Hierarchical Spherical Deformation for Cortical Surface Registration
Nov. 11, 2019—Ilwoo Lyu, Hakmook Kang, Neil D. Woodward, Martin A. Styner and Bennett A. Landman. “Hierarchical Spherical Deformation for Cortical Surface Registration”. Medical Image Analysis, 57, pp. 72-88, 2019. [Full text][Code][Docker] Abstract We present hierarchical spherical deformation for a group-wise shape correspondence to address template selection bias and to minimize registration distortion. In this work, we...
Distanced LSTM: Time-Distanced Gates in Long Short-Term Memory Models for Lung Cancer Detection
Oct. 13, 2019—Gao, R., Huo, Y., Bao, S., Tang, Y., Antic, S.L., Epstein, E.S., Balar, A.B., Deppen, S., Paulson, A.B., Sandler, K.L. and Massion, P.P., 2019, October. Distanced LSTM: Time-Distanced Gates in Long Short-Term Memory Models for Lung Cancer Detection. In International Workshop on Machine Learning in Medical Imaging (pp. 310-318). Springer, Cham. [Full Text] Abstract The field of...
Generalizing Deep Whole Brain Segmentation for Pediatric and Post-Contrast MRI with Augmented Transfer Learning
Aug. 13, 2019—Bermudez, C., Blaber, J., Remedios, S.W., Reynolds, J.E., Lebel, C., McHugo, M., Heckers, S., Huo, Y., Landman, B.A. Generalizing Deep Whole Brain Segmentation for Pediatric and Post-Constrast MRI with Augmented Transfer Learning. SPIE Medical Imaging: Image Processing 2020. Houston, TX. Full Text: NIHMSID Abstract Generalizability is an important problem in deep neural networks, especially in...
Anatomical context improves deep learning on the brain age estimation task
Jul. 12, 2019—Bermudez, C., Plassard, A. J., Chaganti, S., Huo, Y., Aboud, K. E., Cutting, L. E., … & Landman, B. A. (2019). Anatomical context improves deep learning on the brain age estimation task. Magnetic Resonance Imaging. Full Text: https://www.ncbi.nlm.nih.gov/pubmed/31247249 Abstract Deep learning has shown remarkable improvements in the analysis of medical images without the need for...
Cortical Surface Parcellation using Spherical Convolutional Neural Networks
Jun. 6, 2019—*Prasanna Parvathaneni, *Shunxing Bao, Vishwesh Nath, David H. Zald, Neil D. Woodward, Daniel O. Claassen, Carissa Cascio, Yuankai Huo, Bennett A. Landman, Ilwoo Lyu, “Cortical Surface Parcellation using Spherical Convolutional Neural Networks”, MICCAI 2019, LNCS11766, 501-509. Abstract We present cortical surface parcellation using spherical deep convolutional neural networks. Traditional multi-atlas cortical surface parcellation requires inter-subject surface registration using geometric features with high processing...
Improved gray matter surface based spatial statistics in neuroimaging studies
May. 21, 2019—Prasanna Parvathaneni; Ilwoo Lyu; Yuankai Huo; Baxter P. Rogers; Kurt G. Schilling; Vishwesh Nath; Justin A Blaber; Allison E Hainline; Adam W Anderson; Neil D. Woodward; Bennett A Landman. “Improved gray matter surface based spatial statistics in neuroimaging studies.” Magnetic Resonance Imaging, 61, 285-295, 2019. Full text Abstract Neuroimaging often involves acquiring high-resolution anatomical images along with...
A fiber coherence index for quality control of B-table orientation in diffusion MRI scans.
May. 16, 2019—Kurt G Schilling, Fang-Cheng Yeh, Vishwesh Nath, Colin Hansen, Owen Williams, Susan Resnick, Adam W. Anderson, Bennett A. Landman. “A fiber coherence index for quality control of B-table orientation in diffusion MRI scans”. Magnetic Resonance Imaging. 2019. doi: 10.1016/j.mri.2019.01.018 Full text: https://www.ncbi.nlm.nih.gov/pubmed/?term=fiber+coherence+b-table Abstract Purpose The diffusion MRI “b-vector” table describing the diffusion sensitization direction can be flipped and...
Compensation of Gradient Field Nonlinearity and Signal Drift in Diffusion Weighted MRI
Apr. 1, 2019—Colin B. Hansen, Vishwesh Nath, Kurt G. Schilling, Justin A. Blaber, Baxter P. Rogers, Bennett A. Landman, “Compensation of Gradient Field Nonlinearity and Signal Drift in Diffusion Weighted MRI.” ISBI, Venice, Italy, 2019 Diffusion weighted magnetic resonance imaging (DW-MRI) represents an important method of studying local brain tissue microarchitecture and structural connectivity. Interpretation of DW-MRI is challenging...
Learning 3D White Matter Microstructure from 2D Histology
Apr. 1, 2019—Histological analysis is typically the gold standard for validating measures of tissue microstructure derived from magnetic resonance imaging (MRI) contrasts. However, most histological investigations are inherently 2-dimensional (2D), due to increased field-of-view, higher in-plane resolutions, ease of acquisition, decreased costs, and a large number of available contrasts compared to 3-dimensional (3D) analysis. Because of this,...
Histologically derived fiber response functions for diffusion MRI vary across white matter fibers-An ex vivo validation study in the squirrel monkey brain.
Mar. 16, 2019—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://www.ncbi.nlm.nih.gov/pubmed/30908803 Abstract Understanding the...