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Multi-Scale Tumor Localization Based on Priori Guidance-Based Segmentation Method for Osteosarcoma MRI Images

Author

Listed:
  • Baolong Lv

    (School of Information Engineering, Shandong Youth University of Political Science, Jinan 250102, China)

  • Feng Liu

    (School of Information Engineering, Shandong Youth University of Political Science, Jinan 250102, China)

  • Fangfang Gou

    (School of Computer Science and Engineering, Central South University, Changsha 410017, China)

  • Jia Wu

    (School of Computer Science and Engineering, Central South University, Changsha 410017, China
    Research Center for Artificial Intelligence, Monash University, Melbourne, VIC 3800, Australia)

Abstract

Osteosarcoma is a malignant osteosarcoma that is extremely harmful to human health. Magnetic resonance imaging (MRI) technology is one of the commonly used methods for the imaging examination of osteosarcoma. Due to the large amount of osteosarcoma MRI image data and the complexity of detection, manual identification of osteosarcoma in MRI images is a time-consuming and labor-intensive task for doctors, and it is highly subjective, which can easily lead to missed and misdiagnosed problems. AI medical image-assisted diagnosis alleviates this problem. However, the brightness of MRI images and the multi-scale of osteosarcoma make existing studies still face great challenges in the identification of tumor boundaries. Based on this, this study proposed a prior guidance-based assisted segmentation method for MRI images of osteosarcoma, which is based on the few-shot technique for tumor segmentation and fine fitting. It not only solves the problem of multi-scale tumor localization, but also greatly improves the recognition accuracy of tumor boundaries. First, we preprocessed the MRI images using prior generation and normalization algorithms to reduce model performance degradation caused by irrelevant regions and high-level features. Then, we used a prior-guided feature abdominal muscle network to perform small-sample segmentation of tumors of different sizes based on features in the processed MRI images. Finally, using more than 80,000 MRI images from the Second Xiangya Hospital for experiments, the DOU value of the method proposed in this paper reached 0.945, which is at least 4.3% higher than other models in the experiment. We showed that our method specifically has higher prediction accuracy and lower resource consumption.

Suggested Citation

  • Baolong Lv & Feng Liu & Fangfang Gou & Jia Wu, 2022. "Multi-Scale Tumor Localization Based on Priori Guidance-Based Segmentation Method for Osteosarcoma MRI Images," Mathematics, MDPI, vol. 10(12), pages 1-18, June.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:2099-:d:840778
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    References listed on IDEAS

    as
    1. Gao, Wei & Baskonus, Haci Mehmet, 2022. "Deeper investigation of modified epidemiological computer virus model containing the Caputo operator," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    2. Feng Liu & Fangfang Gou & Jia Wu, 2022. "An Attention-Preserving Network-Based Method for Assisted Segmentation of Osteosarcoma MRI Images," Mathematics, MDPI, vol. 10(10), pages 1-25, May.
    3. Yedong Shen & Fangfang Gou & Jia Wu, 2022. "Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks," Mathematics, MDPI, vol. 10(10), pages 1-27, May.
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    Cited by:

    1. Keke He & Yue Qin & Fangfang Gou & Jia Wu, 2023. "A Novel Medical Decision-Making System Based on Multi-Scale Feature Enhancement for Small Samples," Mathematics, MDPI, vol. 11(9), pages 1-19, April.

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