Year: 2024
Type: conference-paper
Abstract: Retinal artery/vein (A/V) segmentation is an important step in screening various diseases. However, accurate retinal A/V segmentation is still a challenging task, and even state-of-the-art methods often fail to respect the correct topology of the vascular structure, which is crucial for clinical application. For better preservation of topology and connectivity in fully automated A/V segmentation, we propose a two-stage deep learning framework that consists of a multi-task U-Net with a customized vessel aggregation module for initial A/V segmentation, and a vascular topology rectification network based on persistent homology. Extensive experiments on public DRIVE-AV and HRF-AV datasets show that the proposed method delivers competitive pixel-level accuracy and the best topology-level accuracy against state-of-the-art methods.
Institution: UNSW Sydney
Language: English
Topic: Retinal Imaging and Analysis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4401751671