Simultaneous segmentation of multiple retinal pathologies using fully convolutional deep neural network

Abstract

The segmentation of retinal pathologies is the primitive and essential step in the development of automated diagnostic system for various systemic, cardiovascular, and ophthalmic diseases. The existing state-of-the-art machine learning based retinal pathologic segmentation techniques mainly aim at delineating pathology of one kind only. In this context, we have proposed a novel end-to-end technique for simultaneous segmentation of multiple retinal pathologies (i.e., exudates, hemorrhages, and cotton-wool spots) using encoder-decoder based fully convolutional neural network architecture. Moreover, the task of retinal pathology extraction has been modeled as a semantic segmentation framework which enables us to obtain pixel-level class labels. The proposed algorithm has been evaluated on publically available Messidor dataset and achieved state-of-the-art mean accuracies of 99.24% (exudates), 97.86% (hemorrhages), and 88.65% (cotton-wool spots). The developed approach may aid in further optimization of pathology quantification module of the QUARTZ software which has been developed earlier by our research group.