A Novel Approach to Detect Outer Retinal Tubulation Using U-Net in SD-OCT Images

Optical Coherence Tomography (OCT) has become a basic non-invasive tool in diagnosing and following different types of eye diseases. This technique can produce high-resolution cross-sectional images of retinal layers. Outer retinal tubulation (ORT) is one of the detectable biomarker by SD-OCT. ORTs defined as hyporeflective, tubular structures with hyperreflective borders or reversed within the retina and appear in many retinal diseases, including age-related macular degeneration (AMD). Our aim is to develop an automatic method that can efficiently characterize ORT biomarker. Detection of this biomarker can be challenging because of its variable size, location, and reflectivity. In this paper, we present a fully convolutional U-Net based architecture to detect ORT. The proposed approach is evaluated using a dataset annotated by ophthalmologists. One of the main challenges was the limited amount of training data that we resolve with real-time augmentation during training and using nested cross-validation. Our method achieved near human performance reaching an overall object-based recall score of 0.847 and Dice score of 0.579 on the test set.