用少量标注数据实现内窥镜视频精准分割，提升医疗影像分析可靠性。
Endo-SemiS: Towards Robust Semi-Supervised Image Segmentation for Endoscopic Video
- 双网络交叉监督+不确定性筛选伪标签，提升未标注数据利用效率。
- 联合伪标签与互学习机制，在有限标注下达到最佳分割效果。
- 专为内窥镜视频设计，适合医疗图像分割场景的科研与临床应用。
本文提出 Endo-SemiS，一种面向内窥镜视频的半监督分割框架，旨在以极少标注实现可靠分割。该框架采用四种策略：(1) 双网络相互交叉监督；(2) 基于不确定性的伪标签生成，仅保留高置信度区域提升质量；(3) 联合伪标签监督，融合两网络伪标签中的可靠像素提供精准监督；(4) 特征级与图像级互学习，降低方差并引导模型收敛至一致解。此外，引入独立的时序矫正网络，利用内窥镜视频的时空信息增强分割性能。在尿路镜碎石与结肠镜息肉筛查两个临床任务上评估，相比现有最优方法，Endo-SemiS 在标注数据有限条件下显著提升性能。代码已公开于 https://github.com/MedICL-VU/Endo-SemiS。
原文摘要 · Abstract (English)
In this paper, we present Endo-SemiS, a semi-supervised segmentation framework for providing reliable segmentation of endoscopic video frames with limited annotation. EndoSemiS uses 4 strategies to improve performance by effectively utilizing all available data, particularly unlabeled data: (1) Cross-supervision between two individual networks that supervise each other; (2) Uncertainty-guided pseudo-labels from unlabeled data, which are generated by selecting high-confidence regions to improve their quality; (3) Joint pseudolabel supervision, which aggregates reliable pixels from the pseudo-labels of both networks to provide accurate supervision for unlabeled data; and (4) Mutual learning, where both networks learn from each other at the feature and image levels, reducing variance and guiding them toward a consistent solution. Additionally, a separate corrective network that utilizes spatiotemporal information from endoscopy video to improve segmentation performance. Endo-SemiS is evaluated on two clinical applications: kidney stone laser lithotomy from ureteroscopy and polyp screening from colonoscopy. Compared to state-of-the-art segmentation methods, Endo-SemiS substantially achieves superior results on both datasets with limited labeled data. The code is publicly available at https://github.com/MedICL-VU/Endo-SemiS
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