Institute of Intelligent Medicine and Biomedical Engineering, Ningbo University, Ningbo, China.
School of Medicine, Ningbo University, Ningbo, China.
J Xray Sci Technol. 2023;31(5):935-949. doi: 10.3233/XST-230025.
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.
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.
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.
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.
A VDVM framework is proposed, which performs well for the detection of vertebrae and achieves good results in vertebral segment matching.
C 臂透视作为脊柱手术的一种有效诊断和治疗方法，可以帮助医生更精确地进行手术。在临床手术中，外科医生通常通过将 C 臂 X 射线图像与数字射线照相术（DR）图像进行比较来确定具体的手术位置。然而，这严重依赖于医生的经验。
本研究设计了一种用于 C 臂 X 射线图像中自动检测椎骨和椎骨节段匹配（VDVM）的框架，以识别椎骨。
所提出的 VDVM 框架主要分为两部分：椎骨检测和椎骨匹配。在第一部分中，使用数据预处理方法来改善 C 臂 X 射线图像和 DR 图像的图像质量。然后使用 YOLOv3 模型检测椎骨，并根据其位置提取椎骨区域。在第二部分中，首先使用 Mobile-Unet 模型分别基于椎骨区域对 C 臂 X 射线图像和 DR 图像的椎骨轮廓进行分割。然后使用最小外接矩形计算轮廓的倾斜角度，并进行相应的校正。最后，应用多椎骨策略来测量椎骨区域的视觉信息保真度，并基于测量结果进行椎骨匹配。
我们使用 382 张 C 臂 X 射线图像和 203 张全长 X 射线图像来训练椎骨检测模型，在 31 张 C 臂 X 射线图像的测试数据集和 31 张腰椎 DR 图像的测试数据集上分别获得了 0.87 和 0.96 的 mAP。最后，我们在 31 张 C 臂 X 射线图像上实现了 0.733 的椎骨节段匹配准确率。
提出了一种 VDVM 框架，该框架在椎骨检测方面表现良好，在椎骨节段匹配方面取得了良好的效果。
