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Title:Detection-based 3D-2D vertebra matching
Author(s):Yu, Hanchao
Advisor(s):Huang, Thomas
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):deep neural network
3D-2D registration
object detection
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 thesis, 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 train 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 3D-2D registration dataset. The experimental results show that our framework outperforms the state-of-the-art neural-network-based keypoint matching methods.
Issue Date:2019-10-09
Rights Information:Copyright 2019 Hanchao Yu
Date Available in IDEALS:2020-03-02
Date Deposited:2019-12

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