News Category
pyPheWAS: A Phenome-Disease Association Tool for Electronic Medical Record Analysis
Jan. 12, 2022—Kerley, C.I., Chaganti, S., Nguyen, T.Q. et al. pyPheWAS: A Phenome-Disease Association Tool for Electronic Medical Record Analysis. Neuroinform (2022). https://doi.org/10.1007/s12021-021-09553-4 Full text: NIHMSID, Springer Abstract Along with the increasing availability of electronic medical record (EMR) data, phenome-wide association studies (PheWAS) and phenome-disease association studies (PheDAS) have become a prominent, first-line method of analysis for uncovering...
Mapping the impact of non-linear gradient fields on diffusion MRI tensor estimation
Dec. 11, 2021—Praitayini Kanakaraj, Colin B. Hansen, Francois Rheault, Leon Y. Cai, Baxter P. Rogers, Kurt G. Schilling and Bennett A. Landman. “Mapping the impact of non-linear gradient fields on diffusion MRI tensor estimation”. SPIE Medical Imaging 2022. Accepted. Full Text Abstract Non-linear gradients impact diffusion weighted (DW) MRI by corrupting the experimental setup and lead to problems during...
Accelerating 2D Abdominal Organ Segmentation with Active Learning
Dec. 10, 2021—Xin Yu, Yucheng Tang, Qi Yang, Ho Hin Lee, Shunxing Bao, Ann Zenobia Moore, Luigi Ferrucci, Bennett A. Landman Abdominal computed tomography CT imaging enables assessment of body habitus and organ health. Quantification of these health factors necessitates semantic segmentation of key structures. Deep learning efforts have shown remarkable success in automating segmentation of...
RAP-Net: Coarse-to-Fine Multi-Organ Segmentation with Single Random Anatomical Prior
Dec. 28, 2020—Ho Hin Lee, Yucheng Tang, Shunxing Bao, Richard G. Abramson, Yuankai Huo, Bennett A. Landman. “RAP-Net: Coarse-to-Fine Multi-Organ Segmentation with Single Random Anatomical Prior.” arXiv preprint arXiv:2012.12425 (2020). Full Text Abstract Performing coarse-to-fine abdominal multi-organ segmentation facilitates to extract high-resolution segmentation minimizing the lost of spatial contextual information. However, current coarse-to-refine approaches require a significant number of models...
Semi-supervised Machine Learning with MixMatch and Equivalence Classes
Dec. 4, 2020—Colin B. Hansen, Vishwesh Nath, Riqiang Gao, Camilo Bermudez, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Jeffrey D. Blume, Thomas A. Lasko, Bennett A. Landman “Semi-supervised Machine Learning with MixMatch and Equivalence Classes.” Interpretable and Annotation-Efficient Learning for Medical Image Computing. Springer, Cham, 2020. 112-121. Full Text Abstract Semi-supervised methods have an increasing impact on...
Validation of group-wise registration for surface-based functional MRI analysis
Oct. 20, 2020—Chang Yu, Yue Liu, Leon Cai, Cailey Kerley, Kaiwen Xu, Katherine Aboud, Warren Taylor, Hakmook Kang, Andrea Shafer, Lori Beason-Held, Susan Resnick, Bennett Landman, Ilwoo Lyu. “Validation of group-wise registration for surface-based functional MRI analysis”. SPIE Medical Imaging 2021. [Full text][Code] Abstract Resting-state functional MRI (rsfMRI) provides important information for studying and mapping the activities...
![Qualitative comparison of tracer and tractography for PCG injection. Tracer digitized on the reference atlas [and reproduced from Schmahmann and Pandya (2006), with permission] is shown alongside the anatomically matched b0 slice with streamlines shown in black (only streamlines within ± 1 slice are displayed) for both the manual-based dissection and the template-based dissection](https://cdn.vanderbilt.edu/vu-my/wp-content/uploads/sites/2304/2020/08/25134601/429_2020_2129_Fig4_HTML-150x150.png)
Brain connections derived from diffusion MRI tractography can be highly anatomically accurate – if we know where white matter pathways start, where they end, and where they don’t go
Aug. 25, 2020—Kurt G Schilling, Laurent Petit, Francois Rheault, Samuel Remedios, Carlo Pierpaoli, Adam W Anderson, Bennett A Landman, Maxime Descoteaux. “Brain connections derived from diffusion MRI tractography can be highly anatomically accurate – if we know where white matter pathways start, where they end, and where they don’t go”. Brain Struct Funct (2020). https://doi.org/10.1007/s00429-020-02129-z Full text: https://link.springer.com/article/10.1007/s00429-020-02129-z Abstract MR...
Histologically derived fiber response functions for diffusion MRI vary across white matter fibers – an ex vivo validation study in the squirrel monkey brain
Aug. 25, 2020—Kurt G Schilling, Yurui Gao, Iwona Stepniewska, Vaibhav Janve, Bennett A. Landman, Adam W. Anderson. “Histologically derived fiber response functions for diffusion MRI vary across white matter fibers – an ex vivo validation study in the squirrel monkey brain”. NMR in Biomedicine. 2019 Jun;32(6):e4090. doi: 10.1002/nbm.4090. Epub 2019 Mar 25. Full text: https://pubmed.ncbi.nlm.nih.gov/30908803/ Abstract Understanding the...
Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps
Aug. 25, 2020—Kurt G Schilling*, Justin Blaber*, Colin Hansen, Leon Cai, Baxter Rogers, Adam W Anderson, Seth A Smith, Praitayini Kanakaraj, Tonia Rex, Susan M. Resnick, Andrea T. Shafer, Laurie Cutting, Neil Woodward, David Zald, Bennett A Landman.“ Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps ”. PLoS ONE 15(7): e0236418. https://doi.org/10.1371/journal.pone.0236418 Full text: https:...
Automatic Labeling of Cortical Sulci using Convolutional Neural Networks in a Developmental Cohort
Apr. 20, 2020—Lingyan Hao, Shunxing Bao, Yucheng Tang, Riqiang Gao, Prasanna Parvathaneni, Jacob Miller, Willa Voorhies, Jewelia Yao, Silvia Bunge, Kevin Weiner, Bennett Landman, Ilwoo Lyu. “Automatic Labeling of Cortical Sulci using Convolutional Neural Networks in a Developmental Cohort”. IEEE International Symposium on Biomedical Imaging (ISBI) 2020, IEEE, 412-415, Iowa City, Iowa, USA, 2020. [Full text][Code] Abstract...