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511 results on '"Landman, Bennett"'

4. Predicting age from white matter diffusivity with residual learning

Author

Colliot, Olivier, Mitra, Jhimli, Gao, Chenyu, Kim, Michael E., Lee, Ho Hin, Yang, Qi, Mohd Khairi, Nazirah, Kanakaraj, Praitayini, Newlin, Nancy R., Archer, Derek B., Jefferson, Angela L., Taylor, Warren D., Boyd, Brian D., Beason-Held, Lori L., Resnick, Susan M., Huo, Yuankai, Van Schaik, Katherine D., Schilling, Kurt G., Moyer, Daniel, Išgum, Ivana, and Landman, Bennett A.

Published
2024
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7. Evaluation of mean shift, ComBat, and CycleGAN for harmonizing brain connectivity matrices across sites

Author

Colliot, Olivier, Mitra, Jhimli, Xu, Hanliang, Newlin, Nancy R., Kim, Michael E., Gao, Chenyu, Kanakaraj, Praitayini, Krishnan, Aravind R., Remedios, Lucas W., Mohd Khairi, Nazirah, Pechman, Kimberly, Archer, Derek, Hohman, Timothy J., Jefferson, Angela L., Isgum, Ivana, Huo, Yuankai, Moyer, Daniel, Schilling, Kurt G., and Landman, Bennett A.

Published
2024
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11. Nucleus subtype classification using inter-modality learning

Author

Tomaszewski, John E., Ward, Aaron D., Remedios, Lucas W., Bao, Shunxing, Remedios, Samuel W., Lee, Ho Hin, Cai, Leon, Li, Thomas, Deng, Ruining, Cui, Can, Li, Jia, Liu, Qi, Lau, Ken S., Roland, Joseph T., Washington, Mary K., Coburn, Lori A., Wilson, Keith T., Huo, Yuankai, and Landman, Bennett A.

Published
2024
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12. Metrics reloaded: recommendations for image analysis validation

Author

Maier-Hein, Lena, Reinke, Annika, Godau, Patrick, Tizabi, Minu D., Buettner, Florian, Christodoulou, Evangelia, Glocker, Ben, Isensee, Fabian, Kleesiek, Jens, Kozubek, Michal, Reyes, Mauricio, Riegler, Michael A., Wiesenfarth, Manuel, Kavur, A. Emre, Sudre, Carole H., Baumgartner, Michael, Eisenmann, Matthias, Heckmann-Nötzel, Doreen, Rädsch, Tim, Acion, Laura, Antonelli, Michela, Arbel, Tal, Bakas, Spyridon, Benis, Arriel, Blaschko, Matthew B., Cardoso, M. Jorge, Cheplygina, Veronika, Cimini, Beth A., Collins, Gary S., Farahani, Keyvan, Ferrer, Luciana, Galdran, Adrian, van Ginneken, Bram, Haase, Robert, Hashimoto, Daniel A., Hoffman, Michael M., Huisman, Merel, Jannin, Pierre, Kahn, Charles E., Kainmueller, Dagmar, Kainz, Bernhard, Karargyris, Alexandros, Karthikesalingam, Alan, Kofler, Florian, Kopp-Schneider, Annette, Kreshuk, Anna, Kurc, Tahsin, Landman, Bennett A., Litjens, Geert, Madani, Amin, Maier-Hein, Klaus, Martel, Anne L., Mattson, Peter, Meijering, Erik, Menze, Bjoern, Moons, Karel G. M., Müller, Henning, Nichyporuk, Brennan, Nickel, Felix, Petersen, Jens, Rajpoot, Nasir, Rieke, Nicola, Saez-Rodriguez, Julio, Sánchez, Clara I., Shetty, Shravya, van Smeden, Maarten, Summers, Ronald M., Taha, Abdel A., Tiulpin, Aleksei, Tsaftaris, Sotirios A., Van Calster, Ben, Varoquaux, Gaël, and Jäger, Paul F.

Abstract

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint—a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.

