A Comparative Constraint Deep Learning Model for Blood Vessel Segmentation in OCTA Images

Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique widely used in clinical practice for retinal blood vessel imaging. Indicators of retinal blood vessel such as density, diameter, and curvature are closely associated with various retinal diseases, including Diabetic Retinopathy (DR) and Age-related Macular Degeneration (AMD). Obtaining these indicators relies on accurate blood vessel segmentation. Therefore, blood vessel segmentation serves as the foundation for automated diagnosis of retinal diseases. However, blood vessel segmentation from OCTA images is often affected by some noises, such as Gaussian noise and shadow noise. This study proposes a novel deep learning model called the comparative constraint model. “Comparative” means comparing the outputs of multiple models, while “constraint” means restricting the model produce the same output for images with different noises. The key idea is to train the model to produce consistent segmentation results for images with and without added noise, thereby enhancing the noise resistance of model. The effectiveness of the proposed method has been validated on a publicly available dataset, OCTA-500-6M, achieving an average Dice of $\mathbf{8 8. 5 8 \%}$ and an average $\mathbf{I o U}$ of $\mathbf{7 9. 6 0 \%}$. These results are significantly better than those obtained using a standard U-Net.