DRFNet: a deep radiomic fusion network for nAMD/PCV differentiation in OCT images

Abstract Objective. Neovascular age-related macular degeneration (nAMD) and polypoidal choroidal vasculopathy (PCV) present many similar clinical features. However, there are significant differences in the progression of nAMD and PCV. and it is crucial to make accurate diagnosis for treatment. In this paper, we propose a structure-radiomic fusion network (DRFNet) to differentiate PCV and nAMD in optical coherence tomography (OCT) images. Approach. The subnetwork (RIMNet) is designed to automatically segment the lesion of nAMD and PCV. Another subnetwork (StrEncoder) is designed to extract deep structural features of the segmented lesion. The subnetwork (RadEncoder) is designed to extract radiomic features from the segmented lesions based on radiomics. 305 eyes (155 with nAMD and 150 with PCV) are included and manually annotated CNV region in this study. The proposed method was trained and evaluated by 4-fold cross validation using the collected data and was compared with the advanced differentiation methods. Main results. The proposed method achieved high classification performace of nAMD/PCV differentiation in OCT images, which was an improvement of 4.68 compared with other best method. Significance . The presented structure-radiomic fusion network (DRFNet) has great performance of diagnosing nAMD and PCV and high clinical value by using OCT instead of indocyanine green angiography.