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Md-Net: Multi-scale Dilated Convolution Network for CT Images Segmentation

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Abstract

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.

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Acknowledgements

Funding was provided by National Natural Science Foundation of China (Grant No. 61762014).

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Authors and Affiliations

  1. College of Electronic Engineering, Guangxi Normal University, Guilin, 541004, People’s Republic of China

    Haiying Xia, Weifan Sun, Shuxiang Song & Xiangwei Mou

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  1. Haiying Xia
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  2. Weifan Sun
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  3. Shuxiang Song
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  4. Xiangwei Mou
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Correspondence to Weifan Sun.

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Xia, H., Sun, W., Song, S. et al. Md-Net: Multi-scale Dilated Convolution Network for CT Images Segmentation. Neural Process Lett 51, 2915–2927 (2020). https://doi.org/10.1007/s11063-020-10230-x

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