Learning lifespan brain anatomical correspondence via cortical developmental continuity transfer

IF 10.7 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Medical image analysis Pub Date : 2024-08-30 DOI:10.1016/j.media.2024.103328
Lu Zhang , Zhengwang Wu , Xiaowei Yu , Yanjun Lyu , Zihao Wu , Haixing Dai , Lin Zhao , Li Wang , Gang Li , Xianqiao Wang , Tianming Liu , Dajiang Zhu
{"title":"Learning lifespan brain anatomical correspondence via cortical developmental continuity transfer","authors":"Lu Zhang ,&nbsp;Zhengwang Wu ,&nbsp;Xiaowei Yu ,&nbsp;Yanjun Lyu ,&nbsp;Zihao Wu ,&nbsp;Haixing Dai ,&nbsp;Lin Zhao ,&nbsp;Li Wang ,&nbsp;Gang Li ,&nbsp;Xianqiao Wang ,&nbsp;Tianming Liu ,&nbsp;Dajiang Zhu","doi":"10.1016/j.media.2024.103328","DOIUrl":null,"url":null,"abstract":"<div><p>Identifying anatomical correspondences in the human brain throughout the lifespan is an essential prerequisite for studying brain development and aging. But given the tremendous individual variability in cortical folding patterns, the heterogeneity of different neurodevelopmental stages, and the scarce of neuroimaging data, it is difficult to infer reliable lifespan anatomical correspondence at finer scales. To solve this problem, in this work, we take the advantage of the developmental continuity of the cerebral cortex and propose a novel transfer learning strategy: the model is trained from scratch using the age group with the largest sample size, and then is transferred and adapted to the other groups following the cortical developmental trajectory. A novel loss function is designed to ensure that during the transfer process the common patterns will be extracted and preserved, while the group-specific new patterns will be captured. The proposed framework was evaluated using multiple datasets covering four lifespan age groups with 1,000+ brains (from 34 gestational weeks to young adult). Our experimental results show that: 1) the proposed transfer strategy can dramatically improve the model performance on populations (e.g., early neurodevelopment) with very limited number of training samples; and 2) with the transfer learning we are able to robustly infer the complicated many-to-many anatomical correspondences among different brains at different neurodevelopmental stages. (Code will be released soon: <span><span>https://github.com/qidianzl/CDC-transfer</span><svg><path></path></svg></span>).</p></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"99 ","pages":"Article 103328"},"PeriodicalIF":10.7000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical image analysis","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1361841524002536","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Identifying anatomical correspondences in the human brain throughout the lifespan is an essential prerequisite for studying brain development and aging. But given the tremendous individual variability in cortical folding patterns, the heterogeneity of different neurodevelopmental stages, and the scarce of neuroimaging data, it is difficult to infer reliable lifespan anatomical correspondence at finer scales. To solve this problem, in this work, we take the advantage of the developmental continuity of the cerebral cortex and propose a novel transfer learning strategy: the model is trained from scratch using the age group with the largest sample size, and then is transferred and adapted to the other groups following the cortical developmental trajectory. A novel loss function is designed to ensure that during the transfer process the common patterns will be extracted and preserved, while the group-specific new patterns will be captured. The proposed framework was evaluated using multiple datasets covering four lifespan age groups with 1,000+ brains (from 34 gestational weeks to young adult). Our experimental results show that: 1) the proposed transfer strategy can dramatically improve the model performance on populations (e.g., early neurodevelopment) with very limited number of training samples; and 2) with the transfer learning we are able to robustly infer the complicated many-to-many anatomical correspondences among different brains at different neurodevelopmental stages. (Code will be released soon: https://github.com/qidianzl/CDC-transfer).

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
通过大脑皮层发育的连续性转移学习生命周期的大脑解剖对应关系。
确定人脑在整个生命周期中的解剖对应关系是研究大脑发育和衰老的必要前提。但是,由于大脑皮层折叠模式存在巨大的个体差异、不同神经发育阶段的异质性以及神经影像学数据的稀缺性,很难在更精细的尺度上推断出可靠的生命周期解剖学对应关系。为了解决这个问题,在这项工作中,我们利用大脑皮层发育的连续性优势,提出了一种新颖的迁移学习策略:使用样本量最大的年龄组从头开始训练模型,然后按照皮层发育轨迹迁移并适应到其他组。我们设计了一个新颖的损失函数,以确保在转移过程中提取和保留共同模式,同时捕捉特定组别的新模式。我们使用多个数据集对所提出的框架进行了评估,这些数据集涵盖了四个生命年龄组的 1000 多个大脑(从 34 孕周到年轻成人)。实验结果表明1)在训练样本数量非常有限的人群(如早期神经发育)中,所提出的迁移策略可以显著提高模型性能;2)通过迁移学习,我们能够稳健地推断出不同神经发育阶段不同大脑之间复杂的多对多解剖对应关系。(代码即将发布:https://github.com/qidianzl/CDC-transfer)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
期刊最新文献
Corrigendum to "Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge" [Medical Image Analysis, April 2022, Volume 77, 102333]. Editorial for Special Issue on Foundation Models for Medical Image Analysis. Few-shot medical image segmentation with high-fidelity prototypes. The Developing Human Connectome Project: A fast deep learning-based pipeline for neonatal cortical surface reconstruction. AutoFOX: An automated cross-modal 3D fusion framework of coronary X-ray angiography and OCT.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1