CE-Net: Context Encoder Network for 2D Medical Image Segmentation
CE-Net: Context Encoder Network for 2D Medical Image Segmentation
复制标题
CE-Net：用于 2D 医学图像分割的上下文编码器网络
DOI：
10.1109/tmi.2019.2903562
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发表时间：
2019-10-01
影响因子：
10.6
通讯作者：
Liu, Jiang
中科院分区：
文献类型：
--
作者：
Gu, Zaiwang;Cheng, Jun;Liu, Jiang
医学图像分割是医学图像分析的重要步骤。随着卷积神经网络在图像处理中的快速发展，深度学习已经被用于医学图像分割，如视盘分割、血管检测、肺部分割、细胞分割等。此前，已经提出了基于U-net的方法。然而，连续池化和步幅卷积操作导致了一些空间信息的丢失。在本文中，我们提出了一个上下文编码器网络（CE-Net），以捕获更多的高层信息，并保留空间信息的二维医学图像分割。CE-Net主要包含三个主要组件：特征编码器模块、上下文提取器和特征解码器模块。我们使用预训练的ResNet块作为固定特征提取器。上下文提取器模块由新提出的稠密卷积块和残差多核池化块组成。我们将所提出的CE-Net应用于不同的2D医学图像分割任务。综合结果表明，该方法优于原来的U-Net方法和其他国家的最先进的方法，视盘分割，血管检测，肺分割，细胞轮廓分割，视网膜光学相干断层扫描层分割。
Medical image segmentation is an important step in medical image analysis. With the rapid development of a 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, and so on. Previously, U-net based approaches have been proposed. However, the consecutive pooling and strided convolutional operations led to the loss of some spatial information. In this paper, we propose a context encoder network (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 the pretrained ResNet block as the fixed feature extractor. The context extractor module is formed by a newly proposed dense atrous convolution block and a residual multi-kernel pooling 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.