Taking full advantage of uncertainty estimation: an uncertainty-assisted two-stage pipeline for multi-organ segmentation

Zhou Zheng; Masahiro Oda; Kazunari Misawa; Kensaku Mori

doi:10.1117/12.2612535

4 April 2022 Taking full advantage of uncertainty estimation: an uncertainty-assisted two-stage pipeline for multi-organ segmentation

Zhou Zheng, Masahiro Oda, Kazunari Misawa, Kensaku Mori

Author Affiliations +

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120333M (2022) https://doi.org/10.1117/12.2612535
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

This paper proposes an uncertainty-assisted two-stage pipeline for multi-organ (liver, spleen, and stomach) segmentation. Deep learning methods, especially the convolutional neural networks (CNNs), have been widely applied for multi-organ segmentation in abdominal CT images. However, most models for multi-organ segmentation ignore uncertainty analysis and do not explore the role of uncertainty information in profiting segmentation accuracy. In the first stage of our approach, we analyze the effects of test-time dropout (TTD), test-time augmentation (TTA), and the combination (TTA + TTD) on segmentation accuracy. We also obtain their corresponding uncertainty information at both voxel and structure levels; For the second stage, we propose a novel uncertaintyguided interactive unified level set framework to optimize the segmentation accuracy further efficiently. Our experiments were done on a locally collected dataset containing 400 CT cases. The validation results showed that: 1) utilizing and combining TTD and TTA could improve the baseline performance. TTA, in particular, could boost the baseline performance by a large margin (Dice: 79.66% to 90.95%, ASD: 5.92 to 1.28 voxels), outperforming the state-of-the-art methods, and its corresponding aleatoric uncertainty could provide a better uncertainty estimation than TTD based epistemic uncertainty and contributes to reducing mis-segmentation; 2) Compared with the mainstream interactive algorithms, the proposed level set framework obtained competitive results and required about 76.78% fewer user interaction scribbles in the meanwhile.

Conference Presentation

Citation Download Citation

Zhou Zheng, Masahiro Oda, Kazunari Misawa, and Kensaku Mori "Taking full advantage of uncertainty estimation: an uncertainty-assisted two-stage pipeline for multi-organ segmentation", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120333M (4 April 2022); https://doi.org/10.1117/12.2612535

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