Towards developing a multiple modality fusion technique for automatic detection of Glaucoma

Divya Jyothi Gaddipati

Abstract

Glaucoma is a major eye disease which when untreated, can gradually lead to irreversible loss in vision. The underlying causes are a loss of retinal nerve fibres (resulting in a thinning of the layer and enlargement of the optic cup) and peripapillary atrophy. Since these occur without any sign of symptoms in the initial stages, it is difficult to diagnose the disease in the early stages. Hence, the development of Computer-aided diagnostics (CAD) systems for early detection and treatment of the disease has attracted the attention of many medical experts and researchers alike. Optical Coherence Tomography (OCT) and Fundus photography are two widely used retinal imaging techniques for obtaining the structural information of the eye which helps to analyze and detect the diseases. Existing automated systems rely largely on fundus images for assessment of glaucoma due to their fast acquisition and cost. OCT images provide vital and unambiguous information for understanding the changes occurring in the retina, specifically related to the retinal nerve fiber layer and the optic nerve head which are essential for disease assessment. However, the high cost of OCT is a deterrent for deployment in screening at large scale. Hence, the focus of this thesis is to investigate the potential of integrating the two retinal imaging techniques which provide complementary information of the eye for developing automatic glaucoma screening system. Firstly, we propose a deep learning approach directly operating on 3D OCT volumes for glaucoma assessment which showed promising results, thus demonstrating the prominence of the highly discriminative features learnt from OCT for automated glaucoma detection. Next, we present a novel CAD solution wherein both OCT and fundus modality images are leveraged to learn a model that can perform a mapping of fundus to the OCT feature space. We show how this model can be subsequently used to detect glaucoma given an image from only one modality (fundus), thus enabling the automated screening operation to be executed on a large scale. The results show that fundus to OCT feature space mapping is an attractive option for glaucoma detection

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