  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.
