Dual-Model Semi-Supervised Anterior Segment Structure Segmentation Using Mamba

Dong Ouyang, Xiaoming Liu, Ying Zhang, Guohuan Wu, Jinshan Tang

Abstract: Accurate segmentation of key anatomical structures in anterior segment OCT (AS-OCT) images is critical for diagnosing serious ophthalmic conditions such as keratitis and cataract. However, due to the scarcity of labeled data in this domain, most existing methods struggle to precisely segment both the lens and the anterior chamber angle simultaneously. To address these limitations, we propose a semi-supervised segmentation framework based on collaborative training between U-Net and Mamba-UNet. A Scale Fusion Module (SFM) is introduced to integrate the outputs of both models, generating multi-scale predictions and fused pseudo-labels. A multi-scale supervision strategy is then employed to guide learning at different levels. Additionally, we design a novel anatomical structure consistency loss that leverages anatomical properties from the fused pseudo-labels to preserve anatomical correctness. Experimental results on two AS-OCT datasets demonstrate the effectiveness and superiority of our proposed approach.

External IDs:dblp:conf/smc/OuyangLZWT25