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

Yuhe Shen; Liling Guan; Kai Yu; Xinjian Chen

doi:10.1117/12.2511727

15 March 2019 A CNN based retinal regression model for Bruch’s membrane opening detection

Yuhe Shen, Liling Guan, Kai Yu, Xinjian Chen

Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 109492A (2019) https://doi.org/10.1117/12.2511727
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Citation Download Citation

Yuhe Shen, Liling Guan, Kai Yu, and Xinjian Chen "A CNN based retinal regression model for Bruch’s membrane opening detection", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109492A (15 March 2019); https://doi.org/10.1117/12.2511727

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available