Evidence map›Paper›PMID 36522528›Full record

ArticleEye (London, England)2023

3D-FVS: construction and application of three-dimensional fundus vascular structure model based on single image features.

Zhaomin Yao, Renli Luo, Chencong Xing, Fei Li, Gancheng Zhu, Zhiguo Wang, Guoxu Zhang

Abstract read
In one paragraph

Article in Eye (London, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Zhaomin YaoCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China.
Renli LuoCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China.
Chencong XingSchool of Computer Science and Software Engineering, East China Normal University, Shanghai, 200241, China.
Fei LiCollege of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China.
Gancheng ZhuCollege of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China.
Zhiguo WangCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China. wangzhiguo5778@163.com.ORCID 0000-0003-3686-6220
Guoxu ZhangCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China. zhangguoxu_502@163.com.

Funding

Natural Science Foundation of Liaoning Province (Liaoning Provincial Natural Science Foundation) 2019JH2/10300010
6 · The paper itself

Abstract

backgroundFundus microvasculature may be visually observed by ophthalmoscope and has been widely used in clinical practice. Due to the limitations of available equipment and technology, most studies only utilized the two-dimensional planar features of the fundus microvasculature.

methodsThis study proposed a novel method for establishing the three-dimensional fundus vascular structure model and generating hemodynamic characteristics based on a single image. Firstly, the fundus vascular are segmented through our proposed network framework. Then, the length and width of vascular segments and the relationship among the adjacent segments are collected to construct the three-dimensional vascular structure model. Finally, the hemodynamic model is generated based on the vascular structure model, and highly correlated hemodynamic features are selected to diagnose the ophthalmic diseases.

resultsIn fundus vascular segmentation, the proposed network framework obtained 98.63% and 97.52% on Area Under Curve (AUC) and accuracy respectively. In diagnosis, the high correlation features extracted based on the proposed method achieved 95% on accuracy.

conclusionsThis study demonstrated that hemodynamic features filtered by relevance were essential for diagnosing retinal diseases. Additionally, the method proposed also outperformed the existing models on the levels of retina vessel segmentation. In conclusion, the proposed method may represent a novel way to diagnose retinal related diseases, which can analysis two-dimensional fundus pictures by extracting heterogeneous three-dimensional features.

Indexed as

AlgorithmsRetinal DiseasesFundus OculiHumansImage Processing, Computer-AssistedRetinal Vessels

Identifiers

PMID36522528
PMCPMC10397231

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.