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MC-Net: multi-scale context-attention network for medical CT image segmentation

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Abstract

The encoder-decoder CNN architecture has greatly improved CT medical image segmentation, but it encounters a bottleneck due to the loss of details in the encoding process, which limits the accuracy improvement. To address this problem, we propose a multi-scale context-attention network (MC-Net). The key idea is to explore the useful information across multiple scales and the context for the segmentation of objects of medical interest. Through the introduction of multi-scale and context-attention modules, MC-Net gains the ability to extract local and global semantic information around targets. To further improve the segmentation accuracy, we weight the pixels depending on whether they belong to targets. Many experiments on a lung dataset and a bladder dataset demonstrate that the proposed MC-Net outperforms state-of-the-art methods in terms of accuracy, sensitivity, the area under the receiver operating characteristic curve and the Dice score.

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Authors and Affiliations

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

    Haiying Xia, Mingjun Ma, Haisheng Li & Shuxiang Song

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  1. Haiying Xia
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  2. Mingjun Ma
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  3. Haisheng Li
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  4. Shuxiang Song
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Correspondence to Shuxiang Song.

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This work was supported in part by the National Natural Science Foundation of China under Grant 61762014, in part by the Science and Technology Project of Guangxi under Grant 2018GXNSFAA281351, and in part by the Innovation Project of Guangxi Graduate Education under Grant YCSW2021096.

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Xia, H., Ma, M., Li, H. et al. MC-Net: multi-scale context-attention network for medical CT image segmentation. Appl Intell 52, 1508–1519 (2022). https://doi.org/10.1007/s10489-021-02506-z

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