  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.
