Semi-supervised 3D retinal fluid segmentation via correlation mutual learning with global reasoning attention

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.