Conference Papers

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Subject: search.filters.subject.Optical Coherence Tomography

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    Development and validation of a novel automated method for quantification of choroidal thickness in age-related macular degeneration
    (SPIE, 2021) Smitha, A.; Jidesh, P.; Janarthanam, J.; Lakshminarayanan, V.
    Age-related Macular Degeneration (AMD) is a progressive, irreversible retinal disorder, and one of the leading causes of severe visual impairment or even blindness in the elderly population. The choroid plays a vital role in the pathophysiology of AMD. It is known that abnormal choroidal blood flow leads to retinal photoreceptor dysfunction and eventual death. We propose a new automated algorithm that can be used to quantify choroidal thickness (CT) from Optical Coherence Tomography (OCT) images of the retina. This thickness evaluation procedure includes image contrast enhancement, localization around the fovea centralis, segmentation of Retinal Pigment Epithelium (RPE) and choroidal layer, followed by CT measurement at multiple locations in the sub-foveal region at intervals of 0.5 mm on both nasal and temporal sides up to a distance of 1 mm from the center of the foveal pit. The horizontal radial scan OCT images (Cirrus 5000, Carl Zeiss Meditec Inc., Dublin, CA) of both healthy and AMD patients were used to measure the CT using the new algorithm. The statistical tests convey that the CT of AMD patients is relatively smaller than the normal condition. Furthermore, t-Test conducted between the proposed approach and clinical approach of extracting CT measurements confirm that the proposed method is in good agreement with the clinical measurements. On an average, the thickness of the choroid is found to be 0.32 ± 0.10 mm for the normal category and 0.21 ± 0.06 mm for the AMD category, in the central sub-foveal region, as obtained from the proposed automatic CT measurement method. The clinical significance and the results of automated choroid extraction are discussed in this paper. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
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    Attention Assisted Patch-Wise CNN for the Segmentation of Fluids from the Retinal Optical Coherence Tomography Images
    (Springer Science and Business Media Deutschland GmbH, 2024) Anoop, B.N.; Parida, S.; Ajith, B.; Girish, G.N.; Kothari, A.R.; Kavitha, M.S.; Rajan, J.
    Optical Coherence Tomography (OCT) is an important imaging modality in ophthalmology to visualize the abnormalities present in the retina. One of the major reasons for blindness is the accumulation of fluids in the various layers of the retina called retinal cysts. Accurate estimation of the type of cyst and its volume is important for effective treatment planning. In this paper, we propose attention assisted convolutional neural network-based architecture to detect and quantify three types of retinal cysts namely the intra-retinal cyst, sub-retinal cyst and pigmented epithelial detachment from the OCT images of the human retina. The proposed architecture has an encoder-decoder structure with an attention and a multi-scale module. The qualitative and quantitative performance of the model is evaluated on the publicly available RETOUCH retinal OCT fluid detection challenge data set. The proposed model outperforms the state-of-the-art methods in terms of precision, recall, and dice coefficient. Furthermore, the proposed model is computationally efficient due to its less number of model parameters. © Springer Nature Switzerland AG 2024.