Tracing 3D Anatomy in 2D Strokes: A Multi-Stage Projection Driven Approach to Cervical Spine Fracture Identification
arXiv:2601.15235v4 Announce Type: replace-cross Abstract: Cervical spine fractures require rapid and accurate diagnosis, yet automatic CT interpretation remains challenging as subtle injuries must be assessed across large 3D volumes. We ask whether full 3D vertebra segmentation is necessary for automated fracture recognition, or whether vertebra masks approximated from 2D projections can preserve sufficient diagnostic context. We propose an end-to-end pipeline that localizes the cervical spine, estimates C1-C7 vertebra masks from optimized 2D projections, and uses the resulting vertebra-level
Advances in AI, particularly in computer vision and machine learning optimization, are enabling more sophisticated and efficient medical image analysis techniques.
This development can significantly improve the speed and accuracy of critical diagnoses like cervical spine fractures, reducing healthcare costs and improving patient outcomes.
The reliance on full 3D segmentation for complex medical diagnoses may decrease, potentially simplifying AI model development and computational requirements for certain applications.
- · Healthcare providers
- · Medical imaging AI developers
- · Patients with spinal injuries
- · Radiologists
- · Traditional manual image analysis workflows
Automated diagnosis of cervical spine fractures becomes faster and more accessible.
Generalized AI approaches could be adopted for other complex anatomical fracture identifications.
Reduced diagnostic bottlenecks could free up radiologist time for more nuanced or complex cases, reshaping their role.
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Read at arXiv cs.AI