CE-Net: Context Encoder Network for 2D Medical Image Segmentation

by   Zaiwang Gu, et al.
Southern University of Science & Technology
ShanghaiTech University

Medical image segmentation is an important step in medical image analysis. With the rapid development of convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as optic disc segmentation, blood vessel detection, lung segmentation, cell segmentation, etc. Previously, U-net based approaches have been proposed. However, the consecutive pooling and strided convolutional operations lead to the loss of some spatial information. In this paper, we propose a context encoder network (referred to as CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation. CE-Net mainly contains three major components: a feature encoder module, a context extractor and a feature decoder module. We use pretrained ResNet block as the fixed feature extractor. The context extractor module is formed by a newly proposed dense atrous convolution (DAC) block and residual multi-kernel pooling (RMP) block. We applied the proposed CE-Net to different 2D medical image segmentation tasks. Comprehensive results show that the proposed method outperforms the original U-Net method and other state-of-the-art methods for optic disc segmentation, vessel detection, lung segmentation, cell contour segmentation and retinal optical coherence tomography layer segmentation.


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Code Repositories


Code for TMI 2018 "Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation"

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Reimplementation of the proposed Architecture of paper "CE-Net: Context Encoder Network for 2D Medical Image Segmentation" and evaluating on Luna grand challenge dataset.

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Image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics. In this project, we try to solve the problem in ISBI challenge.

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