解决髋关节置换中模糊影像的骨骼点定位难题
UNSCT-HRNet: Modeling Anatomical Uncertainty for Landmark Detection in Total Hip Arthroplasty
- 融合空间关系与不确定性估计，自适应处理不规则影像
- 在非结构化数据上指标提升超60%，鲁棒性显著增强
- 适合医学影像分析、手术规划等临床场景应用
全髋关节置换（THA）依赖放射影像中的精确骨骼点定位，但患者体位不规则或解剖标志遮挡导致的数据无序性给现有方法带来挑战。为此，本文提出UNSCT-HRNet（无序CT-高分辨率网络），结合坐标卷积与极化注意力的空间关系融合（SRF）模块，以及基于熵的不确定性估计（UE）模块，增强模型对复杂空间关系的捕捉能力并确保预测符合解剖学合理性。该方法无需依赖固定数量的点进行预测，适用于无序数据，在多个指标上相比现有方法性能提升超过60%。实验表明，该方法在结构化数据上也保持良好表现。整体而言，UNSCT-HRNet可作为可靠的自动化解决方案，用于THA术前规划与术后监测。
原文摘要 · Abstract (English)
Total hip arthroplasty (THA) relies on accurate landmark detection from radiographic images, but unstructured data caused by irregular patient postures or occluded anatomical markers pose significant challenges for existing methods. To address this, we propose UNSCT-HRNet (Unstructured CT - High-Resolution Net), a deep learning-based framework that integrates a Spatial Relationship Fusion (SRF) module and an Uncertainty Estimation (UE) module. The SRF module, utilizing coordinate convolution and polarized attention, enhances the model's ability to capture complex spatial relationships. Meanwhile, the UE module which based on entropy ensures predictions are anatomically relevant. For unstructured data, the proposed method can predict landmarks without relying on the fixed number of points, which shows higher accuracy and better robustness comparing with the existing methods. Our UNSCT-HRNet demonstrates over a 60% improvement across multiple metrics in unstructured data. The experimental results also reveal that our approach maintains good performance on the structured dataset. Overall, the proposed UNSCT-HRNet has the potential to be used as a new reliable, automated solution for THA surgical planning and postoperative monitoring.
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