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

## TL;DR

This paper presents a hierarchical deep-learning and level-set based method for automatic lumbar vertebra segmentation in X-ray images, improving accuracy for compression fracture assessment despite challenges like overlapping shadows and unclear boundaries.

## Contribution

It introduces a novel structured approach combining pose-driven learning, M-net segmentation, and level-set refinement specifically for lumbar vertebra detection in challenging X-ray images.

## Key findings

- Center position detection error: 25.35±10.86 pixels
- Mean Dice similarity: 91.60±2.22%
- Validated on clinical data with promising results

## Abstract

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 $91.60\pm2.22\%$.