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Med Image Anal. 2019 Jul;55:216-227. doi: 10.1016/j.media.2019.05.002. Epub 2019 May 10.

Automated segmentation of macular edema in OCT using deep neural networks.

Author information

1
Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, PR China.
2
Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, PR China. Electronic address: zhangyi@scu.edu.cn.

Abstract

Macular edema is an eye disease that can affect visual acuity. Typical disease symptoms include subretinal fluid (SRF) and pigment epithelium detachment (PED). Optical coherence tomography (OCT) has been widely used for diagnosing macular edema because of its non-invasive and high resolution properties. Segmentation for macular edema lesions from OCT images plays an important role in clinical diagnosis. Many computer-aided systems have been proposed for the segmentation. Most traditional segmentation methods used in these systems are based on low-level hand-crafted features, which require significant domain knowledge and are sensitive to the variations of lesions. To overcome these shortcomings, this paper proposes to use deep neural networks (DNNs) together with atrous spatial pyramid pooling (ASPP) to automatically segment the SRF and PED lesions. Lesions-related features are first extracted by DNNs, then processed by ASPP which is composed of multiple atrous convolutions with different fields of view to accommodate the various scales of the lesions. Based on ASPP, a novel module called stochastic ASPP (sASPP) is proposed to combat the co-adaptation of multiple atrous convolutions. A large OCT dataset provided by a competition platform called "AI Challenger" are used to train and evaluate the proposed model. Experimental results demonstrate that the DNNs together with ASPP achieve higher segmentation accuracy compared with the state-of-the-art method. The stochastic operation added in sASPP is empirically verified as an effective regularization method that can alleviate the overfitting problem and significantly reduce the validation error.

KEYWORDS:

Atrous convolution; Deep neural networks; Macular edema segmentation

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