brian R., Drake, Jonathan, Tremont, Geoffrey, Daiello, Lori A., Drake, Jonathan D., Ritter, Aaron, Bernick, Charles, Munic, Donna, O'Connelll, Abigail, Mintzer, Jacobo, Wiliams, Arthur, Masdeu, Joseph, Shi, Jiong, Garcia, Angelica, Newhouse, Paul, Potkin, Steven, Salloway, Stephen, Malloy, Paul, Correia, Stephen, Kittur, Smita, Pearlson, Godfrey D., Blank, Karen, Anderson, Karen, Flashman, Laura A., Seltzer, Marc, Hynes, Mary L., Santulli, Robert B., Relkin, Norman, Chiang, Gloria, Lee, Athena, Lin, Michael, Ravdin, Lisa, Petersen, Ron, Neylan, Thomas, Grafman, Jordan, Danowski, Sarah, Nguyen-Barrera, Catherine, Hayes, Jacqueline, Finley, Shannon, Bernstein, Matthew, Senjem, Matt, Foster, Norm, Kim, Sungeun, Sood, Ajay, Blanchard, Kimberly S., Fleischman, Debra, Arfanakis, Konstantinos, Varon, Daniel, Greig, Maria T., Petrella, Jeffrey R., Goldstein, Bonnie, Martin, Kimberly S., Reist, Christopher, Sadowsky, Carl, Martinez, Walter, Villena, Teresa, Rosen, Howard, Marshall, Gad, Peskind, Elaine R., Petrie, Eric C., Li, Gail, Mackin, Scott, Jimenez-Maggiora, Gustavo, Drake, Erin, Donohue, Mike, Nelson, Craig, Bickford, David, Butters, Meryl, Zmuda, Michelle, Reyes, Denise, Faber, Kelley M., Nudelman, Kelly N., Au, Yiu Ho, Scherer, Kelly, Catalinotto, Daniel, Stark, Samuel, Ong, Elise, Fernandez, Dariella, Jo, Taeho, Kim, Junpyo, Bice, Paula, Huynh, Kevin, Wang, Tingting, Meikle, Peter J., and Giles, Corey

Published
2023
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30. Hippocampal subfield volumetry from structural isotropic 1 mm3 MRI scans: A note of caution

Author

Wisse, Laura EM, Chételat, Gaël, Daugherty, Ana M, de Flores, Robin, la Joie, Renaud, Mueller, Susanne G, Stark, Craig EL, Wang, Lei, Yushkevich, Paul A, Berron, David, Raz, Naftali, Bakker, Arnold, Olsen, Rosanna K, and Carr, Valerie A

Subjects
Biomedical and Clinical Sciences, Neurosciences, Biomedical Imaging, Bioengineering, Aging, Hippocampus, Humans, Magnetic Resonance Imaging, Organ Size, mm(3), FreeSurfer, hippocampal subfields, MRI, volumetry, 1 mm3, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

Spurred by availability of automatic segmentation software, in vivo MRI investigations of human hippocampal subfield volumes have proliferated in the recent years. However, a majority of these studies apply automatic segmentation to MRI scans with approximately 1 × 1 × 1 mm3 resolution, a resolution at which the internal structure of the hippocampus can rarely be visualized. Many of these studies have reported contradictory and often neurobiologically surprising results pertaining to the involvement of hippocampal subfields in normal brain function, aging, and disease. In this commentary, we first outline our concerns regarding the utility and validity of subfield segmentation on 1 × 1 × 1 mm3 MRI for volumetric studies, regardless of how images are segmented (i.e., manually or automatically). This image resolution is generally insufficient for visualizing the internal structure of the hippocampus, particularly the stratum radiatum lacunosum moleculare, which is crucial for valid and reliable subfield segmentation. Second, we discuss the fact that automatic methods that are employed most frequently to obtain hippocampal subfield volumes from 1 × 1 × 1 mm3 MRI have not been validated against manual segmentation on such images. For these reasons, we caution against using volumetric measurements of hippocampal subfields obtained from 1 × 1 × 1 mm3 images.

