VerteNet -- A Multi-Context Hybrid CNN Transformer for Accurate Vertebral Landmark Localization in Lateral Spine DXA Images 文章
详细信息
- 来源站点
- ArXiv CS.CV
- 作者
- Arooba Maqsood, Zaid Ilyas, Afsah Saleem, Erchuan Zhang, David Suter, Parminder Raina, Jonathan M. Hodgson, John T. Schousboe, William D. Leslie, Joshua R. Lewis, Syed Zulqarnain Gilani
- 文章类型
- PAPER
- 语言
- en
- 发布日期
- 2026-07-13
摘要
arXiv:2502.02097v4 Announce Type: replace Abstract: Vertebral Landmarks Localization in Dual-Energy X-ray Absorptiometry based Lateral Spine Imaging plays a critical role in evaluating spinal alignment, Vertebral Fracture Assessment, and facilitating intervertebral guide placement for Abdominal Aortic Calcification quantification. While lateral spine DXA scans offer advantages such as reduced cost and lower radiation exposure, its analysis remains challenging due to a low signal-to-noise ratio and imaging artifacts. Artificial Intelligence presents a promising approach for improving the precision and accuracy of VLL. In this study, we introduce a novel architecture that employs dual-resolution attention mechanisms to capture both fine-grained local details and broader contextual information. Our approach enhances feature integration by leveraging skip connections and decoder layers through dual-resolution self-attention and cross-attention mechanisms.