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Masood, S, Fang, R, Li, P, Li, H, Sheng, B, Mathavan, A, Wang, X, Yang, P, Wu, Q, Qin, J and Jia, W (2019) Automatic Choroid Layer Segmentation from Optical Coherence Tomography Images Using Deep Learning. Scientific Reports, 9. ISSN 2045-2322
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Automatic Choroid Layer Segmentation from Optical Coherence Tomography Images Using Deep Learning.pdf - Published Version Available under License Creative Commons Attribution. Download (4MB) | Preview |
Abstract
The choroid layer is a vascular layer in human retina and its main function is to provide oxygen and support to the retina. Various studies have shown that the thickness of the choroid layer is correlated with the diagnosis of several ophthalmic diseases. For example, diabetic macular edema (DME) is a leading cause of vision loss in patients with diabetes. Despite contemporary advances, automatic segmentation of the choroid layer remains a challenging task due to low contrast, inhomogeneous intensity, inconsistent texture and ambiguous boundaries between the choroid and sclera in Optical Coherence Tomography (OCT) images. The majority of currently implemented methods manually or semi-automatically segment out the region of interest. While many fully automatic methods exist in the context of choroid layer segmentation, more effective and accurate automatic methods are required in order to employ these methods in the clinical sector. This paper proposed and implemented an automatic method for choroid layer segmentation in OCT images using deep learning and a series of morphological operations. The aim of this research was to segment out Bruch’s Membrane (BM) and choroid layer to calculate the thickness map. BM was segmented using a series of morphological operations, whereas the choroid layer was segmented using a deep learning approach as more image statistics were required to segment accurately. Several evaluation metrics were used to test and compare the proposed method against other existing methodologies. Experimental results showed that the proposed method greatly reduced the error rate when compared with the other state-of-the art methods.
| Item Type: | Article |
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| Uncontrolled Keywords: | 0601 Biochemistry and Cell Biology, 0299 Other Physical Sciences |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computer Science & Mathematics |
| Publisher: | Nature Publishing Group |
| Related URLs: | |
| Date Deposited: | 02 Oct 2019 10:30 |
| Last Modified: | 04 Sep 2021 08:47 |
| DOI or ID number: | 10.1038/s41598-019-39795-x |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/11439 |
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