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Proceedings Paper

A CNN based retinal regression model for Bruch’s membrane opening detection
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Paper Abstract

Glaucoma is a common eye disease. It causes damage to patient’s vision and is difficult to diagnose. By locating Bruch’s membrane opening (BMO) in the Optical Coherence Tomography (OCT) image we can compute important diagnostic parameters which can increase the probability of early diagnosis of glaucoma. Besides the traditional methods, which depend on stratification results, this paper introduces a new method based on an end-to-end deep learning model to detect the BMO. Our model is composed of three parts. The first part is a CNN based retinal feature extraction network. It extracts feature map for both Optic Nerve Head (ONH) proposal and BMO detection. The second part is an ONH proposal network to detect region of interest (ROI) containing BMO. The third part is using the feature map from ONH proposal network to regress the location of BMO. The model has shown a clear precedence over other methods in terms of accuracy. Satisfactory results have been obtained when compared with clinical results.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109492A (15 March 2019); doi: 10.1117/12.2511727
Show Author Affiliations
Yuhe Shen, Soochow Univ. (China)
Jiangsu Univ. of Technology (China)
Liling Guan, Soochow Univ. (China)
Kai Yu, Soochow Univ. (China)
Xinjian Chen, Soochow Univ. (China)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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