Automatic segmentation of foveal avascular zone based on adaptive watershed algorithm in retinal optical coherence tomography angiography images

The size and shape of the foveal avascular zone (FAZ) have a strong positive correlation with several vision-threatening retinovascular diseases. The identification, segmentation and analysis of FAZ are of great significance to clinical diagnosis and treatment. We presented an adaptive watershed algorithm to automatically extract FAZ from retinal optical coherence tomography angiography (OCTA) images. For the traditional watershed algorithm, over-segmentation is the most common problem. FAZ is often incorrectly divided into multiple regions by redundant dams. This paper analyzed the relationship between the dams length and the maximum inscribed circle radius of FAZ, and proposed an adaptive watershed algorithm to solve the problem of over-segmentation. Here, 132 healthy retinal images and 50 diabetic retinopathy (DR) images were used to verify the accuracy and stability of the algorithm. Three ophthalmologists were invited to make quantitative and qualitative evaluations on the segmentation results of this algorithm. The quantitative evaluation results show that the correlation coefficients between the automatic and manual segmentation results are 0.945 (in healthy subjects) and 0.927 (in DR patients), respectively. For qualitative evaluation, the percentages of perfect segmentation (score of 3) and good segmentation (score of 2) are 99.4% (in healthy subjects) and 98.7% (in DR patients), respectively. This work promotes the application of watershed algorithm in FAZ segmentation, making it a useful tool for analyzing and diagnosing eye diseases.

Automated summary

This study presents an adaptive watershed algorithm for the automatic extraction of the foveal avascular zone (FAZ) from retinal optical coherence tomography angiography (OCTA) images. The algorithm solves the common problem of over-segmentation in traditional watershed algorithms. Evaluation results show high correlation coefficients and percentages of accurate segmentation, indicating the effectiveness of the algorithm for clinical diagnosis and treatment of eye diseases.