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.
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Badar, M., Shahzad, M., Fraz, M.M. (2018). Simultaneous Segmentation of Multiple Retinal Pathologies Using Fully Convolutional Deep Neural Network. In: Nixon, M., Mahmoodi, S., Zwiggelaar, R. (eds) Medical Image Understanding and Analysis. MIUA 2018. Communications in Computer and Information Science, vol 894. Springer, Cham. https://doi.org/10.1007/978-3-319-95921-4_29
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