Interactive Individualized Neuroanatomy Labeling for Neuroanatomy Teaching

Q4 Computer Science
Felippe T. Angelo, R. Voltoline, G. Gonçalves, Shin-Ting Wu
{"title":"Interactive Individualized Neuroanatomy Labeling for Neuroanatomy Teaching","authors":"Felippe T. Angelo, R. Voltoline, G. Gonçalves, Shin-Ting Wu","doi":"10.24132/jwscg.2021.29.4","DOIUrl":null,"url":null,"abstract":"As the imaging technology and the understanding of neurological disease improve, a solid understanding of neu-roanatomy has become increasingly relevant. Neuroanatomy teaching includes the practice of cadaveric dissectionand neuroanatomy atlases consisting of images of a brain with its labeled structures. However, the natural inter-individual neuroanatomical variability cannot be taken into account. This work addresses the individual grossneuroanatomy atlas that could enrich medical students’ experiences with various individual variations in anatomi-cal landmarks and their spatial relationships. We propose to deform the CerebrA cortical atlas into the individualanatomical magnetic resonance imaging data to increase students’ opportunity to contact normal neuroanatomicalvariations in the early stages of studies. Besides, we include interactive queries on the labels/names of neu-roanatomical structures from an individual neuroanatomical atlas in a 3D space. An implementation on top ofSimpleITK library and VMTK-Neuro software is presented. We generated a series of surface and internal neu-roanatomy maps from 16 test volumes to attest to the potential of the proposed technique in brain labeling. Forthe age group between 10 to 75, there is evidence that the superficial cortical labeling is accurate with the visualassessment of the degree of concordance between the neuroanatomical and label boundaries.","PeriodicalId":39283,"journal":{"name":"Journal of WSCG","volume":"102 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of WSCG","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24132/jwscg.2021.29.4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0

Abstract

As the imaging technology and the understanding of neurological disease improve, a solid understanding of neu-roanatomy has become increasingly relevant. Neuroanatomy teaching includes the practice of cadaveric dissectionand neuroanatomy atlases consisting of images of a brain with its labeled structures. However, the natural inter-individual neuroanatomical variability cannot be taken into account. This work addresses the individual grossneuroanatomy atlas that could enrich medical students’ experiences with various individual variations in anatomi-cal landmarks and their spatial relationships. We propose to deform the CerebrA cortical atlas into the individualanatomical magnetic resonance imaging data to increase students’ opportunity to contact normal neuroanatomicalvariations in the early stages of studies. Besides, we include interactive queries on the labels/names of neu-roanatomical structures from an individual neuroanatomical atlas in a 3D space. An implementation on top ofSimpleITK library and VMTK-Neuro software is presented. We generated a series of surface and internal neu-roanatomy maps from 16 test volumes to attest to the potential of the proposed technique in brain labeling. Forthe age group between 10 to 75, there is evidence that the superficial cortical labeling is accurate with the visualassessment of the degree of concordance between the neuroanatomical and label boundaries.
神经解剖学教学中的交互式个性化神经解剖学标签
随着影像学技术和对神经系统疾病认识的提高,对神经解剖学的深入了解变得越来越重要。神经解剖学教学包括尸体解剖的实践和由大脑图像及其标记结构组成的神经解剖学地图集。然而,自然的个体间神经解剖学变异不能被考虑在内。这项工作解决了个体神经解剖学图谱,可以丰富医学生的经验,各种个体差异的解剖标志和他们的空间关系。我们建议将大脑皮质图谱变形为个体解剖磁共振成像数据,以增加学生在研究早期接触正常神经解剖变异的机会。此外,我们还包括在3D空间中对来自单个神经解剖图谱的神经解剖结构的标签/名称的交互式查询。给出了基于simpleitk库和VMTK-Neuro软件的实现。我们从16个测试卷中生成了一系列的表面和内部神经解剖图,以证明所提出的技术在大脑标记方面的潜力。对于10至75岁年龄组,有证据表明,浅表皮层标记是准确的,视觉评估神经解剖和标记边界之间的一致性程度。
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 求助全文
来源期刊
Journal of WSCG
Journal of WSCG Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
0.80
自引率
0.00%
发文量
12
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信