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An Automatic Classification Method for Adolescent Idiopathic Scoliosis Based on U-net and Support Vector Machine

机译：基于U-net和支持向量机的青少年特发性脊柱侧弯自动分类方法

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摘要

The traditional manual method for adolescent idiopathic scoliosis diagnosis suffers from observer variability. Doctors need an objective, accurate and fast detection method which would help to overcome the problem encountered by the traditional classification. This study introduces new techniques, including automatic radiograph segmentation, scoliosis measurement and classification, based on artificial intelligence. Firstly, the vertebral region in the radiograph was segmented by U-net and the scoliosis measurement was performed on the segmented image. Secondly SVM classification was conducted by extracting the curve features in posteroanterior images and supplementary parameters in lateral and bending images. Finally, the results of automatic scoliosis measurement were compared with the one made by surgeons and the accuracy of the proposed automatic classification method was verified by a test set. The U-net segmentation model was successfully established to segment the vertebrae and the differences between the measurement results obtained by the automatic and manual measurement method were less than one degree and the accuracy of the automatic curve identification approach was found to be 100%. (C) 2019 Society for Imaging Science and Technology.
机译：青春期特发性脊柱侧弯诊断的传统手动方法存在观察者变异性。医生需要一种客观，准确，快速的检测方法，这将有助于克服传统分类所遇到的问题。这项研究引入了新技术，包括基于人工智能的自动X射线照片分割，脊柱侧弯测量和分类。首先，通过U-net对X线片中的椎骨区域进行分割，并对分割后的图像进行脊柱侧弯测量。其次，通过提取后前图像中的曲线特征以及侧面图像和弯曲图像中的辅助参数来进行SVM分类。最后，将脊柱侧弯自动测量的结果与外科医生的测量结果进行了比较，并通过测试仪验证了所提出的自动分类方法的准确性。成功建立了U-net分割模型，对椎骨进行分割，自动和手动测量方法得到的测量结果之间的差异小于1度，自动曲线识别方法的准确性为100％。 （C）2019影像科学与技术学会。

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  • 来源
    《Journal of Imaging Science and Technology》 |2019年第6期|060502.1-060502.13|共13页
  • 作者

    Tan Zhiqiang; Yang Kai; Sun Yu; Wu Bo; Li Shibo; Hu Ying; Tao Huiren;

  • 作者单位

    Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Minimally Invas Surg Robot & Sys Shenzhen 518055 Peoples R China|Univ Chinese Acad Sci CAS Beijing 100049 Peoples R China;

    Xi An Jiao Tong Univ Affiliated Hosp 2 Dept Orthopaed Xian 710000 Peoples R China;

    Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Minimally Invas Surg Robot & Sys Shenzhen 518055 Peoples R China;

    Shenzhen Univ Clin Med Acad Dept Orthopaed Gen Hosp Shenzhen 518055 Peoples R China;

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