Reliable vertebrae annotations are key to perform analysis of spinal X-ray images. However, obtaining annotation of vertebrae from those images is usually carried out manually due to its complexity (i.e. small structures with varying shape), making it a costly and tedious process. To accelerate this process, we proposed an ensemble pipeline, VertXNet, that combines two state-of-the-art (SOTA) segmentation models (respectively U-Net and Mask R-CNN) to automatically segment and label vertebrae in X-ray spinal images. Moreover, VertXNet introduces a rule-based approach that allows to robustly infer vertebrae labels (by locating the 'reference' vertebrae which are easier to segment than others) for a given spinal X-ray image. We evaluated the proposed pipeline on three spinal X-ray datasets (two internal and one publicly available), and compared against vertebrae annotated by radiologists. Our experimental results have shown that the proposed pipeline outperformed two SOTA segmentation models on our test dataset (MEASURE 1) with a mean Dice of 0.90, vs. a mean Dice of 0.73 for Mask R-CNN and 0.72 for U-Net. To further evaluate the generalization ability of VertXNet, the pre-trained pipeline was directly tested on two additional datasets (PREVENT and NHANES II) and consistent performance was observed with a mean Dice of 0.89 and 0.88, respectively. Overall, VertXNet demonstrated significantly improved performance for vertebra segmentation and labeling for spinal X-ray imaging, and evaluation on both in-house clinical trial data and publicly available data further proved its generalization.

翻译：可靠的椎骨标注是对脊柱X射线图像进行分析的关键。然而，由于椎骨结构微小且形状多变，从这些图像中获取椎骨标注通常需要人工完成，过程既昂贵又繁琐。为加速这一流程，我们提出了一种集成管道VertXNet，该管道结合了两种最先进的（SOTA）分割模型（分别为U-Net和Mask R-CNN），可自动分割并标注脊柱X射线图像中的椎骨。此外，VertXNet引入了一种基于规则的方法，通过定位比其它椎骨更易分割的"参考"椎骨，能够稳健地推断给定脊柱X射线图像中的椎骨标签。我们在三个脊柱X射线数据集（两个内部数据集及一个公开数据集）上评估了所提管道，并与放射科医师标注的椎骨进行了对比。实验结果表明，该管道在我们测试数据集（MEASURE 1）上的平均Dice系数为0.90，优于两个SOTA分割模型（Mask R-CNN为0.73，U-Net为0.72）。为进一步评估VertXNet的泛化能力，我们将预训练管道直接应用于另外两个数据集（PREVENT和NHANES II），观察到了一致的性能表现，平均Dice系数分别为0.89和0.88。总体而言，VertXNet在脊柱X射线成像的椎骨分割与标注方面展现出显著提升的性能，在内部临床试验数据及公开数据上的评估进一步验证了其泛化能力。