Abstract
Accurate 3D segmentation of fluid lesions in optical coherence tomography (OCT) is crucial for the early diagnosis of diabetic macular edema (DME). However, higher-dimensional spatial complexity and limited annotated data present significant challenges for effective 3D lesion segmentation. To address these issues, we propose a novel semi-supervised strategy using a correlation mutual learning framework for segmenting 3D DME lesions from 3D OCT images. Our method integrates three key innovations: (1) a shared encoder with three parallel, slightly different decoders, exhibiting cognitive biases and calculating statistical discrepancies among the decoders to represent uncertainty in unlabeled challenging regions. (2) a global reasoning attention module integrated into the encoder's output to transfer label prior knowledge to unlabeled data; and (3) a correlation mutual learning scheme, enforcing mutual consistency between one decoder's probability map and the soft pseudo labels generated by the other decoders. Extensive experiments demonstrate that our approach outperforms state-of-the-art (SOTA) methods, highlighting the potential of our framework for tackling the complex task of 3D retinal lesion segmentation.