Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model.
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-consu...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2017-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5560565?pdf=render |
| _version_ | 1818319799853252608 |
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| author | Bhavna Josephine Antony Byung-Jin Kim Andrew Lang Aaron Carass Jerry L Prince Donald J Zack |
| author_facet | Bhavna Josephine Antony Byung-Jin Kim Andrew Lang Aaron Carass Jerry L Prince Donald J Zack |
| author_sort | Bhavna Josephine Antony |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T10:14:52Z |
| format | Article |
| id | doaj.art-0bbc8fb91021400eb5cf0206252beaa9 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T10:14:52Z |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-0bbc8fb91021400eb5cf0206252beaa92022-12-21T23:51:20ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01128e018105910.1371/journal.pone.0181059Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model.Bhavna Josephine AntonyByung-Jin KimAndrew LangAaron CarassJerry L PrinceDonald J ZackThe 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.http://europepmc.org/articles/PMC5560565?pdf=render |
| spellingShingle | Bhavna Josephine Antony Byung-Jin Kim Andrew Lang Aaron Carass Jerry L Prince Donald J Zack Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model. PLoS ONE |
| title | Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model. |
| title_full | Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model. |
| title_fullStr | Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model. |
| title_full_unstemmed | Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model. |
| title_short | Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model. |
| title_sort | automated segmentation of mouse oct volumes asimov validation clinical study of a light damage model |
| url | http://europepmc.org/articles/PMC5560565?pdf=render |
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