A Deep Learning System for Fully Automated Retinal Vessel Measurement in High Throughput Image Analysis

Danli Shi; Zhihong Lin; Wei Wang; Zachary Tan; Xianwen Shang; Xueli Zhang; Wei Meng; Zongyuan Ge; Mingguang He

doi:10.3389/fcvm.2022.823436

A Deep Learning System for Fully Automated Retinal Vessel Measurement in High Throughput Image Analysis

Danli Shi, Zhihong Lin, Wei Wang, Zachary Tan, Xianwen Shang, Xueli Zhang, Wei Meng, Zongyuan Ge, Mingguang He (Corresponding Author)

Research output: Journal article publication › Journal article › Academic research › peer-review

56 Citations (Scopus)

Abstract

Motivation: Retinal microvasculature is a unique window for predicting and monitoring major cardiovascular diseases, but high throughput tools based on deep learning for in-detail retinal vessel analysis are lacking. As such, we aim to develop and validate an artificial intelligence system (Retina-based Microvascular Health Assessment System, RMHAS) for fully automated vessel segmentation and quantification of the retinal microvasculature. Results: RMHAS achieved good segmentation accuracy across datasets with diverse eye conditions and image resolutions, having AUCs of 0.91, 0.88, 0.95, 0.93, 0.97, 0.95, 0.94 for artery segmentation and 0.92, 0.90, 0.96, 0.95, 0.97, 0.95, 0.96 for vein segmentation on the AV-WIDE, AVRDB, HRF, IOSTAR, LES-AV, RITE, and our internal datasets. Agreement and repeatability analysis supported the robustness of the algorithm. For vessel analysis in quantity, less than 2 s were needed to complete all required analysis.

Original language	English
Article number	823436
Pages (from-to)	1-12
Number of pages	12
Journal	Frontiers in Cardiovascular Medicine
Volume	9
DOIs	https://doi.org/10.3389/fcvm.2022.823436
Publication status	Published - 22 Mar 2022
Externally published	Yes

Keywords

artificial intelligence
automated analysis
cardiovascular disease
epidemiology
hierarchical vessel morphology

ASJC Scopus subject areas

Cardiology and Cardiovascular Medicine

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

More information

10.3389/fcvm.2022.823436

Cite this

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title = "A Deep Learning System for Fully Automated Retinal Vessel Measurement in High Throughput Image Analysis",

abstract = "Motivation: Retinal microvasculature is a unique window for predicting and monitoring major cardiovascular diseases, but high throughput tools based on deep learning for in-detail retinal vessel analysis are lacking. As such, we aim to develop and validate an artificial intelligence system (Retina-based Microvascular Health Assessment System, RMHAS) for fully automated vessel segmentation and quantification of the retinal microvasculature. Results: RMHAS achieved good segmentation accuracy across datasets with diverse eye conditions and image resolutions, having AUCs of 0.91, 0.88, 0.95, 0.93, 0.97, 0.95, 0.94 for artery segmentation and 0.92, 0.90, 0.96, 0.95, 0.97, 0.95, 0.96 for vein segmentation on the AV-WIDE, AVRDB, HRF, IOSTAR, LES-AV, RITE, and our internal datasets. Agreement and repeatability analysis supported the robustness of the algorithm. For vessel analysis in quantity, less than 2 s were needed to complete all required analysis.",

keywords = "artificial intelligence, automated analysis, cardiovascular disease, epidemiology, hierarchical vessel morphology",

author = "Danli Shi and Zhihong Lin and Wei Wang and Zachary Tan and Xianwen Shang and Xueli Zhang and Wei Meng and Zongyuan Ge and Mingguang He",

note = "Publisher Copyright: Copyright {\textcopyright} 2022 Shi, Lin, Wang, Tan, Shang, Zhang, Meng, Ge and He.",

year = "2022",

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doi = "10.3389/fcvm.2022.823436",

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T1 - A Deep Learning System for Fully Automated Retinal Vessel Measurement in High Throughput Image Analysis

AU - Shi, Danli

AU - Lin, Zhihong

AU - Wang, Wei

AU - Tan, Zachary

AU - Shang, Xianwen

AU - Zhang, Xueli

AU - Meng, Wei

AU - Ge, Zongyuan

AU - He, Mingguang

PY - 2022/3/22

Y1 - 2022/3/22

N2 - Motivation: Retinal microvasculature is a unique window for predicting and monitoring major cardiovascular diseases, but high throughput tools based on deep learning for in-detail retinal vessel analysis are lacking. As such, we aim to develop and validate an artificial intelligence system (Retina-based Microvascular Health Assessment System, RMHAS) for fully automated vessel segmentation and quantification of the retinal microvasculature. Results: RMHAS achieved good segmentation accuracy across datasets with diverse eye conditions and image resolutions, having AUCs of 0.91, 0.88, 0.95, 0.93, 0.97, 0.95, 0.94 for artery segmentation and 0.92, 0.90, 0.96, 0.95, 0.97, 0.95, 0.96 for vein segmentation on the AV-WIDE, AVRDB, HRF, IOSTAR, LES-AV, RITE, and our internal datasets. Agreement and repeatability analysis supported the robustness of the algorithm. For vessel analysis in quantity, less than 2 s were needed to complete all required analysis.

AB - Motivation: Retinal microvasculature is a unique window for predicting and monitoring major cardiovascular diseases, but high throughput tools based on deep learning for in-detail retinal vessel analysis are lacking. As such, we aim to develop and validate an artificial intelligence system (Retina-based Microvascular Health Assessment System, RMHAS) for fully automated vessel segmentation and quantification of the retinal microvasculature. Results: RMHAS achieved good segmentation accuracy across datasets with diverse eye conditions and image resolutions, having AUCs of 0.91, 0.88, 0.95, 0.93, 0.97, 0.95, 0.94 for artery segmentation and 0.92, 0.90, 0.96, 0.95, 0.97, 0.95, 0.96 for vein segmentation on the AV-WIDE, AVRDB, HRF, IOSTAR, LES-AV, RITE, and our internal datasets. Agreement and repeatability analysis supported the robustness of the algorithm. For vessel analysis in quantity, less than 2 s were needed to complete all required analysis.

KW - artificial intelligence

KW - automated analysis

KW - cardiovascular disease

KW - epidemiology

KW - hierarchical vessel morphology

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U2 - 10.3389/fcvm.2022.823436

DO - 10.3389/fcvm.2022.823436

M3 - Journal article

AN - SCOPUS:85138498788

SN - 2297-055X

VL - 9

SP - 1

EP - 12

JO - Frontiers in Cardiovascular Medicine

JF - Frontiers in Cardiovascular Medicine

M1 - 823436

ER -

A Deep Learning System for Fully Automated Retinal Vessel Measurement in High Throughput Image Analysis

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

More information

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