Automatic Choroid Layer Segmentation from Optical Coherence Tomography Images Using Deep Learning

Scientific Reports
Saleha MasoodWeiping Jia

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...Continue Reading

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Citations

Apr 24, 2020·Eye and Vision·Yan TongYin Shen
Jan 30, 2021·Computational and Mathematical Methods in Medicine·Fang HeXiaojun Chen
Jun 23, 2021·Journal of Neuroscience Methods·Kyungmoo LeeElliott H Sohn
Nov 19, 2021·Medical & Biological Engineering & Computing·Hajar DaneshRahele Kafieh

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Methods Mentioned

BETA
feature extraction

Software Mentioned

MATLAB
Heidelberg Eye Explorer
Caffe

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