On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge
Dec. 13, 2022—Alberto De Luca, Andrada Ianus, Alexander Leemans, Marco Palombo, Noam Shemesh, Hui Zhang, Daniel C Alexander, Markus Nilsson, Martijn Froeling, Geert-Jan Biessels, Mauro Zucchelli, Matteo Frigo, Enes Albay, Sara Sedlar, Abib Alimi, Samuel Deslauriers-Gauthier, Rachid Deriche, Rutger Fick, Maryam Afzali, Tomasz Pieciak, Fabian Bogusz, Santiago Aja-Fernandez, Evren Ozarslan, Derek K Jones, Haoze Chan, Mingwu Jin,...
Pandora: 4-D white matter bundle population-based atlases derived from diffusion MRI fiber tractography
Dec. 13, 2022—Colin B Hansen*, Qi Yang*, Ilwoo Lyu, Francois Rheault, Cailey Kerley, Bramsh Qamar Chandio,Shreyas Fadnavis, Owen Williams, Andrea T. Shafer, Susan M. Resnick, David H. Zald, Laurie Cutting, Warren D Taylor, Brian Boyd, Eleftherios Garyfallidis, Adam W Anderson, Maxime Descoteaux, Bennett A Landman, Kurt G Schilling. “Pandora: 4-D white matter bundle population-based atlases derived from...
Deep whole brain segmentation of 7T structural MRI
Dec. 4, 2022—Karthik Ramadass, Xin Yu, Leon Y. Cai, Yucheng Tang, Shunxing Bao, Cailey Kerley, Micah D’Archangel, Laura A Barquero, Allen T. Newton, Isabel Gauthier, Rankin Williams McGugin, Benoit M. Dawant, Laurie E. Cutting, Yuankai Huo, Bennett A. Landman SPIE Medical Imaging 2023 Abstract 7T magnetic resonance imaging (MRI) has the potential to drive our understanding of...
Mapping the Impact of Approximate Gradient Nonlinearity Fields Correction on Tractography
Dec. 3, 2022—Praitayini Kanakaraj, Francois Rheault, Leon Y. Cai, Nancy Newlin, Baxter P. Rogers, Kurt G. Schilling, Bennett A. Landman. “Mapping the Impact of Approximate Gradient Nonlinearity Fields Correction on Tractography” SPIE Medical Imaging : Image Processing 2023. Accepted. Abstract Nonlinear gradients impact diffusion weighted MRI by introducing spatial variation in estimated diffusion tensors. Recent studies have...
Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography.
Dec. 2, 2022—Thomas Z. Li, Kaiwen Xu, Riqiang Gao, Yucheng Tang, Thomas A. Lasko, Fabien Maldonado, Kim Sandler, Bennett A. Landman. Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography. SPIE Medical Imaging 2022. Full text: arxiv.org/abs/2209.01676 Abstract Features learned from single radiologic images are unable to provide information about whether and how much a lesion...
Predicting Crohn’s disease severity in the colon using mixed cell nucleus density from pseudo labels
Dec. 1, 2022—Lucas W. Remedios, Shunxing Bao, Cailey I. Kerley, Leon Y. Cai, François Rheault, Ruining Deng, Can Cui, Sophie Chiron, Ken S. Lau, Joseph T. Roland, Mary K. Washington, Lori A. Coburn, Keith T. Wilson, Yuankai Huo, Bennett A. Landman (2023). Predicting Crohn’s disease severity in the colon using mixed cell nucleus density from pseudo labels....
Extending the value of routine lung screening CT with quantitative body composition assessment
Nov. 28, 2022—Kaiwen Xu, Riqiang Gao, Yucheng Tang, Steve A. Deppen, Kim L. Sandler, Michael N. Kammer, Sanja L. Antic, Fabien Maldonado, Yuankai Huo, Mirza S. Khan, Bennett A. Landman Abstract Certain body composition phenotypes, like sarcopenia, are well established as predictive markers for post-surgery complications and overall survival of lung cancer patients. However, their association with...
SynBOLD-DisCo: Synthetic BOLD images for distortion correction of fMRI without additional calibration scans
Nov. 13, 2022—Tian Yu*, Leon Y. Cai*, Victoria L. Morgan, Sarah E. Goodale, Dario J. Englot, Catherine E. Chang, Bennett A. Landman, and Kurt G. Schilling * Equal first authorship https://github.com/MASILab/SynBOLD-DisCo Abstract The blood oxygen level dependent (BOLD) signal from functional magnetic resonance imaging (fMRI) is a noninvasive technique that has been widely used in research to...
Batch size: go big or go home? Counterintuitive improvement in medical autoencoders with smaller batch size
Nov. 13, 2022—Cailey I. Kerley*, Leon Y. Cai*, Yucheng Tang, Lori L. Beason-Held, Susan M. Resnick, Laurie E. Cutting, and Bennett A. Landman. *Equal first authorship Abstract Batch size is a key hyperparameter in training deep learning models. Conventional wisdom suggests larger batches produce improved model performance. Here we present evidence to the contrary, particularly when using autoencoders...
3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation
Oct. 6, 2022—Ho Hin Lee, Shunxing Bao, Yuankai Huo, Bennett A. Landman, “3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation”, arXiv 2022 Full Text Abstract Vision transformers (ViTs) have quickly superseded convolutional networks (ConvNets) as the current state-of-the-art (SOTA) models for medical image segmentation. Hierarchical transformers (e.g., Swin Transformers) reintroduced several...