Published
2021

31. Barnes-Hut Approximation for Point SetGeodesic Shooting

Author

Wang, Jiancong, Xie, Long, Yushkevich, Paul, and Gee, James

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Geodesic shooting has been successfully applied to diffeo-morphic registration of point sets. Exact computation of the geodesicshooting between point sets, however, requiresO(N2) calculations each time step on the number of points in the point set. We proposean approximation approach based on the Barnes-Hut algorithm to speedup point set geodesic shooting. This approximation can reduce the al-gorithm complexity toO(N b+N logN). The evaluation of the proposedmethod in both simulated images and the medial temporal lobe thick-ness analysis demonstrates a comparable accuracy to the exact point set geodesic shooting while offering up to 3-fold speed up. This improvementopens up a range of clinical research studies and practical problems towhich the method can be effectively applied.

Published
2019

32. Accurate and Robust Alignment of Variable-stained Histologic Images Using a General-purpose Greedy Diffeomorphic Registration Tool

Author

Venet, Ludovic, Pati, Sarthak, Yushkevich, Paul, and Bakas, Spyridon

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Variously stained histology slices are routinely used by pathologists to assess extracted tissue samples from various anatomical sites and determine the presence or extent of a disease. Evaluation of sequential slides is expected to enable a better understanding of the spatial arrangement and growth patterns of cells and vessels. In this paper we present a practical two-step approach based on diffeomorphic registration to align digitized sequential histopathology stained slides to each other, starting with an initial affine step followed by the estimation of a detailed deformation field.

Published
2019

33. Diffeomorphic Medial Modeling

Author

Yushkevich, Paul A., Aly, Ahmed, Wang, Jiancong, Xie, Long, Gorman, Robert C., Younes, Laurent, and Pouch, Alison

Subjects
Computer Science - Graphics, Computer Science - Computational Geometry
Abstract

Deformable shape modeling approaches that describe objects in terms of their medial axis geometry (e.g., m-reps [Pizer et al., 2003]) yield rich geometrical features that can be useful for analyzing the shape of sheet-like biological structures, such as the myocardium. We present a novel shape analysis approach that combines the benefits of medial shape modeling and diffeomorphometry. Our algorithm is formulated as a problem of matching shapes using diffeomorphic flows under constraints that approximately preserve medial axis geometry during deformation. As the result, correspondence between the medial axes of similar shapes is maintained. The approach is evaluated in the context of modeling the shape of the left ventricular wall from 3D echocardiography images., Comment: Accepted to the 26th International Conference on Information Processing in Medical Imaging (IPMI 2019)

Published
2019

35. Improved Segmentation of Deep Sulci in Cortical Gray Matter Using a Deep Learning Framework Incorporating Laplace’s Equation

Author

Ravikumar, Sadhana, primary, Ittyerah, Ranjit, additional, Lim, Sydney, additional, Xie, Long, additional, Das, Sandhitsu, additional, Khandelwal, Pulkit, additional, Wisse, Laura E. M., additional, Bedard, Madigan L., additional, Robinson, John L., additional, Schuck, Terry, additional, Grossman, Murray, additional, Trojanowski, John Q., additional, Lee, Edward B., additional, Tisdall, M. Dylan, additional, Prabhakaran, Karthik, additional, Detre, John A., additional, Irwin, David J., additional, Trotman, Winifred, additional, Mizsei, Gabor, additional, Artacho-Pérula, Emilio, additional, de Onzono Martin, Maria Mercedes Iñiguez, additional, del Mar Arroyo Jiménez, Maria, additional, Muñoz, Monica, additional, Romero, Francisco Javier Molina, additional, del Pilar Marcos Rabal, Maria, additional, Cebada-Sánchez, Sandra, additional, González, José Carlos Delgado, additional, de la Rosa-Prieto, Carlos, additional, Parada, Marta Córcoles, additional, Wolk, David A., additional, Insausti, Ricardo, additional, and Yushkevich, Paul A., additional

