- Title
- Longitudinal analysis of mouse SDOCT volumes
- Creator
- Antony, Bhavna; Carass, Aaron; Lang, Andrew; Kim, Byung-Jin; Zack, Donald; Prince, Jerry
- Date
- 2017
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/197346
- Identifier
- vital:18836
- Identifier
-
https://doi.org/10.1117/12.2257432
- Identifier
- ISBN:0277-786X
- Abstract
- Spectral-domain optical coherence tomography (SDOCT), in addition to its routine clinical use in the diagnosis of ocular diseases, has begun to find 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.
- Publisher
- SPIE
- Relation
- SPIE Medical Imaging; Orlando, Florida, United States; 2017; in Proceedings Volume 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging; 101371H (2017) Vol. 10137
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Subject
- 4603 Computer vision and multimedia computation
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