Preliminary study of substantia nigra analysis by tensorial feature extraction.

IF 2.3 3区 医学 Q3 ENGINEERING, BIOMEDICAL
Hayato Itoh, Masahiro Oda, Shinji Saiki, Koji Kamagata, Wataru Sako, Kei-Ichi Ishikawa, Nobutaka Hattori, Shigeki Aoki, Kensaku Mori
{"title":"Preliminary study of substantia nigra analysis by tensorial feature extraction.","authors":"Hayato Itoh, Masahiro Oda, Shinji Saiki, Koji Kamagata, Wataru Sako, Kei-Ichi Ishikawa, Nobutaka Hattori, Shigeki Aoki, Kensaku Mori","doi":"10.1007/s11548-024-03175-2","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Parkinson disease (PD) is a common progressive neurodegenerative disorder in our ageing society. Early-stage PD biomarkers are desired for timely clinical intervention and understanding of pathophysiology. Since one of the characteristics of PD is the progressive loss of dopaminergic neurons in the substantia nigra pars compacta, we propose a feature extraction method for analysing the differences in the substantia nigra between PD and non-PD patients.</p><p><strong>Method: </strong>We propose a feature-extraction method for volumetric images based on a rank-1 tensor decomposition. Furthermore, we apply a feature selection method that excludes common features between PD and non-PD. We collect neuromelanin images of 263 patients: 124 PD and 139 non-PD patients and divide them into training and testing datasets for experiments. We then experimentally evaluate the classification accuracy of the substantia nigra between PD and non-PD patients using the proposed feature extraction method and linear discriminant analysis.</p><p><strong>Results: </strong>The proposed method achieves a sensitivity of 0.72 and a specificity of 0.64 for our testing dataset of 66 non-PD and 42 PD patients. Furthermore, we visualise the important patterns in the substantia nigra by a linear combination of rank-1 tensors with selected features. The visualised patterns include the ventrolateral tier, where the severe loss of neurons can be observed in PD.</p><p><strong>Conclusions: </strong>We develop a new feature-extraction method for the analysis of the substantia nigra towards PD diagnosis. In the experiments, even though the classification accuracy with the proposed feature extraction method and linear discriminant analysis is lower than that of expert physicians, the results suggest the potential of tensorial feature extraction.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"2133-2142"},"PeriodicalIF":2.3000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computer Assisted Radiology and Surgery","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s11548-024-03175-2","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/27 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

Abstract

Purpose: Parkinson disease (PD) is a common progressive neurodegenerative disorder in our ageing society. Early-stage PD biomarkers are desired for timely clinical intervention and understanding of pathophysiology. Since one of the characteristics of PD is the progressive loss of dopaminergic neurons in the substantia nigra pars compacta, we propose a feature extraction method for analysing the differences in the substantia nigra between PD and non-PD patients.

Method: We propose a feature-extraction method for volumetric images based on a rank-1 tensor decomposition. Furthermore, we apply a feature selection method that excludes common features between PD and non-PD. We collect neuromelanin images of 263 patients: 124 PD and 139 non-PD patients and divide them into training and testing datasets for experiments. We then experimentally evaluate the classification accuracy of the substantia nigra between PD and non-PD patients using the proposed feature extraction method and linear discriminant analysis.

Results: The proposed method achieves a sensitivity of 0.72 and a specificity of 0.64 for our testing dataset of 66 non-PD and 42 PD patients. Furthermore, we visualise the important patterns in the substantia nigra by a linear combination of rank-1 tensors with selected features. The visualised patterns include the ventrolateral tier, where the severe loss of neurons can be observed in PD.

Conclusions: We develop a new feature-extraction method for the analysis of the substantia nigra towards PD diagnosis. In the experiments, even though the classification accuracy with the proposed feature extraction method and linear discriminant analysis is lower than that of expert physicians, the results suggest the potential of tensorial feature extraction.

Abstract Image

通过张量特征提取进行黑质分析的初步研究
目的:帕金森病(PD)是老龄化社会中常见的进行性神经退行性疾病。为了及时进行临床干预和了解病理生理学,我们需要早期帕金森病生物标志物。由于帕金森氏症的特征之一是黑质紧实部多巴胺能神经元的逐渐丧失,我们提出了一种特征提取方法,用于分析帕金森氏症患者和非帕金森氏症患者黑质的差异:方法:我们提出了一种基于秩-1张量分解的容积图像特征提取方法。方法:我们提出了基于秩-1张量分解的容积图像特征提取方法,并应用特征选择方法排除了帕金森病和非帕金森病之间的共同特征。我们收集了 263 名患者的神经黑素图像:我们收集了 263 名患者的神经黑素图像:124 名帕金森病患者和 139 名非帕金森病患者,并将其分为训练数据集和测试数据集进行实验。然后,我们利用所提出的特征提取方法和线性判别分析对黑质和非黑质病变患者的分类准确性进行了实验评估:结果:对于由 66 名非帕金森病患者和 42 名帕金森病患者组成的测试数据集,所提出的方法达到了 0.72 的灵敏度和 0.64 的特异性。此外,我们还通过秩-1张量与选定特征的线性组合,将黑质中的重要模式可视化。可视化模式包括腹外侧层,在腹外侧层可以观察到帕金森病患者神经元的严重损失:我们开发了一种新的特征提取方法,用于分析黑质以诊断帕金森病。在实验中,尽管使用所提出的特征提取方法和线性判别分析的分类准确率低于专家医师的分类准确率,但实验结果表明了张量特征提取的潜力。
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
International Journal of Computer Assisted Radiology and Surgery
International Journal of Computer Assisted Radiology and Surgery ENGINEERING, BIOMEDICAL-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
5.90
自引率
6.70%
发文量
243
审稿时长
6-12 weeks
期刊介绍: The International Journal for Computer Assisted Radiology and Surgery (IJCARS) is a peer-reviewed journal that provides a platform for closing the gap between medical and technical disciplines, and encourages interdisciplinary research and development activities in an international environment.
×
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