Rethinking Fetal Brain Atlas Construction: A Deep Learning Perspective

doi:10.1007/978-3-031-73260-7_9

	Rethinking Fetal Brain Atlas Construction: A Deep Learning Perspective
	Zhang, Kai1 ; Huang, Shijie1 ; Zhu, Fangmei 2; Ding, Zhongxiang 2; Chen, Geng 3; Shen, Dinggang1,4,5
	2025
会议录名称	LECTURE NOTES IN COMPUTER SCIENCE (INCLUDING SUBSERIES LECTURE NOTES IN ARTIFICIAL INTELLIGENCE AND LECTURE NOTES IN BIOINFORMATICS)
ISSN	0302-9743
卷号	14747 LNCS
页码	94-104
发表状态	已发表
DOI	10.1007/978-3-031-73260-7_9
摘要	Atlas construction is a crucial task for the analysis of fetal brain magnetic resonance imaging (MRI). Traditional registration-based methods for atlas construction may suffer from issues such as inaccurate registration and difficulty in defining morphology and geometric information. To address these challenges, we propose a novel deep learning-based approach for fetal brain atlas construction, which can replace traditional registration-based methods. Our fundamental assumption is that, in the feature space, the atlas is positioned at the center of a group of images, with the minimum distance to all images. Our approach utilizes the powerful representation ability of deep learning methods to learn the complex anatomical structure of the brain at multiple scales, by introducing a distance loss function to minimize the sum of distances between the atlas and all images in the group. We further utilize tissue maps as a structural guide to constrain our results, making them more physiologically realistic. To the best of our knowledge, we are the first to construct fetal brain atlases with powerful deep learning techniques. Our experiments on a large-scale fetal brain MRI dataset demonstrate that our approach can construct fetal brain atlases with better performance than previous registration-based methods while avoiding their limitations. Our code is publicly available at https://github.com/ZhangKai47/FetalBrainAtlas. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
关键词	Brain Contrastive Learning Deep learning Image registration Medical imaging Atlas construction Brain atlas Deep learning Feature space Fetal brain Geometric information Learn+ Learning methods Learning-based approach Minimums distance
会议名称	9th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2024, held in Conjunction with the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024
出版地	GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
会议地点	Marrakesh, Morocco
会议日期	October 6, 2024 - October 6, 2024
URL	查看原文
收录类别	EI ; CPCI-S
语种	英语
资助项目	National Natural Science Foundation of China["62131015","62250710165","U23A20295","2022ZD0209000"] ; Shanghai Municipal Central Guided Local Science and Technology Development Fund[YDZX20233100001001] ; Key R&D Program of Guangdong Province, China["2023B0303040001","2021B0101420006"]
WOS研究方向	Computer Science ; Neurosciences & Neurology ; Obstetrics & Gynecology ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Computer Science, Artificial Intelligence ; Neuroimaging ; Obstetrics & Gynecology ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001437147800009
出版者	Springer Science and Business Media Deutschland GmbH
EI入藏号	20244417277456
EI主题词	Magnetic resonance imaging
EISSN	1611-3349
EI分类号	101.1 ; 1101.2 ; 1101.2.1 ; 1106.3.1 ; 709 Electrical Engineering, General ; 746 Imaging Techniques
原始文献类型	Conference article (CA)
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/449160
专题	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	Chen, Geng; Shen, Dinggang
作者单位	1.School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai; 201210, China 2.Department of Radiology, Affiliated Hangzhou First People’s Hospital, Westlake University School of Medicine, Hangzhou; 310006, China 3.National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an; 710072, China 4.Shanghai United Imaging Intelligence Co., Ltd., Shanghai; 200230, China 5.Shanghai Clinical Research and Trial Center, Shanghai; 201210, 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	Zhang, Kai,Huang, Shijie,Zhu, Fangmei,et al. Rethinking Fetal Brain Atlas Construction: A Deep Learning Perspective[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:Springer Science and Business Media Deutschland GmbH,2025:94-104.