Deep Learning Models for Retinal Blood Vessels Segmentation: A Review

Toufique Ahmed Soomro, Ahmed J. Afifi, Lihong Zheng, Shafiullah Soomro, Junbin Gao, Olaf Hellwich, Manoranjan Paul

IEEE Access 2019 article cited by 155

Source Open-access PDF More in Medicine & Health

importance 6/10 ai assisted Retinal Imaging and Analysis ophthalmology stage: research

Problem

Diagnosing diabetic retinopathy requires labor-intensive manual analysis of retinal blood vessels by ophthalmologists.

Why it matters

Diabetic retinopathy can lead to blindness without proper clinical diagnosis and treatment.

What the AI did

This is a review paper surveying deep learning models that segment retinal blood vessels from images, with improved performance over manual procedures.

Results

not stated in source (only qualitative 'improved performance over manual procedures')

Impact

Evidence supports that deep learning has been successfully implemented in retinal image analysis; no metrics given.

Record

Venue	IEEE Access
Published	2019
Work type	article
Authors	Toufique Ahmed Soomro, Ahmed J. Afifi, Lihong Zheng, Shafiullah Soomro, Junbin Gao, Olaf Hellwich, Manoranjan Paul
Citations	155 (OpenAlex, at import time)
References	97
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1109/access.2019.2920616
Open access	gold
Keywords	deep learning, retinal blood vessels, segmentation, diabetic retinopathy, review

Importance rating

6 / 10

significant — thousands to millions affected

Diabetic retinopathy can cause blindness without diagnosis; manual ophthalmologist analysis is labor-intensive. No scale or metrics stated in abstract.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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