Striving for Simplicity: Simple Yet Effective Prior-Aware Pseudo-Labeling for Semi-Supervised Ultrasound Image Segmentation

• URL: http://arxiv.org/abs/2503.13987v1
• Date: Tue, 18 Mar 2025 07:44:09 GMT
• Title: Striving for Simplicity: Simple Yet Effective Prior-Aware Pseudo-Labeling for Semi-Supervised Ultrasound Image Segmentation
• Authors: Yaxiong Chen, Yujie Wang, Zixuan Zheng, Jingliang Hu, Yilei Shi, Shengwu Xiong, Xiao Xiang Zhu, Lichao Mou,
• Abstract summary: Medical ultrasound imaging is ubiquitous, but manual analysis struggles to keep pace.<n>Semi-supervised learning leveraging both unlabeled and limited labeled data is a promising approach.<n>We present a simple yet effective pseudo-labeling method with an adversarially learned shape prior to regularize segmentations.
• Score: 40.08784389029472
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical ultrasound imaging is ubiquitous, but manual analysis struggles to keep pace. Automated segmentation can help but requires large labeled datasets, which are scarce. Semi-supervised learning leveraging both unlabeled and limited labeled data is a promising approach. State-of-the-art methods use consistency regularization or pseudo-labeling but grow increasingly complex. Without sufficient labels, these models often latch onto artifacts or allow anatomically implausible segmentations. In this paper, we present a simple yet effective pseudo-labeling method with an adversarially learned shape prior to regularize segmentations. Specifically, we devise an encoder-twin-decoder network where the shape prior acts as an implicit shape model, penalizing anatomically implausible but not ground-truth-deviating predictions. Without bells and whistles, our simple approach achieves state-of-the-art performance on two benchmarks under different partition protocols. We provide a strong baseline for future semi-supervised medical image segmentation. Code is available at https://github.com/WUTCM-Lab/Shape-Prior-Semi-Seg.

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