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An Attention-Preserving Network-Based Method for Assisted Segmentation of Osteosarcoma MRI Images

Author

Listed:
  • Feng Liu

    (School of Information Engineering, Shandong Youth University of Political Science, Jinan 250103, China)

  • Fangfang Gou

    (School of Computer Science and Engineering, Central South University, Changsha 410083, China)

  • Jia Wu

    (School of Computer Science and Engineering, Central South University, Changsha 410083, China
    Research Center for Artificial Intelligence, Monash University, Melbourne, VIC 3800, Australia)

Abstract

Osteosarcoma is a malignant bone tumor that is extremely dangerous to human health. Not only does it require a large amount of work, it is also a complicated task to outline the lesion area in an image manually, using traditional methods. With the development of computer-aided diagnostic techniques, more and more researchers are focusing on automatic segmentation techniques for osteosarcoma analysis. However, existing methods ignore the size of osteosarcomas, making it difficult to identify and segment smaller tumors. This is very detrimental to the early diagnosis of osteosarcoma. Therefore, this paper proposes a Contextual Axial-Preserving Attention Network (CaPaN)-based MRI image-assisted segmentation method for osteosarcoma detection. Based on the use of Res2Net, a parallel decoder is added to aggregate high-level features which effectively combines the local and global features of osteosarcoma. In addition, channel feature pyramid (CFP) and axial attention (A-RA) mechanisms are used. A lightweight CFP can extract feature mapping and contextual information of different sizes. A-RA uses axial attention to distinguish tumor tissues by mining, which reduces computational costs and thus improves the generalization performance of the model. We conducted experiments using a real dataset provided by the Second Xiangya Affiliated Hospital and the results showed that our proposed method achieves better segmentation results than alternative models. In particular, our method shows significant advantages with respect to small target segmentation. Its precision is about 2% higher than the average values of other models. For the segmentation of small objects, the DSC value of CaPaN is 0.021 higher than that of the commonly used U-Net method.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:10:p:1665-:d:814485
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    References listed on IDEAS

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    1. Haixin Peng & Huacong Sun & Yanfei Guo, 2021. "3D multi-scale deep convolutional neural networks for pulmonary nodule detection," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-14, January.
    2. Syed Furqan Qadri & Linlin Shen & Mubashir Ahmad & Salman Qadri & Syeda Shamaila Zareen & Muhammad Azeem Akbar, 2022. "SVseg: Stacked Sparse Autoencoder-Based Patch Classification Modeling for Vertebrae Segmentation," Mathematics, MDPI, vol. 10(5), pages 1-19, March.
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    Cited by:

    1. Yuzhou Wu & Cheng Peng & Xuechen Chen & Xin Yao & Zhigang Chen, 2022. "A Data-Driven System Based on Deep Learning for Diagnosis Fetal Cavum Septum Pellucidum in Ultrasound Images," Mathematics, MDPI, vol. 10(23), pages 1-18, December.
    2. 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.
    3. 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.

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