Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/214511
Title: DEEP LEARNING APPROACHES ON OPTICAL COHERENCE TOMOGRAPHY IMAGES FOR IMAGE RESTORATION, SEGMENTATION, AND GLAUCOMA DIAGNOSIS
Authors: HARIS CHEONG
ORCID iD:   orcid.org/0000-0001-8856-8319
Keywords: GAN, OCT, segmentation, glaucoma, diagnosis
Issue Date: 19-Aug-2021
Citation: HARIS CHEONG (2021-08-19). DEEP LEARNING APPROACHES ON OPTICAL COHERENCE TOMOGRAPHY IMAGES FOR IMAGE RESTORATION, SEGMENTATION, AND GLAUCOMA DIAGNOSIS. ScholarBank@NUS Repository.
Abstract: In this work, we aimed to develop deep learning (DL) approaches to maximize diagnosis accuracy of glaucoma by restoring and segmenting optical coherence tomog- raphy B-scans. First, we proposed a novel technique that can restore information within retinal blood vessel shadows. Second, we presented a DL algorithm to remove speckle noise. This technique also reduced the chance of motion artifacts from mani- festing in OCT B-scans. We show that this technique preserves the integrity of the B-scan and removes speckle noise and retinal blood vessel shadows simultaneously with a single pass of a single network. We showed that the developed algorithm could improve segmentation performance on B-scans created by a different OCT manufacturer. Thirdly, we developed a novel method to diagnose glaucoma from the 3D structure of retinal blood vessels.
URI: https://scholarbank.nus.edu.sg/handle/10635/214511
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