Gabor-modulated depth separable convolution for retinal vessel segmentation in fundus images.
Journal:
Computers in biology and medicine
PMID:
39946785
Abstract
BACKGROUND: In diabetic retinopathy, precise segmentation of retinal vessels is essential for accurate diagnosis and effective disease management. This task is particularly challenging due to the varying sizes of vessels, their bifurcations, and the presence of highly curved segments. While numerous automated segmentation techniques have demonstrated strong performance, deep neural networks have struggled to effectively model the geometric transformations of retinal vessels without extensive training datasets. Moreover, the inconsistent quality of fundus photographs often results in less than satisfactory accuracy in vessel structure detection.