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CI-UNet: melding convnext and cross-dimensional attention for robust medical image segmentation

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Abstract

Deep learning-based methods have recently shown great promise in medical image segmentation task. However, CNN-based frameworks struggle with inadequate long-range spatial dependency capture, whereas Transformers suffer from computational inefficiency and necessitate substantial volumes of labeled data for effective training. To tackle these issues, this paper introduces CI-UNet, a novel architecture that utilizes ConvNeXt as its encoder, amalgamating the computational efficiency and feature extraction capabilities. Moreover, an advanced attention mechanism is proposed to captures intricate cross-dimensional interactions and global context. Extensive experiments on two segmentation datasets, namely BCSD, and CT2USforKidneySeg, confirm the excellent performance of the proposed CI-UNet as compared to other segmentation methods.

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The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

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Funding

This work was supported in Tianjin Research Innovation Project for Postgraduate Students (2022SKY126).

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Author notes

  1. Zhuo Zhang and Yihan Wen contributed equally to this work.

Authors and Affiliations

  1. School of Electronic and Information Engineering, Tiangong University, Tianjin, 300387, China

    Zhuo Zhang

  2. International School of Information Science and Engineering, Dalian University of Technology, Dalian, 116620, LiaoNing, China

    Yihan Wen

  3. Tianjin Cerebral Vascular and Neural Degenerative Disease Key Laboratory, Tianjin Huanhu Hospital, Tianjin, 300350, China

    Xiaochen Zhang & Quanfeng Ma

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  1. Zhuo Zhang
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  2. Yihan Wen
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Correspondence to Xiaochen Zhang or Quanfeng Ma.

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Zhang, Z., Wen, Y., Zhang, X. et al. CI-UNet: melding convnext and cross-dimensional attention for robust medical image segmentation. Biomed. Eng. Lett. 14, 341–353 (2024). https://doi.org/10.1007/s13534-023-00341-4

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