Medical-image Analysis and Statistical Interpretation (MASI) Lab

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...

An overview of the proposed method. Three geometric features (iH, SD, H) are used for training the spherical U-Net to predict 49 cortical parcellation labels. For each geometric property, intermediate deformation fields draw a total of 11+11 respective samples after boundary and geometric alignment for data augmentation. The cortical parcellation is then performed using the original geometric features of testing subjects.

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...

Fig. 1. Effects of incorrect b-vector tables. Diffusion encoded color (DEC) maps and vector maps are shown for a correct gradient table (A), a table with a flipped component (B), and one with permuted components (C). With a flipped component, the DEC map may appear correct, but unit vectors will be flipped in one plane (coronal plane in this example), and will appear correct in another (axial). For a permuted table, both the color maps and vectors will be erroneous. In both (B) and (C), directions are incorrect, and subsequent analysis and tractography will also be erroneous. DEC and vector maps are colored red, green, and blue for diffusion primarily in the right/left, anterior/posterior, and superior/inferior directions, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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,...

Response functions estimated using conventional methods differ from the histologically derived response functions. Kernels estimated using the highest FA voxels, the iterative calibration method, and modeled from the diffusion tensor are shown in two views. The average histologically derived response function for each monkey (across 6 z stacks) is shown (monkeys 1–3 correspond to A‐C) along with the mean (red) and mean ± standard deviation (gray) surfaces to highlight variability. Note that differences in magnitude are due to differing normalization in our work and in MRTrix3. Shape, and not magnitude, determines the final FOD shape

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...

Consideration of Cerebrospinal Fluid Intensity Variation in Diffusion Weighted MRI

Feb. 7, 2019—Colin B. Hansen, Vishwesh Nath, Allison E. Hainline, Kurt G. Schilling, Prasanna Parvathaneni, Roza G. Bayrak, Justin A. Blaber, Owen Williams, Susan Resnick, Lori Beason-Held, Okan Irfanoglu, Carlo Pierpaoli, Adam W. Anderson, Baxter P. Rogers, Bennett A. Landman, “Consideration of Cerebrospinal Fluid Intensity Variation in Diffusion Weighted MRI.” SPIE Medical Imaging, San Diego, CA, 2019 Abstract Diffusion weighted MRI (DW-MRI) depends on...

Imaging Biomarkers in Thyroid Eye Disease and their Clinical Associations

Dec. 18, 2018—Shikha Chaganti, Katrina Nelson, Kevin Mundy, Robert Harrigan, Robert Galloway, Louise A. Mawn, Bennett Landman, “Imaging biomarkers in thyroid eye disease and their clinical associations,” Journal of Medical Imaging. (2018), doi: 10.1117/1.JMI.5.4.044001. Abstract Purpose: The purpose of this study is to understand the phenotypes of thyroid eye disease (TED) through data derived from a multi-atlas...

Electronic Medical Record Context Signatures Improve Diagnostic Classification using Medical Image Computing

Dec. 17, 2018—Shikha Chaganti, Louise A. Mawn, Hakmook Kang, Josephine Egan, Susan M. Resnick, Lori L. Beason-Held, Bennett A. Landman, Thomas A. Lasko. “Electronic Medical Record Context Signatures Improve Diagnostic Classification using Medical Image Computing”. IEEE Journal of Biomedical and Health Informatics. (In Press) Abstract Composite models that combine medical imaging with electronic medical records (EMR) improve...

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