Automatic detection and segmentation of lumbar vertebrae from X-ray images for compression fracture evaluation

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 vertebrae 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 by 160 lumbar X-ray images, resulting in a mean Dice similarity metric of 91.60 +/- 2.22% . The results show that the proposed method achieves accurate and robust identification of each lumbar vertebra and fine segmentation of individual vertebra. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

An automatic X-ray image segmentation technique combining deep-learning and level-set methods is proposed for compression fracture detection and evaluation. This structured hierarchical segmentation method utilizes pose-driven learning and M-net to accurately identify lumbar vertebrae and segment individual vertebrae. Fine tuning segmentation is achieved by combining the level-set method with the obtained segmentation results, resulting in accurate and robust identification of each lumbar vertebra.