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.
Abstract (translated)
医学图像分割是医学图像分析中的一个重要环节。随着卷积神经网络在图像处理中的迅速发展，深度学习已被应用于医学图像分割，如视盘分割、血管检测、肺分割、细胞分割等，以前提出了基于U-NET的方法。然而，连续的池和跨步卷积操作会导致一些空间信息的丢失。本文提出了一种上下文编码网络（简称CE网），用于二维医学图像分割，捕获更多的高层次信息，保留空间信息。CENET主要由三个主要部分组成：特征编码模块、上下文提取模块和特征译码器模块。我们使用预训练的resnet块作为固定的特征抽取器。上下文提取模块由一个新提出的密集阿托拉斯卷积块和剩余多核池块构成。将该网络应用于不同的二维医学图像分割任务。综合结果表明，该方法在视盘分割、血管检测、肺分割、细胞轮廓分割和视网膜光学相干断层扫描层分割等方面优于原U-NET方法和其他最先进的方法。
URL
https://arxiv.org/abs/1903.02740
