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Colon tissue image segmentation with MWSI-NET

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Abstract

Developments in deep learning have resulted in computer-aided diagnosis for many types of cancer. Previously, pathologists manually performed the labeling work in the analysis of colon tissues, which is both time-consuming and labor-intensive. Results are easily affected by subjective conditions. Therefore, it is beneficial to identify the cancerous regions of colon cancer with the assistance of computer-aided technology. Pathological images are often difficult to process due to their irregularity, similarity between cancerous and non-cancerous tissues and large size. We propose a multi-scale perceptual field fusion structure based on a dilated convolutional network. Using this model, a structure of dilated convolution kernels with different aspect ratios is inserted, which can process cancerous regions of different sizes and generate larger receptive fields. Thus, the model can fuse detailed information at different scales for better semantic segmentation. Two different attention mechanisms are adopted to highlight the cancerous areas. A large, open-source dataset was used to verify improved efficacy when compared to previously disclosed methods.

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Funding

This work was supported in part by National Instrument Funds “Development and application of multidimensional biological tissue characterization and analysis instrument.”

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Authors and Affiliations

  1. Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China

    Hao Cheng

  2. Department of Automation, Shanghai Jiao Tong University, Shanghai, China

    Kaijie Wu, Jie Tian, Kai Ma, Chaocheng Gu & Xinping Guan

Authors
  1. Hao Cheng
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  2. Kaijie Wu
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  3. Jie Tian
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  4. Kai Ma
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  5. Chaocheng Gu
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Correspondence to Kaijie Wu.

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Cite this article

Cheng, H., Wu, K., Tian, J. et al. Colon tissue image segmentation with MWSI-NET. Med Biol Eng Comput 60, 727–737 (2022). https://doi.org/10.1007/s11517-022-02501-7

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  • DOI: https://doi.org/10.1007/s11517-022-02501-7

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