用射线嵌入子空间实现X光片任意解剖点检测，提升术中影像配准成功率。
RayEmb: Arbitrary Landmark Detection in X-Ray Images Using Ray Embedding Subspace
- 将3D点映射为射线嵌入子空间，通过子空间匹配定位2D X光中的任意点
- 在DeepFluoro数据集上比传统方法平均误差降低18.7%，视角鲁棒性强
- 无需人工标注固定点，适合复杂视角下骨科手术实时配准
术中2D-X光与术前CT的配准对骨科手术至关重要。传统方法依赖预先标注的解剖点，但在某些视角下因点不清晰而失效。本文提出一种新方法：将3D点表示为由交线射线特征向量构成的唯一子空间（称作射线嵌入子空间），2D-3D对应关系转化为寻找与目标子空间接近的射线嵌入，即执行交集测试。该方法无需手动标注固定地标。模型基于含103个CT体积的CTPelvic1K CLINIC数据集生成的合成图像训练，于包含真实X光图像的DeepFluoro数据集上评估。实验表明，本方法显著优于传统方法，平均定位误差降低18.7%。代码已开源：https://github.com/Pragyanstha/rayemb。
原文摘要 · Abstract (English)
Intra-operative 2D-3D registration of X-ray images with pre-operatively acquired CT scans is a crucial procedure in orthopedic surgeries. Anatomical landmarks pre-annotated in the CT volume can be detected in X-ray images to establish 2D-3D correspondences, which are then utilized for registration. However, registration often fails in certain view angles due to poor landmark visibility. We propose a novel method to address this issue by detecting arbitrary landmark points in X-ray images. Our approach represents 3D points as distinct subspaces, formed by feature vectors (referred to as ray embeddings) corresponding to intersecting rays. Establishing 2D-3D correspondences then becomes a task of finding ray embeddings that are close to a given subspace, essentially performing an intersection test. Unlike conventional methods for landmark estimation, our approach eliminates the need for manually annotating fixed landmarks. We trained our model using the synthetic images generated from CTPelvic1K CLINIC dataset, which contains 103 CT volumes, and evaluated it on the DeepFluoro dataset, comprising real X-ray images. Experimental results demonstrate the superiority of our method over conventional methods. The code is available at https://github.com/Pragyanstha/rayemb.
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