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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 …