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Proceedings Paper

Longitudinal analysis of mouse SDOCT volumes
Author(s): Bhavna J. Antony; Aaron Carass; Andrew Lang; Byung-Jin Kim; Donald J. Zack; Jerry L. Prince
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Paper Abstract

Spectral-domain optical coherence tomography (SDOCT), in addition to its routine clinical use in the diagnosis of ocular diseases, has begun to fund increasing use in animal studies. Animal models are frequently used to study disease mechanisms as well as to test drug efficacy. In particular, SDOCT provides the ability to study animals longitudinally and non-invasively over long periods of time. However, the lack of anatomical landmarks makes the longitudinal scan acquisition prone to inconsistencies in orientation. Here, we propose a method for the automated registration of mouse SDOCT volumes. The method begins by accurately segmenting the blood vessels and the optic nerve head region in the scans using a pixel classification approach. The segmented vessel maps from follow-up scans were registered using an iterative closest point (ICP) algorithm to the baseline scan to allow for the accurate longitudinal tracking of thickness changes. Eighteen SDOCT volumes from a light damage model study were used to train a random forest utilized in the pixel classification step. The area under the curve (AUC) in a leave-one-out study for the retinal blood vessels and the optic nerve head (ONH) was found to be 0.93 and 0.98, respectively. The complete proposed framework, the retinal vasculature segmentation and the ICP registration, was applied to a secondary set of scans obtained from a light damage model. A qualitative assessment of the registration showed no registration failures.

Paper Details

Date Published: 13 March 2017
PDF: 8 pages
Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 101371H (13 March 2017); doi: 10.1117/12.2257432
Show Author Affiliations
Bhavna J. Antony, Johns Hopkins Univ. (United States)
Aaron Carass, Johns Hopkins Univ. (United States)
Andrew Lang, Johns Hopkins Univ. (United States)
Byung-Jin Kim, Johns Hopkins Univ. School of Medicine (United States)
Donald J. Zack, Johns Hopkins Univ. School of Medicine (United States)
Jerry L. Prince, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 10137:
Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor Gimi, Editor(s)

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