  For compression fracture detection and evaluation, an automatic X-ray image
segmentation technique that combines deep-learning and level-set methods is
proposed. Automatic segmentation is much more difficult for X-ray images than
for CT or MRI images because they contain overlapping shadows of
thoracoabdominal structures including lungs, bowel gases, and other bony
structures such as ribs. Additional difficulties include unclear object
boundaries, the complex shape of the vertebra, inter-patient variability, and
variations in image contrast. Accordingly, a structured hierarchical
segmentation method is presented that combines the advantages of two
deep-learning methods. Pose-driven learning is used to selectively identify the
five lumbar vertebra in an accurate and robust manner. With knowledge of the
vertebral positions, M-net is employed to segment the individual vertebra.
Finally, fine-tuning segmentation is applied by combining the level-set method
with the previously obtained segmentation results. The performance of the
proposed method was validated using clinical data, resulting in center position
detection error of $25.35\pm10.86$ and a mean Dice similarity metric of
$91.60\pm2.22\%$.
