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Improved fully convolutional neuron networks on small retinal vessel segmentation using local phase as attention
Xihe Kuang1, Xiayu Xu2, Leyuan Fang3
1The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
Frontiers in Medicine
|March 20, 2023
Summary
This study introduces a novel deep learning method (UN-LPCOS) to improve the segmentation of small retinal vessels, crucial for early disease detection. The approach enhances diagnostic accuracy for conditions like diabetes and glaucoma.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Retinal vessel segmentation is vital for diagnosing diseases like diabetes, glaucoma, and hypertension.
- Current segmentation methods often overlook small retinal vessels, which are sensitive indicators of circulatory health and early disease.
- Accurate segmentation of small vessels is essential for timely diagnosis and disease warning.
Purpose of the Study:
- To develop an advanced method for accurate retinal vessel segmentation, with a specific focus on small vessels.
- To improve the early diagnosis and monitoring of systemic diseases through enhanced retinal image analysis.
- To introduce a new evaluation metric for assessing small vessel segmentation performance.
Main Methods:
- Combined unsupervised methods: local phase congruency (LPC) and orientation scores (OS).
- Integrated LPC and OS into a U-Net-based deep learning network with attention mechanisms (UN-LPCOS).
- Proposed a new metric, sensitivity on a small ship (Se), to specifically evaluate small vessel segmentation.
Main Results:
- The proposed UN-LPCOS method demonstrated remarkable ability in identifying and segmenting small retinal vessels.
- Achieved outstanding segmentation performance on both overall vessel structures and small vessels.
- Validated effectiveness on the DRIVE dataset and data from The Maastricht Study.
Conclusions:
- The UN-LPCOS method significantly improves retinal vessel segmentation, particularly for small vessels.
- This technique holds great potential for early disease detection and monitoring through enhanced retinal image analysis.
- The proposed Se metric provides a valuable tool for evaluating small vessel segmentation performance.
