Dynamic Statistical Attention-based lightweight model for Retinal Vessel Segmentation: DyStA-RetNet.
Journal:
Computers in biology and medicine
Published Date:
Dec 28, 2024
Abstract
BACKGROUND AND OBJECTIVE: Accurate extraction of retinal vascular components is vital in diagnosing and treating retinal diseases. Achieving precise segmentation of retinal blood vessels is challenging due to their complex structure and overlapping vessels with other anatomical features. Existing deep neural networks often suffer from false positives at vessel branches or missing fragile vessel patterns. Also, deployment of the existing models in resource-constrained environments is challenging due to their computational complexity. An attention-based and computationally efficient architecture is proposed in this work to bridge this gap while enabling improved segmentation of retinal vascular structures.