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Spinal MRI image segmentation based on improved U-Net

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Abstract

Segmenting vertebrae and intervertebral discs from spinal MRI images is essential for the diagnosis and treatment of spinal diseases. However, the similarity among different vertebrae and discs, coupled with pathological variations, complicates the segmentation process of MRI images. To tackle this challenge, this study introduces an improved U-Net architecture for spinal MRI image segmentation. The encoder employs a Multi-scale Feature Fusion Module that integrates shallow and deep features through feature downsampling. Additionally, a Dual Attention Mechanism Module is embedded within both the encoder and decoder to enhance feature extraction capabilities. Furthermore, a Graph Convolutional Module is integrated to capture contextual relationships between vertebrae and intervertebral discs. To mitigate background interference, a Spinal Region Guidance Module is also proposed. Experiments conducted on the public dataset from the MRSpineSeg Challenge yielded an average Dice Similarity Coefficient of 85.11%. The results indicate that our proposed method not only improves the segmentation performance of spinal MRI images but also surpasses other models, which holds significant implications for the clinical diagnosis and treatment of spinal diseases.

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Data availability

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

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant(No.62071161), the Scientific Research Fund of Zhejiang Provincial Department of Education(No.Y202351775) andZhejiang Province College Students’ Science and Technology Innovation Activity Program and XinMiao Talent Program Project(No.2024R407B060).

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Authors and Affiliations

  1. School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China

    Xiajin Mei, Wenxin Zhang & Yuliang Ma

  2. Jinan Key Laboratory of Rehabilitation and Evaluation of Motor Dysfunction, The People’s Hospital of Huaiyin, Jinan, 250100, China

    Xiajin Mei

  3. Shandong BetR Medical Technology Co., Ltd., Jinan, 250100, China

    Mingxu Sun & Yuliang Ma

  4. International Joint Research Laboratory for Autonomous Robotic Systems, Hangzhou, 310018, China

    Yuliang Ma

Authors
  1. Xiajin Mei
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  2. Wenxin Zhang
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Contributions

Mei was responsible for model improvement and experimental verification, Mei and Zhang jointly completed the initial draft writing, Sun and Ma collaborated on providing research directions and project management.

Corresponding author

Correspondence to Yuliang Ma.

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Mei, X., Zhang, W., Sun, M. et al. Spinal MRI image segmentation based on improved U-Net. SIViP 18, 9319–9329 (2024). https://doi.org/10.1007/s11760-024-03548-9

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  • DOI: https://doi.org/10.1007/s11760-024-03548-9

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