Global-Local Transformer Network for Automatic Retinal Pathological Fluid Segmentation in Optical Coherence Tomography Images.
Journal:
Computer methods and programs in biomedicine
PMID:
40228373
Abstract
BACKGROUND AND OBJECTIVE: As a pivotal biomarker, the accurate segmentation of retinal pathological fluid such as intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED), was a critical task for diagnosis and treatment management in various retinopathy. However, segmenting pathological fluids from optical coherence tomography (OCT) images still faced several challenges, including large variations in location, size and shape, low intensity contrast between fluids and peripheral tissues, speckle noise interference, and high similarity between fluid and background. Further, owing to the intrinsic local nature of convolution operations, most automatic retinal fluid segmentation approaches built upon deep convolutional neural network had limited capacity in capturing pathological features with global dependencies, prone to deviations. Accordingly, it was of great significance to develop automatic methods for accurate segmentation and quantitative analysis on multi-type retinal fluids in OCT images.