Personalized Patch-based Normality Assessment of Brain Atrophy in Alzheimer's Disease.

Jianwei Zhang, Yonggang Shi
{"title":"Personalized Patch-based Normality Assessment of Brain Atrophy in Alzheimer's Disease.","authors":"Jianwei Zhang, Yonggang Shi","doi":"10.1007/978-3-031-43904-9_6","DOIUrl":null,"url":null,"abstract":"<p><p>Cortical thickness is an important biomarker associated with gray matter atrophy in neurodegenerative diseases. In order to conduct meaningful comparisons of cortical thickness between different subjects, it is imperative to establish correspondence among surface meshes. Conventional methods achieve this by projecting surface onto canonical domains such as the unit sphere or averaging feature values in anatomical regions of interest (ROIs). However, due to the natural variability in cortical topography, perfect anatomically meaningful one-to-one mapping can be hardly achieved and the practice of averaging leads to the loss of detailed information. For example, two subjects may have different number of gyral structures in the same region, and thus mapping can result in gyral/sulcal mismatch which introduces noise and averaging in detailed local information loss. Therefore, it is necessary to develop new method that can overcome these intrinsic problems to construct more meaningful comparison for atrophy detection. To address these limitations, we propose a novel personalized patch-based method to improve cortical thickness comparison across subjects. Our model segments the brain surface into patches based on gyral and sulcal structures to reduce mismatches in mapping method while still preserving detailed topological information which is potentially discarded in averaging. Moreover,the personalized templates for each patch account for the variability of folding patterns, as not all subjects are comparable. Finally, through normality assessment experiments, we demonstrate that our model performs better than standard spherical registration in detecting atrophy in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD).</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10948101/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/978-3-031-43904-9_6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Cortical thickness is an important biomarker associated with gray matter atrophy in neurodegenerative diseases. In order to conduct meaningful comparisons of cortical thickness between different subjects, it is imperative to establish correspondence among surface meshes. Conventional methods achieve this by projecting surface onto canonical domains such as the unit sphere or averaging feature values in anatomical regions of interest (ROIs). However, due to the natural variability in cortical topography, perfect anatomically meaningful one-to-one mapping can be hardly achieved and the practice of averaging leads to the loss of detailed information. For example, two subjects may have different number of gyral structures in the same region, and thus mapping can result in gyral/sulcal mismatch which introduces noise and averaging in detailed local information loss. Therefore, it is necessary to develop new method that can overcome these intrinsic problems to construct more meaningful comparison for atrophy detection. To address these limitations, we propose a novel personalized patch-based method to improve cortical thickness comparison across subjects. Our model segments the brain surface into patches based on gyral and sulcal structures to reduce mismatches in mapping method while still preserving detailed topological information which is potentially discarded in averaging. Moreover,the personalized templates for each patch account for the variability of folding patterns, as not all subjects are comparable. Finally, through normality assessment experiments, we demonstrate that our model performs better than standard spherical registration in detecting atrophy in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD).

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
基于贴片的阿尔茨海默病脑萎缩正常性个性化评估
皮质厚度是神经退行性疾病中与灰质萎缩相关的重要生物标志物。为了对不同受试者的皮层厚度进行有意义的比较,必须建立表面网格之间的对应关系。传统方法通过将表面投影到典型域(如单位球面)上或平均解剖学感兴趣区(ROI)的特征值来实现这一目的。然而,由于大脑皮层地形的天然可变性,很难实现完美的解剖学意义上的一对一映射,而且平均值的做法会导致详细信息的丢失。例如,两个受试者在同一区域的回旋结构数量可能不同,因此映射可能导致回旋/丘脑不匹配,从而引入噪声和平均化,导致局部详细信息丢失。因此,有必要开发新的方法来克服这些内在问题,从而为萎缩检测构建更有意义的比较。为了解决这些局限性,我们提出了一种新颖的基于贴片的个性化方法,以改善不同受试者的皮层厚度比较。我们的模型根据脑回和脑沟结构将大脑表面分割成不同的斑块,以减少映射方法中的不匹配,同时还保留了平均化过程中可能被忽略的详细拓扑信息。此外,由于并非所有受试者都具有可比性,每个斑块的个性化模板还考虑到了折叠模式的可变性。最后,我们通过正态性评估实验证明,在检测轻度认知障碍(MCI)和阿尔茨海默病(AD)患者的萎缩方面,我们的模型比标准球形配准效果更好。
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
自引率
0.00%
发文量
0
期刊最新文献
Self-guided Knowledge-Injected Graph Neural Network for Alzheimer's Diseases. Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation. Attention-Enhanced Fusion of Structural and Functional MRI for Analyzing HIV-Associated Asymptomatic Neurocognitive Impairment. Tagged-to-Cine MRI Sequence Synthesis via Light Spatial-Temporal Transformer. Estimation and Analysis of Slice Propagation Uncertainty in 3D Anatomy Segmentation.
×
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