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Medical image segmentation based on dual-channel integrated cross-layer residual algorithm

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Abstract

Segmentation tasks in medical images have always been a hot topic in the medical imaging field. Compared with traditional images, medical images have richer semantics, which increases the difficulty of feature learning. This paper proposes a new end-to-end dual-channel integrated cross-layer residual algorithm (TIC-Net) based on deep learning to fully mine the semantic information between features for medical image segmentation. First, in the encoder, we built a dual-channel network of traditional convolution and dilated convolution using multiple structures to learn different semantic information from the image and from feature fusion and residual calculation to achieve feature joint mining. Second, we added two sets of new integrated modules between the decoder and encoder to fully fuse the global and local features of each layer of the image in the encoder. Finally, in the decoder, we use a cross-layer feature residual fusion strategy to obtain more semantic information. Compared with the existing partial segmentation model, the proposed deep learning algorithm model achieves the best results with the Kaggle and MICCAI datasets.

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Acknowledgements

The authors would like to thank the Editor-in-Chief, the Associate Editor, and the reviewers for their insightful comments and suggestions.

Funding

This research is partially supported by Science and Technology Department of Xinjiang Uyghur Autonomous Region (2020E0234). Xinjiang Autonomous Region key research and development project (2021B03001-4). We would also like to thank our tutor for the careful guidance and all the participants for their insightful comments.

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Authors and Affiliations

  1. School of Information Science and Engineering, Xinjiang University, Ürümqi, China

    Hongfeng You

  2. Network Center, XinJiang University, Ürümqi, China

    Long Yu

  3. Software College, Xinjiang University, Ürümqi, China

    Shengwei Tian

  4. Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Ürümqi, China

    Xiang Ma

  5. Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Ürümqi, China

    Yan Xing

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  1. Hongfeng You
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  2. Long Yu
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  4. Xiang Ma
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  5. Yan Xing
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Correspondence to Long Yu.

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You, H., Yu, L., Tian, S. et al. Medical image segmentation based on dual-channel integrated cross-layer residual algorithm. Multimed Tools Appl 82, 5587–5603 (2023). https://doi.org/10.1007/s11042-021-11326-9

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  • DOI: https://doi.org/10.1007/s11042-021-11326-9

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