TY - JOUR
T1 - VDVM
T2 - An automatic vertebrae detection and vertebral segment matching framework for C-arm X-ray image identification
AU - Zhang, Ruyi
AU - Hu, Yiwei
AU - Zhang, Kai
AU - Lan, Guanhua
AU - Peng, Liang
AU - Zhu, Yabin
AU - Qian, Wei
AU - Yao, Yudong
N1 - Publisher Copyright:
© 2023 - IOS Press. All rights reserved.
PY - 2023
Y1 - 2023
N2 - Background: C-arm fluoroscopy, as an effective diagnosis and treatment method for spine surgery, can help doctors perform surgery procedures more precisely. In clinical surgery, the surgeon often determines the specific surgical location by comparing C-arm X-ray images with digital radiography (DR) images. However, this heavily relies on the doctor's experience. Objective: In this study, we design a framework for automatic vertebrae detection as well as vertebral segment matching (VDVM) for the identification of vertebrae in C-arm X-ray images. Methods: The proposed VDVM framework is mainly divided into two parts: vertebra detection and vertebra matching. In the first part, a data preprocessing method is used to improve the image quality of C-arm X-ray images and DR images. The YOLOv3 model is then used to detect the vertebrae, and the vertebral regions are extracted based on their position. In the second part, the Mobile-Unet model is first used to segment the vertebrae contour of the C-arm X-ray image and DR image based on vertebral regions respectively. The inclination angle of the contour is then calculated using the minimum bounding rectangle and corrected accordingly. Finally, a multi-vertebra strategy is applied to measure the visual information fidelity for the vertebral region, and the vertebrae are matched based on the measured results. Results: We use 382 C-arm X-ray images and 203 full length X-ray images to train the vertebra detection model, and achieve a mAP of 0.87 in the test dataset of 31 C-arm X-ray images and 0.96 in the test dataset of 31 lumbar DR images. Finally, we achieve a vertebral segment matching accuracy of 0.733 on 31 C-arm X-ray images. Conclusions: A VDVM framework is proposed, which performs well for the detection of vertebrae and achieves good results in vertebral segment matching.
AB - Background: C-arm fluoroscopy, as an effective diagnosis and treatment method for spine surgery, can help doctors perform surgery procedures more precisely. In clinical surgery, the surgeon often determines the specific surgical location by comparing C-arm X-ray images with digital radiography (DR) images. However, this heavily relies on the doctor's experience. Objective: In this study, we design a framework for automatic vertebrae detection as well as vertebral segment matching (VDVM) for the identification of vertebrae in C-arm X-ray images. Methods: The proposed VDVM framework is mainly divided into two parts: vertebra detection and vertebra matching. In the first part, a data preprocessing method is used to improve the image quality of C-arm X-ray images and DR images. The YOLOv3 model is then used to detect the vertebrae, and the vertebral regions are extracted based on their position. In the second part, the Mobile-Unet model is first used to segment the vertebrae contour of the C-arm X-ray image and DR image based on vertebral regions respectively. The inclination angle of the contour is then calculated using the minimum bounding rectangle and corrected accordingly. Finally, a multi-vertebra strategy is applied to measure the visual information fidelity for the vertebral region, and the vertebrae are matched based on the measured results. Results: We use 382 C-arm X-ray images and 203 full length X-ray images to train the vertebra detection model, and achieve a mAP of 0.87 in the test dataset of 31 C-arm X-ray images and 0.96 in the test dataset of 31 lumbar DR images. Finally, we achieve a vertebral segment matching accuracy of 0.733 on 31 C-arm X-ray images. Conclusions: A VDVM framework is proposed, which performs well for the detection of vertebrae and achieves good results in vertebral segment matching.
KW - C-arm X-ray image
KW - DR image
KW - deep learning
KW - image identification
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U2 - 10.3233/XST-230025
DO - 10.3233/XST-230025
M3 - Article
C2 - 37393485
AN - SCOPUS:85171600672
SN - 0895-3996
VL - 31
SP - 935
EP - 949
JO - Journal of X-Ray Science and Technology
JF - Journal of X-Ray Science and Technology
IS - 5
ER -