Joint Segmentation and Quantification of Chorioretinal Biomarkers in Optical Coherence Tomography Scans: A Deep Learning Approach

In ophthalmology, chorioretinal biomarkers (CRBMs) play a significant role in detecting, quantifying, and ameliorating the treatment of chronic eye conditions. Optical coherence tomography (OCT) imaging is primarily used for investigating various CRBMs and prompt intervention of retinal conditions. However, with extensive clinical applications and increasing prevalence of ocular diseases, the number of OCT scans obtained globally exceeds ophthalmologists’ capacity to examine these in a meaningful manner. Instead, the emergence of deep learning provides a cost-effective and reliable alternative for automated analysis of scans, assisting ophthalmologists in clinical routines and research. This article presents a residual learning-based framework (RASP-Net) that integrates atrous spatial pyramid pooling, coherent preprocessing, and postprocessing mechanisms to achieve joint segmentation and quantification of 11 CRBMs. We used a total of 7000 annotated scans for training, validation, and testing purposes of RASP-Net. Moreover, a novel algorithm for 3-D macular profiles reconstruction is presented to give a more intuitive way for characterizing the CRBMs based on coarse contouring and quantification. The proposed framework is evaluated through several experiments using different performance metrics. The results presented in this study validate the optimal performance of RASP-Net in precise detection and segmentation of CRBMs, with mean balanced accuracy, intersection over union, and dice score values of 0.916, 0.634, and 0.776 respectively. The proposed RASP-Net model characterizes a wide range of CRBMs with fine-grained pixelwise segmentation, extraction, and quantification in the context of retinal pathologies. This proposed system can allow retina experts to monitor the improvement and deterioration of the underlying ocular conditions.