Machine Learning Category
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...
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...
Joint independent component analysis for hypothesizing spatiotemporal relationships between longitudinal gray and white matter changes in preclinical Alzheimer’s disease
Dec. 10, 2021—Leon Y. Cai, Francois Rheault, Cailey I. Kerley, Katherine S. Aboud, Lori L. Beason-Held, Andrea T. Shafer, Susan M. Resnick, Lori C. Jordan, Adam W. Anderson, Kurt G. Schilling, and Bennett A. Landman Abstract Characterizing relationships between gray matter (GM) and white matter (WM) in early Alzheimer’s disease (AD) would improve understanding of how and...
Automated, open-source segmentation of the Hippocampus and amygdala with the open Vanderbilt archive of the temporal lobe
Aug. 28, 2021—Plassard, Andrew J., Shunxing Bao*, Maureen McHugo, Lori Beason-Held, Jennifer U. Blackford, Stephan Heckers, and Bennett A. Landman. “Automated, open-source segmentation of the Hippocampus and amygdala with the open Vanderbilt archive of the temporal lobe.” Magnetic Resonance Imaging 81 (2021): 17-23. Full text: https://www.sciencedirect.com/science/article/abs/pii/S0730725X21000692 Abstract Examining volumetric differences of the amygdala and anterior-posterior regions of the...
Technical Note: Comparison of Convolutional Neural Networks for Detecting Large Vessel Occlusion on Computed Tomography Angiography
Jul. 21, 2021—Lucas W. Remedios, Sneha Lingam, Samuel W. Remedios, Riqiang Gao, Stephen W. Clark, Larry T. Davis, Bennett A. Landman. Technical Note: Comparison of Convolutional Neural Networks for Detecting Large Vessel Occlusion on Computed Tomography Angiography. Medical Physics, 2021 Full Text Abstract Purpose: Artificial intelligence diagnosis and triage of large vessel occlusion may quicken clinical response for...
Body Part Regression With Self-Supervision
Jan. 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,”Body Part Regression with Self-supervision”,IEEETransactions onMedicalImaging,2021 Full Text: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9350603 Abstract Body part regression is a promising new technique that enables content navigation through selfsupervised learning. Using this technique, the global quantitative spatial location for each axial view slice is obtained...
Generalizing deep whole-brain segmentation for post-contrast MRI with transfer learning
Dec. 26, 2020—Bermudez, C., Remedios, S. W., Ramadass, K., McHugo, M., Heckers, S., Huo, Y., & Landman, B. A. (2020). Generalizing deep whole-brain segmentation for post-contrast MRI with transfer learning. Journal of Medical Imaging, 7(6), 064004. Full Text: https://pubmed.ncbi.nlm.nih.gov/33381612/ Abstract Purpose: Generalizability is an important problem in deep neural networks, especially with variability of data acquisition in...
Learning from dispersed manual annotations with an optimized data weighting policy
Dec. 7, 2020—Yucheng Tang, Riqiang Gao, Yunqiang Chen, Dashan Gao, Michael R. Savona, Richard G. Abramson, Shunxing Bao, Yuankai Huo and Bennett A. Landman, “Learning from Dispersed Manual Annotations with an Optimized Data Weighting Policy”, Journal of Medical Imaging, 2020. Full Text: Abstract https://pubmed.ncbi.nlm.nih.gov/32775501/ Purpose: Deep learning methods have become essential tools for quantitative interpretation of medical...
Joint cortical surface and structural connectivity analysis of Alzheimer’s Disease
Nov. 20, 2020—Leon Y. Cai, Cailey I. Kerley, Chang Yu, Katherine S. Aboud, Lori L. Beason-Held, Andrea T. Shafer, Susan M. Resnick, Lori C. Jordan, Adam W. Anderson, Kurt G. Schilling, Ilwoo Lyu, Bennett A. Landman. Joint cortical surface and structural connectivity analysis of Alzheimer’s Disease. SPIE Medical Imaging, 2021. Full Text: NIHMSID Abstract Joint independent component...