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Multi-modal medical image fusion based on densely-connected high-resolution CNN and hybrid transformer

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Abstract

Multi-modal medical image fusion (MMIF) has found wide application in the field of disease diagnosis and surgical guidance. Despite the popularity of deep learning (DL)-based fusion methods, these DL algorithms cannot provide satisfactory fusion performance due to the difficulty in capturing the local information and the long-range dependencies effectively. To address these issues, this paper has presented an unsupervised MMIF method by combining a densely-connected high-resolution network (DHRNet) with a hybrid transformer. In this method, the local features are firstly extracted from the source image using the DHRNet. Then these features are input into the fine-grained attention module in the hybrid transformer to produce the global features by exploring their long-range dependencies. The local and global features are fused by the projection attention module in the hybrid transformer. Finally, based on the fused features, the fused result is reconstructed by the decoder network. The presented network is trained using an unsupervised loss function including edge preservation value, structural similarity, sum of the correlations of differences and structural tensor. Experiments on various multi-modal medical images show that, compared with several traditional and DL-based fusion methods, the presented method can generate visually better fused results and provide better quantitative metrics values.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (NSFC) (Grant No.: 61871440). The authors also acknowledge the support by the medical ultrasound lab at Huazhong University of Science and Technology for providing GPU computation platform.

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Authors and Affiliations

  1. Department of Biomedical Engineering, College of Life Science and Technology, Ministry of Education Key Laboratory of Molecular Biophysics, Huazhong University of Science and Technology, No 1037, Luoyu Road, Wuhan, China

    Quan Zhou, Shaozhuang Ye, Mingwei Wen, Zhiwen Huang, Mingyue Ding & Xuming Zhang

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  1. Quan Zhou
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  2. Shaozhuang Ye
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  3. Mingwei Wen
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  4. Zhiwen Huang
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  6. Xuming Zhang
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Correspondence to Xuming Zhang.

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Zhou, Q., Ye, S., Wen, M. et al. Multi-modal medical image fusion based on densely-connected high-resolution CNN and hybrid transformer. Neural Comput & Applic 34, 21741–21761 (2022). https://doi.org/10.1007/s00521-022-07635-1

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