Quantitative Analysis of Mouse Retinal Layers Using Automated Segmentation of Spectral Domain Optical Coherence Tomography Images.

Dysli, Chantal-Simone; Enzmann, Volker; Sznitman, Raphael; Zinkernagel, Martin (2015). Quantitative Analysis of Mouse Retinal Layers Using Automated Segmentation of Spectral Domain Optical Coherence Tomography Images. Translational vision science & technology, 4(4), p. 9. Association for Research in Vision and Ophthalmology 10.1167/tvst.4.4.9

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PURPOSE

Quantification of retinal layers using automated segmentation of optical coherence tomography (OCT) images allows for longitudinal studies of retinal and neurological disorders in mice. The purpose of this study was to compare the performance of automated retinal layer segmentation algorithms with data from manual segmentation in mice using the Spectralis OCT.

METHODS

Spectral domain OCT images from 55 mice from three different mouse strains were analyzed in total. The OCT scans from 22 C57Bl/6, 22 BALBc, and 11 C3A.Cg-Pde6b(+)Prph2(Rd2) /J mice were automatically segmented using three commercially available automated retinal segmentation algorithms and compared to manual segmentation.

RESULTS

Fully automated segmentation performed well in mice and showed coefficients of variation (CV) of below 5% for the total retinal volume. However, all three automated segmentation algorithms yielded much thicker total retinal thickness values compared to manual segmentation data (P < 0.0001) due to segmentation errors in the basement membrane.

CONCLUSIONS

Whereas the automated retinal segmentation algorithms performed well for the inner layers, the retinal pigmentation epithelium (RPE) was delineated within the sclera, leading to consistently thicker measurements of the photoreceptor layer and the total retina.

TRANSLATIONAL RELEVANCE

The introduction of spectral domain OCT allows for accurate imaging of the mouse retina. Exact quantification of retinal layer thicknesses in mice is important to study layers of interest under various pathological conditions.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Ophthalmology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Medical Imaging Laboratory

UniBE Contributor:

Dysli, Chantal-Simone; Enzmann, Volker; Sznitman, Raphael and Zinkernagel, Martin

Subjects:

600 Technology > 610 Medicine & health

ISSN:

2164-2591

Publisher:

Association for Research in Vision and Ophthalmology

Language:

English

Submitter:

Volker Enzmann

Date Deposited:

18 Nov 2015 08:56

Last Modified:

28 Nov 2020 02:26

Publisher DOI:

10.1167/tvst.4.4.9

PubMed ID:

26336634

Uncontrolled Keywords:

optical coherence tomography; retina; retinal layer segmentation

BORIS DOI:

10.7892/boris.72955

URI:

https://boris.unibe.ch/id/eprint/72955

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