Abstract
The choroid is a complex vascular tissue that is covered with the retinal pigment epithelium. Ultra high speed swept source optical coherence tomography (SS-OCT) provides us with high-resolution cube scan images of the choroid. Robust segmentation techniques are required to reconstruct choroidal volume using SS-OCT images. For automated segmentation, the delineation of the choroidal-scleral (C-S) boundary is key to accurate segmentation. Low contrast of the boundary, scleral canals formed by the vessel and the nerve, and the posterior stromal layer, may cause segmentation errors. Semantic segmentation is one of the applications of deep learning used to classify the parts of images related to the meanings of the subjects. We applied semantic segmentation to choroidal segmentation and measured the volume of the choroid. The measurement results were validated through comparison with those of other segmentation methods. As a result, semantic segmentation was able to segment the C-S boundary and choroidal volume adequately.
Highlights
An artificial neural network represented a significant breakthrough, and deep learning architectures have been applied to various fields, such as computer vision, bioinformatics, and medical image analysis[19]
We examined the similarities and reproducibility of the automated semantic segmentation using a deep convolutional neural network (DCNN), and other methods of choroidal segmentation
We compared the results of the semantic segmentation (SegNet) to manual segmentaion and that made by graph cut method (Fig. 2)
Summary
An artificial neural network represented a significant breakthrough, and deep learning architectures have been applied to various fields, such as computer vision, bioinformatics, and medical image analysis[19]. Semantic segmentation, based on the convolutional neural network, is a novel image analysis technique[20,21], describes the process of associating each pixel of an image with a class label, and is used in the fields of autonomous driving and medical imaging analysis. This method is potentially useful for choroidal segmentation in 3D choroidal analysis. We examined the similarities and reproducibility of the automated semantic segmentation using a deep convolutional neural network (DCNN), and other methods of choroidal segmentation
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