Automated Vertebral Segmentation and Measurement of Vertebral Compression Ratio Based on Deep Learning in X-Ray Images.

Vertebral compression fracture is a deformity of vertebral bodies found on lateral spine images. To diagnose vertebral compression fracture, accurate measurement of vertebral compression ratio is required. Therefore, rapid and accurate segmentation of vertebra is important for measuring the vertebra...

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Publicado en:Journal of Digital Imaging Vol. 34; no. 4; pp. 853 - 862
Autores principales: Kim, Dong Hyun, Jeong, Jin Gyo, Kim, Young Jae, Kim, Kwang Gi, Jeon, Ji Young
Formato: diagnostic images research tables/charts Journal Article
Publicado: Springer Nature Aug2021
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Aug2021
      vid: 34
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10278-021-00471-0
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        atl: Automated Vertebral Segmentation and Measurement of Vertebral Compression Ratio Based on Deep Learning in X-Ray Images.
      aug:
        au:
          Kim, Dong Hyun
          Jeong, Jin Gyo
          Kim, Young Jae
          Kim, Kwang Gi
          Jeon, Ji Young
        affil: Department of Medicine, Gachon University College of Medicine, Gil Medical Center, 38-13 Docjeom-ro 3beon-gil, Namdong-gu, 21565, Incheon, Republic of Korea
      sug:
        subj:
          Spinal Fractures Diagnosis
          Fractures, Compression Diagnosis
          Deep Learning
          Lumbar Vertebrae Radiography
          Thoracic Vertebrae Radiography
          Human
          Descriptive Statistics
          Sensitivity and Specificity
          ROC Curve
          Correlation Coefficient
          Models, Statistical
      ab: Vertebral compression fracture is a deformity of vertebral bodies found on lateral spine images. To diagnose vertebral compression fracture, accurate measurement of vertebral compression ratio is required. Therefore, rapid and accurate segmentation of vertebra is important for measuring the vertebral compression ratio. In this study, we used 339 data of lateral thoracic and lumbar vertebra images for training and testing a deep learning model for segmentation. The result of segmentation by the model was compared with the manual measurement, which is performed by a specialist. As a result, the average sensitivity of the dataset was 0.937, specificity was 0.995, accuracy was 0.992, and dice similarity coefficient was 0.929, area under the curve of receiver operating characteristic curve was 0.987, and the precision recall curve was 0.916. The result of correlation analysis shows no statistical difference between the manually measured vertebral compression ratio and the vertebral compression ratio using the data segmented by the model in which the correlation coefficient was 0.929. In addition, the Bland–Altman plot shows good equivalence in which VCR values are in the area within average ± 1.96. In conclusion, vertebra segmentation based on deep learning is expected to be helpful for the measurement of vertebral compression ratio.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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