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Medical-image Analysis and Statistical Interpretation Lab

‘multi-organ segmentation’

Method framework. Given a CT scan with at high resolution of ~0.8 x ~0.8 x ~2 mm, a low-res section (left panel) is trained with multi-channel segmentation. The low-res part contains down-sampling and normalization in order to preserve the complete spatial information. After the coarse segmentations are acquired from low- res UNet, we interpolate the mask to match the image’s original resolution. Next, random patch sampling (mid panel) is employed to collect patches, and patches are concatenated with corresponding coarse segmentation masks. Finally, we trained a patch-based high-res (right panel) segmentation model, the high-dimensional probability maps are acquired from integration of all patches on field of views. Majority vote is used to merge estimates into a final segmentation.

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...

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Multi-organ segmentation of research cohorts compared to clinical imaging archives. Scans in research cohorts are well- controlled and have been shown to allow for effective segmentation, whereas clinical scans display wide variability. Clinical scans display tumors, liver disease, interventions, etc., which make automatic multi-organ segmentation challenging

Validation and Optimization of Multi-Organ Segmentation on Clinical Imaging Archives

Feb. 12, 2020—Olivia Tang, Yuchen Xu, Yucheng Tang, Ho Hin Lee, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman, “Validation and Optimization of Multi-Organ Segmentation on Clinical Imaging Archives”, SPIE IP:MI 2020. Houston, TX. https://arxiv.org/abs/2002.04102 Abstract Segmentation of abdominal computed tomography (CT) provides spatial context, morphological...

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Examples of segmentations generated by the baseline algorithm. A. An inlier, where the algorithm correctly predicted larger organs, like the liver (on the left side of image in purple) and spleen (on the right side of image in red- pink) and suggested mostly accurate areas of smaller organs. B. An outlier (global failure), where the liver (purple) and spleen (red-pink) were largely correct but major inconsistencies are visible with other organs.

Outlier Guided Optimization of Abdominal Segmentation

Feb. 12, 2020—Yuchen Xu*, Olivia Tang*, Yucheng Tang, Ho Hin Lee, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman, “Outlier Guided Optimization of Abdomen Segmentation”, SPIE IP:MI 2020. Houston, TX https://arxiv.org/abs/2002.04098 Abstract Abdominal multi-organ segmentation of computed tomography (CT) images has been the subject of extensive...

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