Manual annotation of vertebrae on spinal X-ray imaging is costly and time-consuming due to bone shape complexity and image quality variations. In this study, we address this challenge by proposing an ensemble method called VertXNet, to automatically segment and label vertebrae in X-ray spinal images. VertXNet combines two state-of-the-art segmentation models, namely U-Net and Mask R-CNN to improve vertebrae segmentation. A main feature of VertXNet is to also infer vertebrae labels thanks to its Mask R-CNN component (trained to detect 'reference' vertebrae) on a given spinal X-ray image. VertXNet was evaluated on an in-house dataset of lateral cervical and lumbar X-ray imaging for ankylosing spondylitis (AS) patients. Our results show that VertXNet can accurately label spinal X-rays (mean Dice of 0.9). It can be used to circumvent the lack of annotated vertebrae without requiring human expert review. This step is crucial to investigate clinical associations by solving the lack of segmentation, a common bottleneck for most computational imaging projects.

翻译：脊髓X射线成像上脊椎膜的手工注释说明费用昂贵,而且由于骨质形状复杂和图像质量的变化而耗费时间。在本研究中,我们通过在X射线脊髓图像中建议一个名为VertXNet的混合方法来应对这一挑战,该方法将自动分割和标签脊椎。VertXNet结合了两种最先进的分层模型,即U-Net和Mask R-CNN,以改善脊椎部分。VertXNet的一个主要特征是,由于它的面具 R-CNN 组件(经训练,在给给定的脊椎X射线图像中检测“参考”脊椎),从而导致脊椎标签的降低。VertXNet是用一个内部数据组来评价的宫颈和腰脊髓炎X射线成像(AS)病人。我们的结果显示,VertXNet可以准确为脊椎X射线(0.9的剂量为Dice)贴上脊椎线(0.9)的一个特征。这个步骤可以用来避免缺少注解的脊椎膜,而无需进行普通的临床分析。