Published
2024
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13. Understanding metric-related pitfalls in image analysis validation

Author

Reinke, Annika, Tizabi, Minu D., Baumgartner, Michael, Eisenmann, Matthias, Heckmann-Nötzel, Doreen, Kavur, A. Emre, Rädsch, Tim, Sudre, Carole H., Acion, Laura, Antonelli, Michela, Arbel, Tal, Bakas, Spyridon, Benis, Arriel, Buettner, Florian, Cardoso, M. Jorge, Cheplygina, Veronika, Chen, Jianxu, Christodoulou, Evangelia, Cimini, Beth A., Farahani, Keyvan, Ferrer, Luciana, Galdran, Adrian, van Ginneken, Bram, Glocker, Ben, Godau, Patrick, Hashimoto, Daniel A., Hoffman, Michael M., Huisman, Merel, Isensee, Fabian, Jannin, Pierre, Kahn, Charles E., Kainmueller, Dagmar, Kainz, Bernhard, Karargyris, Alexandros, Kleesiek, Jens, Kofler, Florian, Kooi, Thijs, Kopp-Schneider, Annette, Kozubek, Michal, Kreshuk, Anna, Kurc, Tahsin, Landman, Bennett A., Litjens, Geert, Madani, Amin, Maier-Hein, Klaus, Martel, Anne L., Meijering, Erik, Menze, Bjoern, Moons, Karel G. M., Müller, Henning, Nichyporuk, Brennan, Nickel, Felix, Petersen, Jens, Rafelski, Susanne M., Rajpoot, Nasir, Reyes, Mauricio, Riegler, Michael A., Rieke, Nicola, Saez-Rodriguez, Julio, Sánchez, Clara I., Shetty, Shravya, Summers, Ronald M., Taha, Abdel A., Tiulpin, Aleksei, Tsaftaris, Sotirios A., Van Calster, Ben, Varoquaux, Gaël, Yaniv, Ziv R., Jäger, Paul F., and Maier-Hein, Lena

Abstract

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.

Published
2024
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14. The mediation roles of intermuscular fat and inflammation in muscle mitochondrial associations with cognition and mobility

Author

Tian, Qu, Lee, Philip R., Yang, Qi, Moore, Anne Z., Landman, Bennett A., Resnick, Susan M., and Ferrucci, Luigi

Abstract

Mitochondrial dysfunction may contribute to brain and muscle health through inflammation or fat infiltration in the muscle, both of which are associated with cognitive function and mobility. We aimed to examine the association between skeletal muscle mitochondrial function and cognitive and mobility outcomes and tested the mediation effect of inflammation or fat infiltration. We analysed data from 596 Baltimore Longitudinal Study of Aging participants who had concurrent data on skeletal muscle oxidative capacity and cognitive and mobility measures of interest (mean age: 66.1, 55% women, 24% Black). Skeletal muscle oxidative capacity was assessed as post‐exercise recovery rate (kPCr) via P31 MR spectroscopy. Fat infiltration was measured as intermuscular fat (IMF) via CT scan and was available for 541 participants. Inflammation markers [IL‐6, C‐reactive protein (CRP), total white blood cell (WBC), neutrophil count, erythrocyte sedimentation rate (ESR), or albumin] were available in 594 participants. We examined the association of kPCr and cognitive and mobility measures using linear regression and tested the mediation effect of IMF or inflammation using the mediation package in R. Models were adjusted for demographics and PCr depletion. kPCr and IMF were both significantly associated with specific cognitive domains (DSST, TMA‐A, and pegboard dominant hand performance) and mobility (usual gait speed, HABCPPB, 400 m walk time) (all P< 0.05). IMF significantly mediated the relationship between kPCr and these cognitive and mobility measures (all P< 0.05, proportion mediated 13.1% to 27%). Total WBC, neutrophil count, and ESR, but not IL‐6 or CRP, also mediated at least one of the cognitive and mobility outcomes (all P< 0.05, proportion mediated 9.4% to 15.3%). Skeletal muscle mitochondrial function is associated with cognitive performance involving psychomotor speed. Muscle fat infiltration and specific inflammation markers mediate the relationship between muscle mitochondrial function and cognitive and mobility outcomes. Future studies are needed to confirm these associations longitudinally and to understand their mechanistic underpinnings.

