A method and system for bone image segmentation and measurement based on geometric perception deep learning
By employing a bone image segmentation method based on geometric perception deep learning, the problems of strong subjectivity and topological misjudgment in manual measurement are solved, achieving efficient and accurate bone quality assessment and continuous gradient capture, thereby improving the accuracy and efficiency of osteoporosis screening.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NORTHEASTERN UNIV CHINA
- Filing Date
- 2026-06-05
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies for manually measuring osteoporosis are subject to strong subjectivity, low efficiency, and cannot capture continuous longitudinal bone loss gradients. Traditional deep learning models are prone to topological misjudgments in skeletal structure segmentation.
A skeletal image segmentation method based on geometric perception deep learning is adopted, including a macroscopic localization network, a semantic segmentation network, and anatomical prior processing. By generating an accurate skeletal segmentation mask, extracting two-dimensional continuous morphological indicators, and applying intensity-weighted scoring and bidirectional topological inclusion strategies, the problems of topological violations and boundary leakage are overcome.
It achieves efficient and accurate bone quality assessment, significantly improves Dice similarity coefficient and classification accuracy, can output continuous bone loss gradient without human intervention, has high clinical diagnostic consistency, and is suitable for large-scale screening.