Endo-SemiS: Towards Robust Semi-Supervised Image Segmentation for Endoscopic Video
This work addresses the challenge of accurate medical image segmentation with scarce labeled data, which is critical for clinical applications like endoscopy, though it appears incremental as it builds on existing semi-supervised techniques.
The paper tackled the problem of reliable segmentation of endoscopic video frames with limited annotation by proposing Endo-SemiS, a semi-supervised framework that achieved superior results on kidney stone laser lithotomy and polyp screening datasets compared to state-of-the-art methods.
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