Automatic Contouring of Spinal Vertebrae on X-Ray using a Novel Sandwich U-Net Architecture
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918090502045696 |
|---|---|
| author | Murthy, Sunil Munthumoduku Krishna Rajamani, Kumar Rajamani, Srividya Tirunellai Li, Yupei Sun, Qiyang Schuller, Bjoern W. |
| author_facet | Murthy, Sunil Munthumoduku Krishna Rajamani, Kumar Rajamani, Srividya Tirunellai Li, Yupei Sun, Qiyang Schuller, Bjoern W. |
| contents | In spinal vertebral mobility disease, accurately extracting and contouring vertebrae is essential for assessing mobility impairments and monitoring variations during flexion-extension movements. Precise vertebral contouring plays a crucial role in surgical planning; however, this process is traditionally performed manually by radiologists or surgeons, making it labour-intensive, time-consuming, and prone to human error. In particular, mobility disease analysis requires the individual contouring of each vertebra, which is both tedious and susceptible to inconsistencies. Automated methods provide a more efficient alternative, enabling vertebra identification, segmentation, and contouring with greater accuracy and reduced time consumption. In this study, we propose a novel U-Net variation designed to accurately segment thoracic vertebrae from anteroposterior view on X-Ray images. Our proposed approach, incorporating a ``sandwich" U-Net structure with dual activation functions, achieves a 4.1\% improvement in Dice score compared to the baseline U-Net model, enhancing segmentation accuracy while ensuring reliable vertebral contour extraction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09158 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automatic Contouring of Spinal Vertebrae on X-Ray using a Novel Sandwich U-Net Architecture Murthy, Sunil Munthumoduku Krishna Rajamani, Kumar Rajamani, Srividya Tirunellai Li, Yupei Sun, Qiyang Schuller, Bjoern W. Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition In spinal vertebral mobility disease, accurately extracting and contouring vertebrae is essential for assessing mobility impairments and monitoring variations during flexion-extension movements. Precise vertebral contouring plays a crucial role in surgical planning; however, this process is traditionally performed manually by radiologists or surgeons, making it labour-intensive, time-consuming, and prone to human error. In particular, mobility disease analysis requires the individual contouring of each vertebra, which is both tedious and susceptible to inconsistencies. Automated methods provide a more efficient alternative, enabling vertebra identification, segmentation, and contouring with greater accuracy and reduced time consumption. In this study, we propose a novel U-Net variation designed to accurately segment thoracic vertebrae from anteroposterior view on X-Ray images. Our proposed approach, incorporating a ``sandwich" U-Net structure with dual activation functions, achieves a 4.1\% improvement in Dice score compared to the baseline U-Net model, enhancing segmentation accuracy while ensuring reliable vertebral contour extraction. |
| title | Automatic Contouring of Spinal Vertebrae on X-Ray using a Novel Sandwich U-Net Architecture |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.09158 |