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MD-TransUNet: TransUNet with Multi-attention and Dilated Convolution for Brain Stroke Lesion Segmentation

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2023)

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Abstract

The accurate segmentation of stroke lesion regions holds immense significance in shaping treatment strategies and rehabilitation protocols. Due to the large difference in the volume of stroke lesion areas and the great similarity between lesion areas and normal tissues, most of the existing methods for lesion segmentation cannot deal with these problems well. This paper proposes a novel network named MD-TransUNet for the segmentation of stroke lesions, whose framework is based on the UNet architecture. To fully obtain deep image features, it uses ResNet50 for downsampling. MD (multi-dilated) module is employed as the skip connection to gain more receptive fields. Different receptive fields can adapt to varying volumes of lesion areas. Then, a feature extraction module with multi-level attention mechanism is designed using ConvLSTM, non-local spatial attention, and channel attention modules to suppress useless information expression in skip connections and upsampling processes while focusing more on effective spatial and channel information in features. The experiments show that our proposed network gets superior performance than benchmark methods and indicates the generalization and effectiveness of the proposed model.

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Acknowledgements

This research was supported by “Pioneer” and “Leading Goose” R &D Program of Zhejiang Province under No. 2022C03043, Natural Science Foundation of Zhejiang Province under No. LQ21F020015, and the Open Research Project Fund of Key Laboratory of Marine Ecosystem Dynamics, Ministry of Natural Resources under Grants MED202202.

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Authors and Affiliations

  1. School of Computer Science and Technology, Hangzhou Dianzi University, 310018, Zhejiang, China

    Jie Xu, Jian Wan & Xin Zhang

  2. Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education, Ministry of Education, Beijing, China

    Jie Xu & Xin Zhang

  3. School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Zhejiang, China

    Jian Wan

  4. Key Laboratory of Marine Ecosystem Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China

    Xin Zhang

Authors
  1. Jie Xu
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  2. Jian Wan
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  3. Xin Zhang
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Corresponding author

Correspondence to Xin Zhang .

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Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Xi’an Jiaotong-Liverpool, Suzhou, China

    Xinheng Wang

  3. University of Peloponnese, Patra, Greece

    Nikolaos Voros

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Xu, J., Wan, J., Zhang, X. (2024). MD-TransUNet: TransUNet with Multi-attention and Dilated Convolution for Brain Stroke Lesion Segmentation. In: Gao, H., Wang, X., Voros, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 562. Springer, Cham. https://doi.org/10.1007/978-3-031-54528-3_9

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  • DOI: https://doi.org/10.1007/978-3-031-54528-3_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-54527-6

  • Online ISBN: 978-3-031-54528-3

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