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ShanghaiTech University Knowledge Management System
Hierarchical Symmetric Normalization Registration Using Deformation-Inverse Network | |
2024 | |
会议录名称 | INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI (IF:0.402[JCR-2005],0.000[5-Year]) |
ISSN | 0302-9743 |
卷号 | 15002 |
发表状态 | 已发表 |
DOI | 10.1007/978-3-031-72069-7_62 |
摘要 | Most existing deep learning-based medical image registration methods estimate a single-directional displacement field between the moving and fixed image pair, resulting in registration errors when there are substantial differences between the to-be-registered image pairs. To solve this issue, we propose a symmetric normalization network to estimate the deformations in a bi-directional way. Specifically, our method learns two bi-directional half-way displacement fields, which warp the moving and fixed images to their mean space. Besides, a symmetric magnitude constraint is designed in the mean space to ensure precise registration. Additionally, a deformation-inverse network is employed to obtain the inverse of the displacement field, which is applied to the inference pipeline to compose the final end-to-end displacement field between the moving and fixed images. During inference, our method first estimates the two half-way displacement fields and then composes one half-way displacement field with the inverse of another half. Moreover, we adopt a multi-level strategy to hierarchically perform registration, for gradually aligning images to their mean space, thereby improving accuracy and smoothness. Experimental results on two datasets demonstrate that the proposed method improves registration performance compared with state-of-the-art algorithms. Our code is available at https://github.com/QingRui-Sha/HSyN. |
会议举办国 | The Kingdom of Morocco |
关键词 | Symmetric normalization registration Inverse displacement field Magnitude constraint Multi-level architecture |
会议名称 | International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI |
出版地 | GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND |
会议地点 | MOROCCO |
会议日期 | OCT 06-10, 2024 |
URL | 查看原文 |
收录类别 | SCI ; CPCI-S ; EI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China["62131015","62250710165","U23A20295"] ; Shanghai Municipal Central Guided Local Science and Technology Development Fund[YDZX2023310 0001001] ; Science and Technology Commission of Shanghai Municipality (STCSM)[21010502600] ; Key R&D Program of Guangdong Province, China["2023B0303040001","2021B0101420006"] ; STI 2030-Major Projects[2022ZD0209000] |
WOS研究方向 | Computer Science ; Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging |
WOS记录号 | WOS:001342225800062 |
出版者 | SPRINGER INTERNATIONAL PUBLISHING AG |
EISSN | 1611-3349 |
文献类型 | 会议论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/452303 |
专题 | 生物医学工程学院_PI研究组_孙开聪组 信息科学与技术学院_硕士生 信息科学与技术学院_博士生 生物医学工程学院_PI研究组_沈定刚组 |
通讯作者 | Cao, Xiaohuan; Shen, Dinggang |
作者单位 | 1.School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech, Shanghai, China 2.School of Science and Engineering, Chinese University of Hong Kong, Shenzhen, China 3.Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China 4.Shanghai Clinical Research and Trial Center, Shanghai, China |
第一作者单位 | 上海科技大学 |
通讯作者单位 | 上海科技大学 |
第一作者的第一单位 | 上海科技大学 |
推荐引用方式 GB/T 7714 | Sha, Qingrui,Sun, Kaicong,Xu, Mingze,et al. Hierarchical Symmetric Normalization Registration Using Deformation-Inverse Network[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2024. |
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