A Novel Framework for 3D-2D Vertebra Matching

3D-2D medical image matching is a crucial task in image-guided surgery, image-guided radiation therapy and minimally invasive surgery. The task relies on identifying the correspondence between a 2D reference image and the 2D projection of 3D target image. In this paper, we propose a novel image matching framework between 3D CT projection and 2D X-ray image, tailored for vertebra images. The main idea is to learn a vertebra detector by means of deep neural network. The detected vertebra is represented by a bounding box in the 3D CT projection. Next, the bounding box annotated by the doctor on the X-ray image is matched to the corresponding box in the 3D projection. We evaluate our proposed method on our own-collected 3D-2D registration dataset. The experimental results show that our framework outperforms the state-of-the-art neural network-based keypoint matching methods.

3D - 2D医学图像匹配在图像引导手术、图像引导放射治疗和微创手术中是一项关键任务。该任务依赖于识别2D参考图像和3D目标图像的2D投影之间的对应关系。在本文中，我们提出了一种新颖的3D CT投影和2D X射线图像之间的图像匹配框架，专门针对脊椎图像。主要思路是通过深度神经网络学习一个脊椎检测器。检测到的脊椎在3D CT投影中由一个边界框表示。接下来，医生在X射线图像上标注的边界框与3D投影中相应的框进行匹配。我们在自己收集的3D - 2D配准数据集上评估了我们提出的方法。实验结果表明，我们的框架优于现有的基于神经网络的关键点匹配方法。