Send to

Choose Destination
Proc SPIE Int Soc Opt Eng. 2017 Feb 11;10137. pii: 101371H. doi: 10.1117/12.2257432. Epub 2017 Mar 13.

Longitudinal Analysis of Mouse SDOCT Volumes.

Author information

Department of Electrical and Computer Engineering, Johns Hopkins University.
Wilmer Eye Institute, Johns Hopkins University School of Medicine.


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.


longitudinal analysis; mouse; optical coherence tomography; registration; retina; segmentation; vessel

Supplemental Content

Full text links

Icon for PubMed Central
Loading ...
Support Center