@inproceedings{77c3fdaa51c54d609693cd0619e8f350,
title = "A Novel Framework for 3D-2D Vertebra Matching",
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 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.",
keywords = "3D 2D registration, Hough transform, object detection",
author = "Hanchao Yu and Yang Fu and Haichao Yu and Yunchao Wei and Xinchao Wang and Jianbo Jiao and Matthew Bramlet and Thenkurussi Kesavadas and Honghui Shi and Zhangyang Wang and Bihan Wen and Thomas Huang",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019 ; Conference date: 28-03-2019 Through 30-03-2019",
year = "2019",
month = apr,
day = "22",
doi = "10.1109/MIPR.2019.00029",
language = "English (US)",
series = "Proceedings - 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "121--126",
booktitle = "Proceedings - 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019",
address = "United States",
}