Unified Model for Children's Brain Image Segmentation with Co-Registration Framework Guided by Longitudinal MRI

doi:10.1109/JBHI.2024.3393974

	Unified Model for Children's Brain Image Segmentation with Co-Registration Framework Guided by Longitudinal MRI
	Lin Teng1 ; Yichu He 2; Zehong Cao 2; Rui Hua 2; Ye Han 2; Qianjin Feng 3; Feng Shi 2; Dinggang Shen4
	2024
发表期刊	IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (IF:6.7[JCR-2023],7.1[5-Year])
ISSN	2168-2208
EISSN	2168-2208
卷号	PP 期号:99 页码:1-10
发表状态	已发表
DOI	10.1109/JBHI.2024.3393974
摘要	Accurate segmentation of brain structures is crucial for analyzing longitudinal changes in children's brains. However, existing methods are mostly based on models established at a single time-point due to difficulty in obtaining annotated data and dynamic variation of tissue intensity. The main problem with such approaches is that, when conducting longitudinal analysis, images from different time points are segmented by different models, leading to significant variation in estimating development trends. In this paper, we propose a novel unified model with co-registration framework to segment children's brain images covering neonates to preschoolers, which is formulated as two stages. First, to overcome the shortage of annotated data, we propose building gold-standard segmentation with co-registration framework guided by longitudinal data. Second, we construct a unified segmentation model tailored to brain images at 0-6 years old through the introduction of a convolutional network (named SE-VB-Net), which combines our previously proposed VB-Net with Squeeze-and-Excitation (SE) block. Moreover, different from existing methods that only require both T1- and T2-weighted MR images as inputs, our designed model also allows a single T1-weighted MR image as input. The proposed method is evaluated on the main dataset (320 longitudinal subjects with average 2 time-points) and two external datasets (10 cases with 6-month-old and 40 cases with 20-45 weeks, respectively). Results demonstrate that our proposed method achieves a high performance (>92%), even over a single time-point. This means that it is suitable for brain image analysis with large appearance variation, and largely broadens the application scenarios.
关键词	Children Brain tissue segmentation Iterative segmentation with co-registration framework Longitudinal MRI
URL	查看原文
收录类别	EI
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20241916048072
EI主题词	Image segmentation
EI分类号	461.1 Biomedical Engineering ; 461.2 Biological Materials and Tissue Engineering ; 701.2 Magnetism: Basic Concepts and Phenomena ; 746 Imaging Techniques ; 921.6 Numerical Methods
原始文献类型	Article in Press
来源库	IEEE
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/372811
专题	生物医学工程学院生物医学工程学院_PI研究组_沈定刚组生物医学工程学院_博士生生物医学工程学院_博士生
作者单位	1.School of Biomedical Engineering, ShanghaiTech University, Shanghai, China 2.Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China 3.School of Biomedical Engineering, Southern Medical University, Guangzhou, China 4.School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
第一作者单位	生物医学工程学院
第一作者的第一单位	生物医学工程学院
推荐引用方式 GB/T 7714	Lin Teng,Yichu He,Zehong Cao,et al. Unified Model for Children's Brain Image Segmentation with Co-Registration Framework Guided by Longitudinal MRI[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2024,PP(99):1-10.
APA	Lin Teng.,Yichu He.,Zehong Cao.,Rui Hua.,Ye Han.,...&Dinggang Shen.(2024).Unified Model for Children's Brain Image Segmentation with Co-Registration Framework Guided by Longitudinal MRI.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,PP(99),1-10.
MLA	Lin Teng,et al."Unified Model for Children's Brain Image Segmentation with Co-Registration Framework Guided by Longitudinal MRI".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS PP.99(2024):1-10.