Semi-automated quantification of retinal IS/OS damage in en-face OCT image

Gattani, V S and Vupparaboina, K K and Patil, A and Chhablani, J and Richhariya, A and Jana, Soumya (2016) Semi-automated quantification of retinal IS/OS damage in en-face OCT image. Computers in Biology and Medicine, 69. pp. 52-60. ISSN 0010-4825

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A variety of vision ailments are indicated by structural changes in the retinal substructures of the posterior segment of the eye. In particular, integrity of the inner-segment/outer-segment (IS/OS) junction directly relates to the visual acuity. In the en-face optical coherence tomography (OCT) image, IS/OS damage manifests as a dark spot in the foveal region, and its quantification, usually performed by experts, assumes diagnostic significance. In this context, in view of the general scarcity of experts, it becomes imperative to develop algorithmic methods to reduce expert time and effort. Accordingly, we propose a semi-automated method based on level sets. As the energy function, we adopt mutual information which exploits the difference in statistical properties of the lesion and its surroundings. On a dataset of 27 en-face OCT images, segmentation obtained by the proposed algorithm exhibits close visual agreement with that obtained manually. Importantly, our results also match manual results in various statistical criteria. In particular, we achieve a mean Dice coefficient of 85.69%, desirably close to the corresponding observer repeatability index of 89.45%. Finally, we quantify algorithmic accuracy in terms of two quotient measures, defined relative to observer repeatability, which could be used as bases for comparison with future algorithms, even if the latter are tested on disparate datasets.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Article
Uncontrolled Keywords: En-face optical coherence tomography (OCT); Inner-segment/outer-segment (IS/OS) junction; Level-set method; Mutual information; Dice coefficient; Statistical analysis
Subjects: Computer science > Special computer methods
Computer science > Big Data Analytics
Others > Electricity
Others > Medicine
Others > Biological sciences
Divisions: Department of Electrical Engineering
Depositing User: Team Library
Date Deposited: 18 Dec 2015 06:01
Last Modified: 12 Sep 2017 08:44
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