Deep Learning Category
Convolutional-recurrent neural networks approximate diffusion tractography from T1-weighted MRI and associated anatomical context
May. 29, 2023—Leon Y. Cai, Ho Hin Lee, Nancy R. Newlin, Cailey I. Kerley, Praitayini Kanakaraj, Qi Yang, Graham W. Johnson, Daniel Moyer, Kurt G. Schilling, François Rheault, and Bennett A. Landman Paper: https://www.biorxiv.org/content/10.1101/2023.02.25.530046v2 Code: https://github.com/MASILab/cornn_tractography Abstract Diffusion MRI (dMRI) streamline tractography is the gold-standard for in vivo estimation of white matter (WM) pathways in the brain. However, the...
Implementation considerations for deep learning with diffusion MRI streamline tractography
May. 29, 2023—Leon Y. Cai, Ho Hin Lee, Nancy R. Newlin, Michael E. Kim, Daniel Moyer, Francois Rheault, Kurt G. Schilling, and Bennett A. Landman Paper: https://www.biorxiv.org/content/10.1101/2023.04.03.535465v1 Code: https://github.com/MASILab/STrUDeL Abstract One area of medical imaging that has recently experienced innovative deep learning advances is diffusion MRI (dMRI) streamline tractography with recurrent neural networks (RNNs). Unlike traditional imaging studies which...
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....
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...
Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
Jul. 25, 2022—Tang, Yucheng, Dong Yang, Wenqi Li, Holger R. Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, and Ali Hatamizadeh. “Self-supervised pre-training of swin transformers for 3d medical image analysis.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20730-20740. 2022. Full text: Abstract Vision Transformers (ViT)s have shown great performance in self-supervised...
Label efficient segmentation of single slice thigh CT with two-stage pseudo labels
Jul. 25, 2022—Qi Yang, Xin Yu, Ho Hin Lee, Yucheng Tang, Shunxing Bao,Kristofer S. Gravenstein, Ann Zenobia Moore, Sokratis Makrogiannis, Luigi Ferrucci, and Bennett A. Landman. “Label efficient segmentation of single slice thigh CT with two-stage pseudo labels” Journal of Medical Imaging, 2022 Purpose: Muscle, bone, and fat segmentation from thigh images is essential for quantifying body...
Reducing Positional Variance in Cross-sectional Abdominal CT Slices with Deep Conditional Generative Models
Jul. 25, 2022—Xin Yu*, Qi Yang*, Yucheng Tang, Riqiang Gao, Shunxing Bao, Leon Y. Cai, Ho Hin Lee, Ann Zenobia Moore, Luigi Ferrucci, Bennett A. Landman, “Reducing Positional Variance in Cross-sectional Abdominal CT Slices with Deep Conditional Generative Models”, MICCAI 2022 2D low-dose single-slice abdominal computed tomography (CT) slice enables direct measurements of body composition, which...
Generalizing deep learning brain segmentation for skull removal and intracranial measurements
Jul. 25, 2022—Yue Liu, Yuankai Huo, Blake Dewey, Ying Wei, Ilwoo Lyu, Bennett A. Landman ,“Generalizing deep learning brain segmentation for skull removal and intracranial measurements.”Magnetic Resonance Imaging. Volume 88, May 2022, Pages 44-52 Full Text Abstract Total intracranial volume (TICV) and posterior fossa volume (PFV) are essential covariates for brain volumetric analyses with structural magnetic resonanceimaging...