Accurate 3D lesion segmentation in optical coherence tomography (OCT) images is crucial for the detection and treatment evaluation of diabetic macular edema (DME), but is hindered by scarce labeled data and complex boundaries. To address these challenges, we propose a semi-supervised 3D DME segmentation method that leverages a dual independent-branch co-training framework with mask perturbation consistency. The dual branches jointly exploit complementary multi-view features, while an independence-measuring strategy encourages parameter divergence to break performance bottlenecks. To improve robustness against diverse lesion morphologies, we introduce dynamic region-mask augmentation and a bidirectional confidence filtering mechanism, enforcing strong consistency constraints between the two branches. Additionally, a lightweight channel-attention module integrates multi-scale context. Extensive experiments demonstrate that our method surpasses state-of-the-art approaches, validating its effectiveness and superiority.
Liu et al. (2026) studied this question.