A Novel Framework for 3D-2D Vertebra Matching

Hanchao Yu, Yang Fu, Haichao Yu, Jianbo Jiao, Yunchao Wei, Xinchao Wang, Bihan Wen, Zhangyang Wang, Matthew Bramlet, Thenkurussi Kesavadas, Honghui Shi, Thomas Huang

Abstract:

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 the 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 the 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.