Abstract
We present a novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images. CNN-GS first utilizes a CNN to extract features of specific retinal layer boundaries and train a corresponding classifier to delineate a pilot estimate of the eight layers. Next, a graph search method uses the probability maps created from the CNN to find the final boundaries. We validated our proposed method on 60 volumes (2915 B-scans) from 20 human eyes with non-exudative age-related macular degeneration (AMD), which attested to effectiveness of our proposed technique.
Highlights
Optical coherence tomography (OCT) can acquire 3D cross sectional images of human tissue at micron resolutions [1], which has been widely used for a variety of medical and industrial imaging applications [1,2]
The high resolution of OCT enables the visualization of multiple retinal cell layers and biomarkers of retinal and neurodegenerative diseases, including agerelated macular degeneration (AMD) [3,4,5], diabetic retinopathy [6], glaucoma [7], Alzheimer’s disease [8], and amyotrophic lateral sclerosis [9]
Our paper is in the same class of segmentation algorithms, which combines the convolutional neural networks (CNN) model with graph search methodology for the automatic segmentation of nine layer boundaries on human retinal OCT images
Summary
Optical coherence tomography (OCT) can acquire 3D cross sectional images of human tissue at micron resolutions [1], which has been widely used for a variety of medical and industrial imaging applications [1,2]. Our paper is in the same class of segmentation algorithms, which combines the CNN model with graph search methodology (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on human retinal OCT images In this method, we first decompose training OCT images into patches. On another front, other normal anatomic (e.g. large vessels) and pathologic features (e.g. hyperreflective foci) affect the accuracy of segmentation algorithms developed for normal and diseased retina (e.g. the very basic implementation of the GTDP algorithm as discussed in the subsection). Machine learning algorithms, such as the CNN-GS method described in the remainder of this paper, can be used as an alternative approach to reduce the reliance of segmentation techniques on ad hoc rules
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