Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80787
Title: Automatic choroid layer segmentation from optical coherence tomography images using deep learning
Authors: Masood, S
Fang, RG
Li, P
Li, HT
Sheng, B
Mathavan, A
Wang, XN
Yang, P
Wu, Q
Qin, J 
Jia, WP
Issue Date: 2019
Publisher: Nature Publishing Group
Source: Scientific reports, 28 Feb. 2019, v. 9, 3058, p. 1-18 How to cite?
Journal: Scientific reports 
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.
URI: http://hdl.handle.net/10397/80787
EISSN: 2045-2322
DOI: 10.1038/s41598-019-39795-x
Rights: Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
© The Author(s) 2019
The following publication Masood, S., Fang, R. G., Li, P., Li, H. T., Sheng, B., Mathavan, A., ... & Jia, W. P. (2019). Automatic Choroid Layer Segmentation from Optical Coherence Tomography Images Using Deep Learning. Scientific reports, 9(1), 3058, 1-18 is available at https://dx.doi.org/10.1038/s41598-019-39795-x
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