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General retinal layer segmentation in OCT images via reinforcement constraint
Jinbao Hao1, Huiqi Li1, Shuai Lu1
1Beijing Institute of Technology, No. 5, Zhong Guan Cun South Street, Beijing, 100081, China.
Summary
A new general retinal layer segmentation method improves accuracy for ocular disease diagnosis. This approach enhances boundary detection and handles varied retinal OCT image data for consistent performance.
Area of Science:
- Ophthalmology
- Medical Imaging
- Computer Vision
Background:
- Retinal layer thickness changes are linked to ocular diseases like glaucoma.
- Optical coherence tomography (OCT) visualizes retinal structures, crucial for diagnosis.
- Existing segmentation methods lack consistent performance across diverse OCT datasets.
Purpose of the Study:
- To develop a general retinal layer segmentation method for consistent performance.
- To improve segmentation accuracy and reliability in retinal OCT imaging.
- To address challenges posed by varied datasets and disease interferences.
Main Methods:
- A feature-enhanced decoding module with reinforcement constraint for smoother boundaries.
- Position channel attention to capture global spatial and channel dependencies.
- Focal loss to address imbalanced data distribution in retinal OCT images.
Main Results:
- The proposed method achieves state-of-the-art (SOTA) performance.
- Consistent segmentation across five diverse datasets (MGU, DUKE, NR206, OCTA500, private).
- Improved perception of slender retinal structures and boundary continuity.
Conclusions:
- The developed method offers a robust solution for retinal layer segmentation.
- It enhances diagnostic capabilities for ocular diseases using OCT imaging.
- The approach demonstrates generalizability across different retinal OCT datasets.
