Geometry-Consistent Adversarial Registration Model for Unsupervised Multi-Modal Medical Image Registration

图像配准 计算机科学 人工智能 情态动词 计算机视觉 翻译(生物学) 相似性(几何) 模态(人机交互) 医学影像学 保险丝(电气) 比例(比率) 基本事实 图像(数学) 图像翻译 模式识别(心理学) 信使核糖核酸 电气工程 物理 工程类 基因 量子力学 生物化学 化学 高分子化学
作者
Yanxia Liu,Wenqi Wang,Yuhong Li,Haoyu Lai,Sijuan Huang,Xin Yang
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:27 (7): 3455-3466 被引量:12
标识
DOI:10.1109/jbhi.2023.3270199
摘要

Deformable multi-modal medical image registration aligns the anatomical structures of different modalities to the same coordinate system through a spatial transformation. Due to the difficulties of collecting ground-truth registration labels, existing methods often adopt the unsupervised multi-modal image registration setting. However, it is hard to design satisfactory metrics to measure the similarity of multi-modal images, which heavily limits the multi-modal registration performance. Moreover, due to the contrast difference of the same organ in multi-modal images, it is difficult to extract and fuse the representations of different modal images. To address the above issues, we propose a novel unsupervised multi-modal adversarial registration framework that takes advantage of image-to-image translation to translate the medical image from one modality to another. In this way, we are able to use the well-defined uni-modal metrics to better train the models. Inside our framework, we propose two improvements to promote accurate registration. First, to avoid the translation network learning spatial deformation, we propose a geometry-consistent training scheme to encourage the translation network to learn the modality mapping solely. Second, we propose a novel semi-shared multi-scale registration network that extracts features of multi-modal images effectively and predicts multi-scale registration fields in an coarse-to-fine manner to accurately register the large deformation area. Extensive experiments on brain and pelvic datasets demonstrate the superiority of the proposed method over existing methods, revealing our framework has great potential in clinical application.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
赘婿应助sun_lin采纳,获得10
刚刚
风趣绯完成签到,获得积分20
2秒前
3秒前
墨菲特发布了新的文献求助10
4秒前
YamDaamCaa应助N0V1CE采纳,获得50
4秒前
WNing完成签到,获得积分20
4秒前
小巧的虔发布了新的文献求助10
5秒前
量子星尘发布了新的文献求助10
5秒前
6秒前
Owen应助动听胜采纳,获得10
7秒前
7秒前
LISHAN应助大洁采纳,获得10
7秒前
renyi发布了新的文献求助10
8秒前
科研混子完成签到,获得积分10
9秒前
qqqqwf发布了新的文献求助20
10秒前
12秒前
福老板完成签到,获得积分20
12秒前
忆修完成签到,获得积分10
12秒前
12秒前
所所应助儒雅寒天采纳,获得10
14秒前
七七完成签到,获得积分20
14秒前
无私啤酒完成签到,获得积分10
15秒前
诸-z完成签到,获得积分10
15秒前
16秒前
动听胜完成签到,获得积分20
17秒前
我是老大应助大观天下采纳,获得10
17秒前
动听胜发布了新的文献求助10
19秒前
叶叶叶完成签到,获得积分20
20秒前
大观天下完成签到,获得积分10
22秒前
22秒前
沉静亦寒完成签到 ,获得积分10
22秒前
hetao286完成签到,获得积分10
23秒前
Skilixta完成签到,获得积分10
24秒前
newgeno2003完成签到,获得积分10
25秒前
福老板发布了新的文献求助10
25秒前
27秒前
自信河马完成签到,获得积分10
27秒前
梁林林完成签到,获得积分10
28秒前
打打应助以泪洗面奶采纳,获得10
29秒前
搜集达人应助砍柴少年采纳,获得10
30秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Toward a Combinatorial Approach for the Prediction of IgG Half-Life and Clearance 500
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3969917
求助须知:如何正确求助?哪些是违规求助? 3514626
关于积分的说明 11175060
捐赠科研通 3249928
什么是DOI,文献DOI怎么找? 1795165
邀请新用户注册赠送积分活动 875617
科研通“疑难数据库(出版商)”最低求助积分说明 804891