{"ID":2895371,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.09158","arxiv_id":"2507.09158","title":"Automatic Contouring of Spinal Vertebrae on X-Ray using a Novel Sandwich U-Net Architecture","abstract":"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.","short_abstract":"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 manu...","url_abs":"https://arxiv.org/abs/2507.09158","url_pdf":"https://arxiv.org/pdf/2507.09158v1","authors":"[\"Sunil Munthumoduku Krishna Murthy\",\"Kumar Rajamani\",\"Srividya Tirunellai Rajamani\",\"Yupei Li\",\"Qiyang Sun\",\"Bjoern W. Schuller\"]","published":"2025-07-12T06:40:18Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.AI\",\"cs.CV\"]","methods":"[]","has_code":false}