Deep Learning with Automatic Data Augmentation for Segmenting Schisis Cavities in the Optical Coherence Tomography Images of X-Linked Juvenile Retinoschisis Patients.

ABSTRACT: X-linked juvenile retinoschisis (XLRS) is an inherited disorder characterized by retinal schisis cavities, which can be observed in optical coherence tomography (OCT) images. Monitoring disease progression necessitates the accurate segmentation and quantification of these cavities; yet, current manual methods are time consuming and result in subjective interpretations, highlighting the need for automated and precise solutions. We employed five state-of-the-art deep learning models-U-Net, U-Net++, Attention U-Net, Residual U-Net, and TransUNet-for the task, leveraging a dataset of 1500 OCT images from 30 patients. To enhance the models' performance, we utilized data augmentation strategies that were optimized via deep reinforcement learning. The deep learning models achieved a human-equival

SUBMITTER: Wei X 

PROVIDER: S-EPMC10572414 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature