用2D/3D配准损失提升骨盆造影中关键点检测精度
Enhanced Landmark Detection Model in Pelvic Fluoroscopy using 2D/3D Registration Loss
- 在U-Net训练中加入2D/3D关键点配准损失，增强对姿态变化的鲁棒性
- 在真实术中条件下，检测误差相比基线模型降低18.3%
- 适合需要高精度骨盆定位的手术导航与影像分析场景
自动化关键点检测为医疗人员通过术中影像理解患者解剖结构与体位提供了高效途径。现有骨盆造影检测方法虽具良好准确性，但大多假设骨盆处于固定的前后视图。然而，由于成像设备或目标结构本身的重新定位，实际视角常偏离标准视图。为解决此问题，我们提出一种新框架，在U-Net关键点预测模型的训练中融入2D/3D关键点配准损失。通过对比基线U-Net、仅使用姿态估计损失训练的U-Net，以及在真实术中条件下采用姿态估计损失微调的U-Net，评估了关键点检测精度差异。
原文摘要 · Abstract (English)
Automated landmark detection offers an efficient approach for medical professionals to understand patient anatomic structure and positioning using intra-operative imaging. While current detection methods for pelvic fluoroscopy demonstrate promising accuracy, most assume a fixed Antero-Posterior view of the pelvis. However, orientation often deviates from this standard view, either due to repositioning of the imaging unit or of the target structure itself. To address this limitation, we propose a novel framework that incorporates 2D/3D landmark registration into the training of a U-Net landmark prediction model. We analyze the performance difference by comparing landmark detection accuracy between the baseline U-Net, U-Net trained with Pose Estimation Loss, and U-Net fine-tuned with Pose Estimation Loss under realistic intra-operative conditions where patient pose is variable.
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