Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model

- Antony, Bhavna, Kim, Byung-Jin, Lang, Andrew, Carass, Aaron, Prince, Jerry, Zack, Donald

Title
Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model
Creator
Antony, Bhavna; Kim, Byung-Jin; Lang, Andrew; Carass, Aaron; Prince, Jerry; Zack, Donald
Date
2017
Type
Text; Journal article
Identifier
http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/197485
Identifier
vital:18864
Identifier
https://doi.org/10.1371/journal.pone.0181059
Identifier
ISSN:1932-6203
Abstract
The use of spectral-domain optical coherence tomography (SD-OCT) is becoming commonplace for the in vivo longitudinal study of murine models of ophthalmic disease. Longitudinal studies, however, generate large quantities of data, the manual analysis of which is very challenging due to the time-consuming nature of generating delineations. Thus, it is of importance that automated algorithms be developed to facilitate accurate and timely analysis of these large datasets. Furthermore, as the models target a variety of diseases, the associated structural changes can also be extremely disparate. For instance, in the light damage (LD) model, which is frequently used to study photoreceptor degeneration, the outer retina appears dramatically different from the normal retina. To address these concerns, we have developed a flexible graph-based algorithm for the automated segmentation of mouse OCT volumes (ASiMOV). This approach incorporates a machine-learning component that can be easily trained for different disease models. To validate ASiMOV, the automated results were compared to manual delineations obtained from three raters on healthy and BALB/cJ mice post LD. It was also used to study a longitudinal LD model, where five control and five LD mice were imaged at four timepoints post LD. The total retinal thickness and the outer retina (comprising the outer nuclear layer, and inner and outer segments of the photoreceptors) were unchanged the day after the LD, but subsequently thinned significantly (p < 0.01). The retinal nerve fiber-ganglion cell complex and the inner plexiform layers, however, remained unchanged for the duration of the study.
Publisher
Public Library of Science
Relation
PLoS One Vol. 12, no. 8 (2017), p. e0181059-e0181059
Rights
All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
Rights
http://creativecommons.org/licenses/by/4.0/
Rights
Copyright: © 2017 Antony et al. This is an open access article distributed under the terms of the Creative Commons Attribution License,
Rights
Open Access
Subject
Algorithms; Analysis; Animal models; Animals; Apoptosis; Automated segmentation; Basic medicine; Biology; Biology and Life Sciences; Biomedical engineering; Clinical medicine; Clinical study; Clinical trials; Coherence (Optics); Computer engineering; Correlation analysis; Damage assessment; Data processing; Degeneration; Developmental biology; Disease Models, Animal; Female; Image processing; Image segmentation; Immunohistochemistry; In vivo methods and tests; Information management; Learning algorithms; Light; Light - adverse effects; Light damage; Longitudinal Studies; Machine learning; medical and health sciences; Medical imaging; Medicine; Medicine and Health Sciences; Mice; Ophthalmology & optometry; Optical Coherence Tomography; Optics; Outer nuclear layer; Photoreceptors; Reproducibility of Results; Research and Analysis Methods; Retina; Retina - diagnostic imaging; Retina - pathology; Retina - radiation effects; Retinal; Retinal degeneration; Retinal Diseases - diagnostic imaging; Retinal Diseases - etiology; Retinal Diseases - pathology; Science; Segmentation; sense organs; Social Sciences; Technology application; Tomography; Tomography, Optical Coherence - methods; Multidisciplinary
Full Text
Reviewed
Funder
Funding: This work was supported in part by the NIH/NEI through grants P30-EY001765 (PI:Zack), T32-EY007143 (PI:Zack), R01-EY023754 (PI:Zack), R21-EY023812 (PI:Zack), and R01-EY024655 (PI: Prince).
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