AutoMorph: Automated Retinal Vascular Morphology Quantification Via a Deep Learning Pipeline.

ABSTRACT:

Purpose

To externally validate a deep learning pipeline (AutoMorph) for automated analysis of retinal vascular morphology on fundus photographs. AutoMorph has been made publicly available, facilitating widespread research in ophthalmic and systemic diseases.

Methods

AutoMorph consists of four functional modules: image preprocessing, image quality grading, anatomical segmentation (including binary vessel, artery/vein, and optic disc/cup segmentation), and vascular morphology feature measurement. Image quality grading and anatomical segmentation use the most recent deep learning techniques. We employ a model ensemble strategy to achieve robust results and analyze the prediction confidence to rectify false gradable cases in image quality grading. We externally validate the perf

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC9290317 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature