Accurate CT image segmentation is of great importance to the clinical diagnosis. Due to the high similarity of gray values in CT image, the segmented areas are easily affected by their surroundings, which leads to the loss of semantic information. In this paper, we propose a multi-scale dilated convolution network (Md-Net) for CT image segmentation with superior segmentation performance compared with state-of-the-art methods. Specifically, our Md-Net utilizes the dilated convolutions with different sizes to form feature pyramids for extracting the semantic information. Moreover, we use a weighted Diceloss to accelerate the convergence in training process. Meanwhile, the bilinear interpolation and multiple convolutions are taken to reduce the computational cost. Experiment results show that our proposed Md-Net outperforms the representative medical image segmentation methods, including Unet, Unet++, MaskRcnn and CE-Net, in terms of sensitivity, accuracy and area under curve both on lung dataset and Bladder dataset.

中文翻译：

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Md-Net：用于CT图像分割的多尺度膨胀卷积网络

准确的CT图像分割对临床诊断具有重要意义。由于CT图像中灰度值的高度相似性，分割后的区域容易受到周围环境的影响，从而导致语义信息的丢失。在本文中，我们提出了一种用于CT图像分割的多尺度膨胀卷积网络（Md-Net），与最新技术相比，其分割性能更高。具体来说，我们的Md-Net利用大小不同的扩张卷积来形成特征金字塔，以提取语义信息。此外，我们使用加权的Diceloss加快训练过程的收敛速度。同时，采用双线性插值和多次卷积以减少计算成本。