Automatic measurement of choroidal thickness and vasculature in optical coherence tomography images of eyes with retinitis pigmentosa

Retinitis pigmentosa (RP) is a group of genetic disorders, characterized by degeneration of photoreceptor cells which is the main cause of choroidal thinning. It is one of the leading causes of blindness worldwide. Thus, an investigation of choroidal changes is required for a better understanding of disease and diagnosis of RP. In this paper, we propose an automatic technique for measuring the choroidal parameters in optical coherence tomography (OCT) images of eyes with RP. The parameters include the total choroidal area (TCA), luminal area (LA), stromal area (SA), and choroidal thickness (CT). We applied our recently proposed, dense dilated U-Net segmentation model, called ChoroidNET, for segmenting the choroid layer and choroidal vessels for our RP dataset. Choroid segmentation is an important task since the measurement results depend on it. Comparison with other state-of-the-art models shows that ChoroidNET provides a better quantitative and qualitative segmentation of the choroid layer and choroidal vessels. Next, we measure the choroidal parameters based on the segmentation results of ChoroidNET. The proposed method achieves high reliability with an intraclass correlation coefficient (0.961, 0.940, 0.826, 0.916) for TCA, LA, SA, and CT, respectively.