Abstract
Optical coherence tomography (OCT) is used to produce high resolution depth images of the retina and is now the standard of care for in-vivo ophthalmological assessment. It is also increasingly being used for evaluation of neurological disorders such as multiple sclerosis (MS). Automatic segmentation methods identify the retinal layers of the macular cube providing consistent results without intra- and inter-rater variation and is faster than manual segmentation. In this paper, we propose a fast multi-layer macular OCT segmentation method based on a fast level set method. Our framework uses contours in an optimized approach specifically for OCT layer segmentation over the whole macular cube. Our algorithm takes boundary probability maps from a trained random forest and iteratively refines the prediction to subvoxel precision. Evaluation on both healthy and multiple sclerosis subjects shows that our method is statistically better than a state-of-the-art graph-based method.
Highlights
Optical coherence tomography (OCT) is a three-dimensional (3D) imaging technique that emits a beam of light into the tissues to be examined and detects the reflected or back-scattered light from the tissues
The vertex at the i th location in the j th A-scan only connects to vertices in the ( j + 1)th A-scan that are between i − 1 and i + 1. This ensures that the shortest path we find is smooth and, more importantly, it goes through each A-scan only once
We proposed a layer boundary evolution method that loosely originates from the fast level set method to replace the commonly used graph based method
Summary
Optical coherence tomography (OCT) is a three-dimensional (3D) imaging technique that emits a beam of light into the tissues to be examined and detects the reflected or back-scattered light from the tissues. Various approaches have been developed for automated layer segmentation of OCT images [14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35], including methods based on shortest paths [17], active contours [18,19,20], statistical models [21], and level sets [22,23,24,25]. The second part uses the boundary probability maps from the trained random forest for generating two inputs to the layer boundary evolution: a force field and an initial prediction for each boundary. Since the first part is well documented in [28] and its source code is publicly available (http://www.nitrc.org/projects/aura_tools), we briefly introduce each step in the first part and focus most of our discussion on the second part
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