Published
2023
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38. Progress update from the hippocampal subfields group

Author

Olsen, Rosanna K, Carr, Valerie A, Daugherty, Ana M, La Joie, Renaud, Amaral, Robert SC, Amunts, Katrin, Augustinack, Jean C, Bakker, Arnold, Bender, Andrew R, Berron, David, Boccardi, Marina, Bocchetta, Martina, Burggren, Alison C, Chakravarty, M Mallar, Chételat, Gaël, de Flores, Robin, DeKraker, Jordan, Ding, Song‐Lin, Geerlings, Mirjam I, Huang, Yushan, Insausti, Ricardo, Johnson, Elliott G, Kanel, Prabesh, Kedo, Olga, Kennedy, Kristen M, Keresztes, Attila, Lee, Joshua K, Lindenberger, Ulman, Mueller, Susanne G, Mulligan, Elizabeth M, Ofen, Noa, Palombo, Daniela J, Pasquini, Lorenzo, Pluta, John, Raz, Naftali, Rodrigue, Karen M, Schlichting, Margaret L, Shing, Yee Lee, Stark, Craig EL, Steve, Trevor A, Suthana, Nanthia A, Wang, Lei, Werkle‐Bergner, Markus, Yushkevich, Paul A, Yu, Qijing, Wisse, Laura EM, and Group, Hippocampal Subfields

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Neurosciences, Psychology, Hippocampus, Volumetry, Human, Neuroimaging, Structural imaging, Neuroanatomy, Histology, Cytoarchitecture, ex vivo, Hippocampal Subfields Group, ex vivo, Genetics, Biological psychology
Abstract

IntroductionHeterogeneity of segmentation protocols for medial temporal lobe regions and hippocampal subfields on in vivo magnetic resonance imaging hinders the ability to integrate findings across studies. We aim to develop a harmonized protocol based on expert consensus and histological evidence.MethodsOur international working group, funded by the EU Joint Programme-Neurodegenerative Disease Research (JPND), is working toward the production of a reliable, validated, harmonized protocol for segmentation of medial temporal lobe regions. The working group uses a novel postmortem data set and online consensus procedures to ensure validity and facilitate adoption.ResultsThis progress report describes the initial results and milestones that we have achieved to date, including the development of a draft protocol and results from the initial reliability tests and consensus procedures.DiscussionA harmonized protocol will enable the standardization of segmentation methods across laboratories interested in medial temporal lobe research worldwide.

Published
2019

40. Morphometry of medial temporal lobe subregions using high‐resolution T2‐weighted MRI in ADNI3: Why, how, and what's next?

Author

Yushkevich, Paul A., Ittyerah, Ranjit, Li, Yue, Denning, Amanda E., Sadeghpour, Niyousha, Lim, Sydney, McGrew, Emily, Xie, Long, DeFlores, Robin, Brown, Christopher A., Wisse, Laura E. M., Wolk, David A., and Das, Sandhitsu R.

Abstract

This paper for the 20th anniversary of the Alzheimer's Disease Neuroimaging Initiative (ADNI) provides an overview of magnetic resonance imaging (MRI) of medial temporal lobe (MTL) subregions in ADNI using a dedicated high‐resolution T2‐weighted sequence. A review of the work that supported the inclusion of this imaging modality into ADNI Phase 3 is followed by a brief description of the ADNI MTL imaging and analysis protocols and a summary of studies that have used these data. This review is supplemented by a new study that uses novel surface‐based tools to characterize MTL neurodegeneration across biomarker‐defined AD stages. This analysis reveals a pattern of spreading cortical thinning associated with increasing levels of tau pathology in the presence of elevated amyloid beta, with apparent epicenters in the transentorhinal region and inferior hippocampal subfields. The paper concludes with an outlook for high‐resolution imaging of the MTL in ADNI Phase 4. Highlights: As of Phase 3, the Alzheimer's Disease Neuroimaging Initiative (ADNI) magnetic resonance imaging (MRI) protocol includes a high‐resolution T2‐weighted MRI scan optimized for imaging hippocampal subfields and medial temporal lobe (MTL) subregions.These scans are processed by the ADNI core to obtain automatic segmentations of MTL subregions and to derive morphologic measurements.More detailed granular examination of MTL neurodegeneration in response to disease progression is achieved by applying surface‐based modeling techniques.Surface‐based analysis of gray matter loss in MTL subregions reveals increasing and spatially expanding patterns of neurodegeneration with advancing stages of Alzheimer's disease (AD), as defined based on amyloid and tau positron emission tomography biomarkers in accordance with recently proposed criteria.These patterns closely align with post mortem literature on spread of pathological tau in AD, supporting the role of tau pathology in the presence of elevated levels of amyloid beta as the driver of neurodegeneration. [ABSTRACT FROM AUTHOR]

