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...
Workflow Integration of Research AI Tools into a Hospital Radiology Rapid Prototyping Environment
Jul. 25, 2022—Praitayini Kanakaraj, Karthik Ramadas, Shunxing Bao, Melissa Basford, Laura M. Jones, Ho Hin Lee, Kurt G. Schilling, John Jeffery Carr, James Gregory Terry, Yuankai Huo, Kim Lori Sandler, Allen T. Netwon, Bennett A. Landman “Workflow Integration of Research AI Tools into a Hospital Radiology Rapid Prototyping Environment”. Journal of Digital Imaging (2022): 1-11. Full Text...
Aging and white matter microstructure and macrostructure: a longitudinal multi-site diffusion MRI study of 1218 participants
Jul. 25, 2022—Kurt G Schilling, Derek Archer, Fang-Cheng Yeh, Francois Rheault, Leon Cai, Colin Hansen, Qi Yang, Andrea Shafer, Susan Resnick, Kimberly R. Pechman, Katherine A. Gifford, Timothy J. Hohman, Angela Jefferson, Adam W Anderson, Hakmook Kang, Bennett A Landman, Aging and white matter microstructure and microstructure: a longitudinal multi-site diffusion MRI study of 1,184 participants. Brain...
Contrastive semi-supervised harmonization of single-shell to multi-shell diffusion MRI
Jul. 25, 2022—Colin B. Hansen, Kurt G. Schilling, Francois Rheault, Susan Resnick, Andrea T. Shafer, Lori L. Beason-Held, Bennett A. Landmƒan. “Contrastive semi-supervised harmonization of single-shell to multi-shell diffusion MRI.” Magnetic Resonance Imaging (2022). Full Text Abstract Diffusion weighted MRI (DW-MRI) harmonization is necessary for multi-site or multi-acquisition studies. Current statistical methods address the need to harmonize...
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...
Tractostorm 2: Optimizing tractography dissection reproducibility with segmentation protocol dissemination
Feb. 11, 2022—Rheault, Francois, Kurt G. Schilling, Alex Valcourt-Caron, Antoine Théberge, Charles Poirier, Gabrielle Grenier, Guido I. Guberman, John Begnoche, Jon Haitz Legarreta, Leon Y. Cai, Maggie Roy, Manon Edde, Marco Perez Caceres, Mario Ocampo-Pineda, Noor Al-Sharif, Philippe Karan, Pietro Bontempi, Sami Obaid, Sara Bosticardo, Simona Schiavi, Viljami Sairanen, Alessandro Daducci, Laurie E. Cutting, Laurent Petit, Maxime...
Longitudinal changes of connectomes and graph theory measures in aging
Jan. 17, 2022—Yuzhe Wang, Francois Rheault, Kurt G. Schilling, Lori L. Beason-Held, Andrea T. Shafer, Susan M. Resnick, Bennett A. Landman Abstract Changes in brain structure and connectivity in aging can be probed through diffusion weighted MRI and summarized with structural connectome matrices. Complex network analysis based on graph theory has been applied to provide measures that...
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...
Pancreas CT Segmentation by Predictive Phenotyping
Dec. 14, 2021—Y. Tang, R.Gao, H.H.Lee, Q.Yang, X.Yu,Y.Zhou, S.Bao, Y.Huo, J.Spraggins, J.Virostko, Z.Xu, B.A. Landman. “Pancreas CTSegmentation by Predictive Phenotyping”. International Conference on MedicalImage Computing and Computer Assisted Intervention(MICCAI), 2021 Full Text: https://link.springer.com/chapter/10.1007/978-3-030-87193-2_3 Abstract Pancreas CT segmentation offers promise at understanding the structural manifestation of metabolic conditions. To date, the medical primary record of conditions that impact...
High-resolution 3D abdominal segmentation with random patch network fusion
Dec. 14, 2021—Y. Tang,R.Gao,S.Han, Y.Chen, D.Gao, V.Nath, C.Bermudez, M.R. Savona, R.G. Abramson, S.Bao,I.Lyu, Y.Huo and B.A. Landman,“High-resolution 3D Abdominal Segmentation with Random PatchNetworkFusion”,Medical Image Analysis, 2021. Full Text: https://www.sciencedirect.com/science/article/pii/S1361841520302589 Abstract Deep learning for three dimensional (3D) abdominal organ segmentation on high-resolution computed to- mography (CT) is a challenging topic, in part due to the limited memory provide...