Retinal Fluids Segmentation Using Volumetric Deep Neural Networks on Optical Coherence Tomography Scans

Retinal diseases are among the significant reasons for vision loss worldwide. Age-related macular degeneration (AMD) influences older people, and 170 million individuals are diagnosed with AMD on the global level. This number is expected to increase to 288 million people by 2040. Optical coherence tomography (OCT) is the most effective and noninvasive modality to view the retinal layers. The frequent visit of patients affected with retinal diseases raised the need for developing automatic algorithms to localize and quantity the morphological changes occurring in the retina. Deep learning networks show excellent performance in classifying 2D scans at the image level and pixel level. Medical data is usually obtained with depth information, and using only 2D information could lead to lower accuracy in localizing the fluid volume size. Mimicking human performance in manually locating the diseases over medical images is the main target of automatic methods to exceed. In this study, we have used the RETOUCH challenge dataset to segment various retinal fluids. Human performance reported in the challenge scored 0.71 in the dice similarity coefficient (DSC) metric. Encoder-decoder network is demonstrated in a 3D manner for the retinal disease segmentation, and the average performance score is 0.73 in the dice metric from the Cirrus scanner data. The dataset released three different fluids, and intraretinal fluid (IRF) is more identified with 0.79 in the DSC metric.