Wide-field OCT volumetric segmentation using semi-supervised CNN and transformer integration.

ABSTRACT: Wide-field optical coherence tomography (OCT) imaging can enable monitoring of peripheral changes in the retina, beyond the conventional fields of view used in current clinical OCT imaging systems. However, wide-field scans can present significant challenges for retinal layer segmentation. Deep Convolutional Neural Networks (CNNs) have shown strong performance in medical imaging segmentation but typically require large-scale, high-quality, pixel-level annotated datasets to be effectively developed. To address this challenge, we propose an advanced semi-supervised learning framework that combines the detailed capabilities of convolutional networks with the broader perspective of transformers. This method efficiently leverages labelled and unlabelled data to reduce dependence on extensive, m

SUBMITTER: Sreng S 

PROVIDER: S-EPMC11850926 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature