CE-Net: Context Encoder Network for 2D Medical Image Segmentation
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by
Zaiwang Gu, Jun Cheng, Huazhu Fu, Kang Zhou, Huaying Hao, Yitian Zhao,
Tianyang Zhang, Shenghua Gao, Jiang Liu
2019
Abstract
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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