Published
2024
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15. Semantic-Aware Contrastive Learning for Multi-Object Medical Image Segmentation

Author

Lee, Ho Hin, Tang, Yucheng, Yang, Qi, Yu, Xin, Cai, Leon Y., Remedios, Lucas W., Bao, Shunxing, Landman, Bennett A., and Huo, Yuankai

Abstract

Medical image segmentation, or computing voxel-wise semantic masks, is a fundamental yet challenging task in medical imaging domain. To increase the ability of encoder-decoder neural networks to perform this task across large clinical cohorts, contrastive learning provides an opportunity to stabilize model initialization and enhances downstream tasks performance without ground-truth voxel-wise labels. However, multiple target objects with different semantic meanings and contrast level may exist in a single image, which poses a problem for adapting traditional contrastive learning methods from prevalent “image-level classification” to “pixel-level segmentation”. In this article, we propose a simple semantic-aware contrastive learning approach leveraging attention masks and image-wise labels to advance multi-object semantic segmentation. Briefly, we embed different semantic objects to different clusters rather than the traditional image-level embeddings. We evaluate our proposed method on a multi-organ medical image segmentation task with both in-house data and MICCAI Challenge 2015 BTCV datasets. Compared with current state-of-the-art training strategies, our proposed pipeline yields a substantial improvement of 5.53% and 6.09% on Dice score for both medical image segmentation cohorts respectively (p-value$&lt;$ 0.01). The performance of the proposed method is further assessed on external medical image cohort via MICCAI Challenge FLARE 2021 dataset, and achieves a substantial improvement from Dice 0.922 to 0.933 (p-value$&lt;$ 0.01).

Published
2023
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18. Deep whole brain segmentation of 7T structural MRI

Author

Colliot, Olivier, Išgum, Ivana, Ramadass, Karthik, Yu, Xin, Cai, Leon Y., Tang, Yucheng, Bao, Shunxing, Kerley, Cailey I., D'Archangel, Micah A., Barquero, Laura A., Newton, Allen T., Gauthier, Isabel, McGugin, Rankin Williams, Dawant, Benoit M., Cutting, Laurie E., Huo, Yuankai, and Landman, Bennett A.

Published
2023
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23. Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning

Author

Hering, Alessa, Hansen, Lasse, Mok, Tony C. W., Chung, Albert C. S., Siebert, Hanna, Hager, Stephanie, Lange, Annkristin, Kuckertz, Sven, Heldmann, Stefan, Shao, Wei, Vesal, Sulaiman, Rusu, Mirabela, Sonn, Geoffrey, Estienne, Theo, Vakalopoulou, Maria, Han, Luyi, Huang, Yunzhi, Yap, Pew-Thian, Brudfors, Mikael, Balbastre, Yael, Joutard, Samuel, Modat, Marc, Lifshitz, Gal, Raviv, Dan, Lv, Jinxin, Li, Qiang, Jaouen, Vincent, Visvikis, Dimitris, Fourcade, Constance, Rubeaux, Mathieu, Pan, Wentao, Xu, Zhe, Jian, Bailiang, De Benetti, Francesca, Wodzinski, Marek, Gunnarsson, Niklas, Sjolund, Jens, Grzech, Daniel, Qiu, Huaqi, Li, Zeju, Thorley, Alexander, Duan, Jinming, Grosbrohmer, Christoph, Hoopes, Andrew, Reinertsen, Ingerid, Xiao, Yiming, Landman, Bennett, Huo, Yuankai, Murphy, Keelin, Lessmann, Nikolas, van Ginneken, Bram, Dalca, Adrian V., and Heinrich, Mattias P.

Abstract

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods

Published
2023
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39. Multimodal neuroimaging in pediatric type 1 diabetes: a pilot multisite feasibility study of acquisition quality, motion, and variability

Author

Colliot, Olivier, Išgum, Ivana, Cai, Leon Y., Tanase, Costin, Anderson, Adam W., Ramadass, Karthik, Rheault, Francois, Lee, Chelsea A., Patel, Niral J., Jones, Sky, LeStourgeon, Lauren M., Mahon, Alix, Pruthi, Sumit, Gwal, Kriti, Ozturk, Arzu, Kang, Hakmook, Glaser, Nicole, Ghetti, Simona, Jaser, Sarah S., Jordan, Lori C., and Landman, Bennett A.

