PURPOSE Existing automated spine alignment is based on original X-rays that are not applicable for teleradiology for spinal deformities patients. We aim to provide a novel automated vertebral segmentation method enabling accurate sagittal alignment detection, with no restrictions imposed by image quality or pathology type. METHODS A total of 428 optical images of original sagittal X-rays taken by smartphones or screenshots for consecutive patients attending our spine clinic were prospectively collected. Of these, 300 were randomly selected and their vertebrae were labelled with Labelme. The ground truth was specialists measured sagittal alignment parameters. Pre-trained Mask R-CNN was fine-tuned and trained to predict the vertebra level(s) on the remaining 128 testing cases. The sagittal alignment parameters including the thoracic kyphosis (TK), lumbar lordosis (LL) and sacral slope (SS) were auto-detected, based on the segmented vertebra. Dice similarity coefficient (DSC) and mean intersection over union (mIoU) were calculated to evaluate the accuracy of the predicted vertebra. The detected sagittal alignments were then quantitatively compared with the ground truth. RESULTS The DSC was 84.6 ± 3.8% and mIoU was 72.1 ± 4.8% indicating accurate vertebra prediction. The sagittal alignments detected were all strongly correlated with the ground truth (p < 0.001). Standard errors of the estimated parameters had a small difference from the specialists' results (3.5° for TK and SS; 3.4° for LL). CONCLUSION This is the first study using fine-tuned Mask R-CNN to predict vertebral locations on optical images of X-rays accurately and automatically. We provide a novel alignment detection method that has a significant application on teleradiology aiding out-of-hospital consultations. These slides can be retrieved under Electronic Supplementary Material.

中文翻译：

---

不管移动电话上获取的源和图像质量如何，均可提供可预测的对准数据的新颖工具：工程师可以为临床医生提供什么。

目的现有的自动脊柱对准是基于原始的X射线，不适用于脊椎畸形患者的放射线照相。我们旨在提供一种新颖的自动椎骨分割方法，该方法可实现准确的矢状面对准检测，而不受图像质量或病理类型的限制。方法前瞻性地收集了428份由智能手机拍摄的原始矢状X射线光学图像或屏幕快照，用于连续接受我们脊柱诊所治疗的患者。其中，随机选择300个，并用Labelme标记其椎骨。事实是专家测量了矢状面对准参数。对预训练的Mask R-CNN进行了微调和训练，以预测其余128个测试用例的椎骨水平。矢状位对准参数包括胸椎后凸畸形（TK），根据分割的椎骨自动检测腰椎前凸（LL）和骨斜率（SS）。计算骰子相似性系数（DSC）和平均联合相交（mIoU）以评估预测椎骨的准确性。然后将检测到的矢状线与地面真相进行定量比较。结果DSC为84.6±3.8％，mIoU为72.1±4.8％，表明椎骨预测准确。所检测到的矢状位均与地面实况密切相关（p <0.001）。估计参数的标准误差与专家的结果相差很小（TK和SS为3.5°； LL为3.4°）。结论这是第一项使用微调的Mask R-CNN准确自动地预测X射线光学图像上椎骨位置的研究。我们提供了一种新颖的对准检测方法，该方法在远程放射学中有重要的应用，可协助院外会诊。这些幻灯片可以在电子补充材料下找到。