用简单方法提升超声图像分割准确率，避免不合理的解剖结构。
Striving for Simplicity: Simple Yet Effective Prior-Aware Pseudo-Labeling for Semi-Supervised Ultrasound Image Segmentation
- 设计双编码器-解码器网络，用形状先验约束分割结果。
- 在两个数据集上达到当前最佳性能，标签量少时仍稳定有效。
- 适合需要高精度且标注稀缺的医学图像分割场景。
医学超声成像广泛应用，但人工分析难以跟上需求。自动化分割可缓解问题，却依赖大量标注数据，而这类数据稀缺。半监督学习利用少量标注与大量未标注数据是可行方案。现有方法多采用一致性正则化或伪标签，但日趋复杂，且在标注不足时易捕捉伪影或生成不合解剖逻辑的分割结果。本文提出一种简单有效的伪标签方法，引入对抗学习的形状先验以约束分割形态。具体而言，构建一个编码器-双解码器网络，其中形状先验作为隐式形状模型，惩罚不符合解剖结构但不偏离真实标签的预测。无需复杂模块，该方法在两种基准数据集上、不同划分协议下均取得当前最优表现，为未来半监督医学图像分割提供可靠基线。代码已公开于 https://github.com/WUTCM-Lab/Shape-Prior-Semi-Seg。
原文摘要 · Abstract (English)
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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