Towards Complete Ocular Disease Diagnosis in Color Fundus Image

Non-invasive assessment of retinal fundus image is well suited for early detection of ocular disease and is facilitated more by advancements in computed vision and machine learning. Most of the Deep learning based diagnosis system gives just a diagnosis(absence or presence) of a certain number of diseases without hinting the underlying pathological abnormalities. We attempt to extract such pathological markers, as an ophthalmologist would do, in this thesis and pave a way for explainable diagnosis/assistance task. Such abnormalities can be present in various regions of a fundus image including vasculature, Optic Nerve Disc/Cup, or even in non-vascular region. This thesis consist of series of novel techniques starting from robust retinal vessel segmentation, complete vascular topology extraction, and better ArteryVein classification. Finally, we compute two of the most important vascular anomalies-arteryvein ratio and vessel tortuosity. While most of the research focuses on vessel segmentation, and artery-vein classification, we have successfully advanced this line of research one step further. We believe it can be a very valuable framework for future researcher working on automated retinal disease diagnosis

EDDense-Net: Fully Dense Encoder Decoder Network for Joint Segmentation of Optic Cup and Disc

Glaucoma is an eye disease that causes damage to the optic nerve, which can lead to visual loss and permanent blindness. Early glaucoma detection is therefore critical in order to avoid permanent blindness. The estimation of the cup-to-disc ratio (CDR) during an examination of the optical disc (OD) is used for the diagnosis of glaucoma. In this paper, we present the EDDense-Net segmentation network for the joint segmentation of OC and OD. The encoder and decoder in this network are made up of dense blocks with a grouped convolutional layer in each block, allowing the network to acquire and convey spatial information from the image while simultaneously reducing the network's complexity. To reduce spatial information loss, the optimal number of filters in all convolution layers were utilised. In semantic segmentation, dice pixel classification is employed in the decoder to alleviate the problem of class imbalance. The proposed network was evaluated on two publicly available datasets where it outperformed existing state-of-the-art methods in terms of accuracy and efficiency. For the diagnosis and analysis of glaucoma, this method can be used as a second opinion system to assist medical ophthalmologists