Published
2022
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42. Multi-modal learning with missing data for cancer diagnosis using histopathological and genomic data

Author

Drukker, Karen, Iftekharuddin, Khan M., Lu, Hongbing, Mazurowski, Maciej A., Muramatsu, Chisako, Samala, Ravi K., Cui, Can, Asad, Zuhayr, Dean, William F., Smith, Isabelle T., Madden, Christopher, Bao, Shunxing, Landman, Bennett A., Roland, Joseph T., Coburn, Lori A., Wilson, Keith T., Zwerner, Jeffrey P., Zhao, Shilin, Wheless, Lee E., and Huo, Yuankai

Published
2022
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43. Axonal Injury Partially Mediates Associations Between Increased Left Ventricular Mass Index and White Matter Damage

Author

Moore, Elizabeth E., Khan, Omair A., Shashikumar, Niranjana, Pechman, Kimberly R., Liu, Dandan, Bell, Susan P., Nair, Sangeeta, Terry, James G., Gifford, Katherine A., Anderson, Adam W., Landman, Bennett A., Blennow, Kaj, Zetterberg, Henrik, Hohman, Timothy J., Carr, John Jeffrey, and Jefferson, Angela L.

Abstract

Supplemental Digital Content is available in the text.

Published
2022
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44. Enabling AI innovation via data and model sharing: An overview of the NSF Convergence Accelerator Track D

Author

Baru, Chaitanya, Pozmantier, Michael, Altintas, Ilkay, Baek, Stephen, Cohen, Jonathan, Condon, Laura, Fanti, Giulia, Fernandez, Raul Castro, Jackson, Ethan, Lall, Upmanu, Landman, Bennett, Li, Hai Helen, Marin, Claudia, Lopez, Beatriz Martinez, Metaxas, Dimitris, Olsen, Bradley, Page, Grier, Shang, Jingbo, Turkan, Yelda, and Zhang, Peng

Abstract

This article provides a brief overview of 18 projects funded in Track D—Data and Model Sharing to Enable AI Innovation—of the 2020 Cohort of the National Science Foundation's (NSF) Convergence Accelerator (CA) program. The NSF CA is focused on transitioning research to practice for societal impact. The projects described here were funded for one year in phase I of the program, beginning September 2020. Their focus is on delivering tools, technologies, and techniques to assist in sharing data as well as data-driven models to enable AI innovation. A broad range of domain areas is covered by the funded efforts, spanning across healthcare and medicine, to climate change and disaster, and civil/built infrastructure. The projects are addressing sharing of open as well as sensitive/private data. In September 2021, six of the eighteen projects described here were selected for phase II of the program, as noted in this article.

Published
2022
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45. Deterministic inverse design of Tamm plasmon thermal emitters with multi-resonant control

Author

He, Mingze, Nolen, J. Ryan, Nordlander, Josh, Cleri, Angela, McIlwaine, Nathaniel S., Tang, Yucheng, Lu, Guanyu, Folland, Thomas G., Landman, Bennett A., Maria, Jon-Paul, and Caldwell, Joshua D.

Abstract

Wavelength-selective thermal emitters (WS-EMs) are of interest due to the lack of cost-effective, narrow-band sources in the mid- to long-wave infrared. WS-EMs can be realized via Tamm plasmon polaritons (TPPs) supported by distributed Bragg reflectors on metals. However, the design of multiple resonances is challenging as numerous structural parameters must be optimized simultaneously. Here we use stochastic gradient descent to optimize TPP emitters (TPP-EMs) composed of an aperiodic distributed Bragg reflector deposited on doped cadmium oxide (CdO) film, where layer thicknesses and carrier density are inversely designed. The combination of the aperiodic distributed Bragg reflector with the designable plasma frequency of CdO enables multiple TPP-EM modes to be simultaneously designed with arbitrary spectral control not accessible with metal-based TPPs. Using this approach, we experimentally demonstrated and numerically proposed TPP-EMs exhibiting single or multiple emission bands with designable frequencies, line-widths and amplitudes. This thereby enables lithography-free, wafer-scale WS-EMs that are complementary metal–oxide–semiconductor compatible for applications such as free-space communications and gas sensing.

Published
2021
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