Published
2024
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41. Unfolding the Medial Temporal Lobe Cortex to Characterize Neurodegeneration Due to Alzheimer’s Disease Pathology Using Ex vivo Imaging

Author

Ravikumar, Sadhana, Wisse, Laura, Lim, Sydney, Irwin, David, Ittyerah, Ranjit, Xie, Long, Das, Sandhitsu R., Lee, Edward, Tisdall, M. Dylan, Prabhakaran, Karthik, Detre, John, Mizsei, Gabor, Trojanowski, John Q., Robinson, John, Schuck, Theresa, Grossman, Murray, Artacho-Pérula, Emilio, de Onzoño Martin, Maria Mercedes Iñiguez, del Mar Arroyo Jiménez, María, Muñoz, Monica, Romero, Francisco Javier Molina, del Pilar Marcos Rabal, Maria, Sánchez, Sandra Cebada, González, José Carlos Delgado, de la Rosa Prieto, Carlos, Parada, Marta Córcoles, Wolk, David, Insausti, Ricardo, Yushkevich, Paul, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Abdulkadir, Ahmed, editor, Kia, Seyed Mostafa, editor, Habes, Mohamad, editor, Kumar, Vinod, editor, Rondina, Jane Maryam, editor, Tax, Chantal, editor, and Wolfers, Thomas, editor

Published
2021
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43. Ex vivo MRI and histopathology detect novel iron-rich cortical inflammation in frontotemporal lobar degeneration with tau versus TDP-43 pathology

Author

Tisdall, M. Dylan, Ohm, Daniel T., Lobrovich, Rebecca, Das, Sandhitsu R., Mizsei, Gabor, Prabhakaran, Karthik, Ittyerah, Ranjit, Lim, Sydney, McMillan, Corey T., Wolk, David A., Gee, James, Trojanowski, John Q., Lee, Edward B., Detre, John A., Yushkevich, Paul, Grossman, Murray, and Irwin, David J.

Published
2022
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45. Systematic comparison of different techniques to measure hippocampal subfield volumes in ADNI2

Author

Mueller, Susanne G, Yushkevich, Paul A, Das, Sandhitsu, Wang, Lei, Van Leemput, Koen, Iglesias, Juan Eugenio, Alpert, Kate, Mezher, Adam, Ng, Peter, Paz, Katrina, Weiner, Michael W, and Initiative, for the Alzheimer's Disease Neuroimaging

Subjects
Biomedical and Clinical Sciences, Neurosciences, Aging, Neurodegenerative, Brain Disorders, Acquired Cognitive Impairment, Aged, Aged, 80 and over, Alzheimer Disease, Atrophy, Cognition Disorders, Female, Hippocampus, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Positron-Emission Tomography, Reproducibility of Results, Retrospective Studies, Alzheimer's Disease Neuroimaging Initiative, Biological psychology, Clinical and health psychology
Abstract

