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Updated: May 14, 2026
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Doppler Optical Coherence Tomography of Retinal Circulation
Published on: September 18, 2012
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Automatic artery/vein classification methods for retinal blood vessel: A review
Qihan Chen1, Jianqing Peng2, Shen Zhao1
1School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
Summary
This review covers automatic retinal arteriovenous classification methods from 2003-2022. Future research should integrate deep learning with topological information for improved early disease diagnosis.
Area of Science:
- Ophthalmology
- Medical Imaging
- Computer Science
Background:
- Automatic retinal arteriovenous classification aids ophthalmologists in early disease diagnosis.
- Deep learning and topological graph-based methods are leading approaches.
- A comprehensive review of methods from 2003 to 2022 is presented.
Purpose of the Study:
- To review and compare automatic retinal arteriovenous classification methods.
- To analyze public datasets and annotation development.
- To establish a comprehensive evaluation system for classification challenges.
Main Methods:
- Comparative analysis of various classification techniques.
- Classification of public datasets with annotation development tables.
- Development of a comprehensive evaluation system for classification challenges.
Main Results:
- Comparison tables summarizing the performance of different methods.
- Annotation development tables for public datasets.
- Identification of research trends and evolution over time.
Conclusions:
- The integration of deep learning with topological information is a significant future research direction.
- This review provides a foundation for understanding the evolution of retinal arteriovenous classification.
- The proposed evaluation system addresses current challenges in the field.
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