Weakly supervised learning for subcutaneous edema segmentation of abdominal CT using pseudo-labels and multi-stage nnU-Nets

Sayantan Bhadra; Jianfei Liu; Ronald M. Summers

doi:10.1117/12.3008793

3 April 2024 Weakly supervised learning for subcutaneous edema segmentation of abdominal CT using pseudo-labels and multi-stage nnU-Nets

Sayantan Bhadra, Jianfei Liu, Ronald M. Summers

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 1292738 (2024) https://doi.org/10.1117/12.3008793
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Volumetric assessment of edema due to anasarca can help monitor the progression of diseases such as kidney, liver or heart failure. The ability to measure edema non-invasively by automatic segmentation from abdominal CT scans may be of clinical importance. The current state-of-the-art method for edema segmentation using intensity priors is susceptible to false positives or under-segmentation errors. The application of modern supervised deep learning methods for 3D edema segmentation is limited due to challenges in manual annotation of edema. In the absence of accurate 3D annotations of edema, we propose a weakly supervised learning method that uses edema segmentations produced by intensity priors as pseudo-labels, along with pseudo-labels of muscle, subcutaneous and visceral adipose tissues for context, to produce more refined segmentations with demonstrably lower segmentation errors. The proposed method employs nnU-Nets in multiple stages to produce the final edema segmentation. The results demonstrate the potential of weakly supervised learning using edema and tissue pseudo-labels in improved quantification of edema for clinical applications.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Sayantan Bhadra, Jianfei Liu, and Ronald M. Summers "Weakly supervised learning for subcutaneous edema segmentation of abdominal CT using pseudo-labels and multi-stage nnU-Nets", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 1292738 (3 April 2024); https://doi.org/10.1117/12.3008793

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