ObjectiveSubfield-specific measurements provide superior information in the early stages of neurodegenerative diseases compared to global hippocampal measurements. The overall goal was to systematically compare the performance of five representative manual and automated T1 and T2 based subfield labeling techniques in a sub-set of the ADNI2 population.MethodsThe high resolution T2 weighted hippocampal images (T2-HighRes) and the corresponding T1 images from 106 ADNI2 subjects (41 controls, 57 MCI, 8 AD) were processed as follows. A. T1-based: 1. Freesurfer + Large-Diffeomorphic-Metric-Mapping in combination with shape analysis. 2. FreeSurfer 5.1 subfields using in-vivo atlas. B. T2-HighRes: 1. Model-based subfield segmentation using ex-vivo atlas (FreeSurfer 6.0). 2. T2-based automated multi-atlas segmentation combined with similarity-weighted voting (ASHS). 3. Manual subfield parcellation. Multiple regression analyses were used to calculate effect sizes (ES) for group, amyloid positivity in controls, and associations with cognitive/memory performance for each approach.ResultsSubfield volumetry was better than whole hippocampal volumetry for the detection of the mild atrophy differences between controls and MCI (ES: 0.27 vs 0.11). T2-HighRes approaches outperformed T1 approaches for the detection of early stage atrophy (ES: 0.27 vs.0.10), amyloid positivity (ES: 0.11 vs 0.04), and cognitive associations (ES: 0.22 vs 0.19).ConclusionsT2-HighRes subfield approaches outperformed whole hippocampus and T1 subfield approaches. None of the different T2-HghRes methods tested had a clear advantage over the other methods. Each has strengths and weaknesses that need to be taken into account when deciding which one to use to get the best results from subfield volumetry.

Published
2018

46. Automatic segmentation of medial temporal lobe subregions in multi-scanner, multi-modality MRI of variable quality

Author

Li, Yue, primary, Xie, Long, additional, Khandelwal, Pulkit, additional, Wisse, Laura E. M., additional, Brown, Christopher A., additional, Prabhakaran, Karthik, additional, Tisdall, M. Dylan, additional, Mechanic-Hamilton, Dawn, additional, Detre, John A., additional, Das, Sandhitsu R., additional, Wolk, David A., additional, and Yushkevich, Paul A., additional

Published
2024
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47. Medial temporal lobe atrophy patterns in early- versus late-onset amnestic Alzheimer's disease

Author

Wuestefeld, Anika, primary, Pichet Binette, Alexa, additional, van Westen, Danielle, additional, Strandberg, Olof, additional, Stomrud, Erik, additional, Mattsson-Carlgren, Niklas, additional, Janelidze, Shorena, additional, Smith, Ruben, additional, Palmqvist, Sebastian, additional, Baumeister, Hannah, additional, Berron, David, additional, Yushkevich, Paul A., additional, Hansson, Oskar, additional, Spotorno, Nicola, additional, and Wisse, Laura EM, additional

Published
2024
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49. Open-source graphical user interface for the creation of synthetic skeletons for medical image analysis

Author

Herz, Christian, primary, Vergnet, Nicolas, additional, Tian, Sijie, additional, Aly, Abdullah H., additional, Jolley, Matthew A., additional, Tran, Nathanael, additional, Arenas, Gabriel, additional, Lasso, Andras, additional, Schwartz, Nadav, additional, O’Neill, Kathleen E., additional, Yushkevich, Paul A., additional, and Pouch, Alison M., additional

Published
2024
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50. The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview

Author

Pati, Sarthak, Singh, Ashish, Rathore, Saima, Gastounioti, Aimilia, Bergman, Mark, Ngo, Phuc, Ha, Sung Min, Bounias, Dimitrios, Minock, James, Murphy, Grayson, Li, Hongming, Bhattarai, Amit, Wolf, Adam, Sridaran, Patmaa, Kalarot, Ratheesh, Akbari, Hamed, Sotiras, Aristeidis, Thakur, Siddhesh P., Verma, Ragini, Shinohara, Russell T., Yushkevich, Paul, Fan, Yong, Kontos, Despina, Davatzikos, Christos, Bakas, Spyridon, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2020
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