Body-Wise Category

Body Composition Assessment with Limited Field-of-view Computed Tomography: A Semantic Image Extension Perspective. Medical Image Analysis

Aug. 31, 2023—Kaiwen Xu, Thomas Li, Mirza S. Khan, Riqiang Gao, Sanja L. Antic, Yuankai Huo, Kim L. Sandler, Fabien Maldonado, Bennett A. Landman Paper: https://www.sciencedirect.com/science/article/pii/S1361841523001123 Code: https://github.com/MASILab/S-EFOV Abstract Field-of-view (FOV) tissue truncation beyond the lungs is common in routine lung screening computed tomography (CT). This poses limitations for opportunistic CT-based body composition (BC) assessment as key anatomical structures...

Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation

Aug. 31, 2023—Ho Hin Lee, Yucheng Tang, Qi Yang, Xin Yu, Leon Y. Cai, Lucas W. Remedios, Shunxing Bao, Bennett A. Landman, Yuankai Huo Paper: https://ieeexplore.ieee.org/document/10149329 Code: https://github.com/MASILab/DCC_CL Abstract Medical image segmentation, or computing voxel-wise semantic masks, is a fundamental yet challenging task in medical imaging domain. To increase the ability of encoder-decoder neural networks to perform this task...

Overview of our proposed pre-training framework.
Input CT images are randomly cropped into sub-volumes and
augmented with random inner cutout and rotation, then fed to the
Swin UNETR encoder as input. We use masked volume inpainting,
contrastive learning and rotation prediction as proxy tasks for
learning contextual representations of input images

Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis

Jul. 25, 2022—Tang, Yucheng, Dong Yang, Wenqi Li, Holger R. Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, and Ali Hatamizadeh. “Self-supervised pre-training of swin transformers for 3d medical image analysis.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20730-20740. 2022. Full text: Abstract Vision Transformers (ViT)s have shown great performance in self-supervised...

A representative subject was evaluated with URN (left) and BUSN (right). Green scatters are inliers of influence to the regression, yellow
scatters are outliers of no influence to the distributed data. Darker blue line indicates the normal linear regression on scatters points, lighter blue
line is the RANSAC regressor result according to inliers. Left panel presents the single URN regression with amounts of outliers result in failure of
linearity nature in chest and pelvis regions. Right panel shows the testing result of BUSN method, the distributed scores follows good linearity in
chest, abdomen and pelvis regions in CT scan. In summary, BUSN takes advantage of self-supervised network, which presents better continuity in
regression result among neighbor slices and shows scatter plots without number of outliers.

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...

Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records

Dec. 7, 2020—Yucheng Tang, Riqiang Gao, Ho Hin Lee, Quinn Stanton Wells, Ashley Spann, James Gregory Terry, Jeff Carr, Yuankai Huo, Shunxing Bao and Bennett A. Landman, “Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records”, MICCAI CLIP, 2020. Full Text: Abstract Type II diabetes mellitus (T2DM) is a significant public health concern...

Qualitative comparisons between body-wise and organ-wise approaches on spleen (red), right kidney (yellow), and left kidney (blue). Padded bounding boxes in correspondent colors indicate the ROIs for organ-wise registrations.

Evaluation of Body-Wise and Organ-Wise Registrations for Abdominal Organs

Apr. 15, 2016—Zhoubing Xu, Sahil A. Panjwani, Christopher P. Lee, Ryan P. Burke, Rebeccah B. Baucom, Benjamin K. Poulose, Richard G. Abramson, Bennett A. Landman, “Evaluation of Body-Wise and Organ-Wise Registrations for Abdominal Organs”, In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Oral presentation. Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4845963/ Abstract Identifying cross-sectional and...