Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma.

Saima Rathore, Spyridon Bakas, Sarthak Pati, Hamed Akbari, Ratheesh Kalarot, Patmaa Sridharan, Martin Rozycki, Mark Bergman, Birkan Tunc, Ragini Verma, Michel Bilello, Christos Davatzikos
{"title":"Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma.","authors":"Saima Rathore,&nbsp;Spyridon Bakas,&nbsp;Sarthak Pati,&nbsp;Hamed Akbari,&nbsp;Ratheesh Kalarot,&nbsp;Patmaa Sridharan,&nbsp;Martin Rozycki,&nbsp;Mark Bergman,&nbsp;Birkan Tunc,&nbsp;Ragini Verma,&nbsp;Michel Bilello,&nbsp;Christos Davatzikos","doi":"10.1007/978-3-319-75238-9_12","DOIUrl":null,"url":null,"abstract":"<p><p>Quantitative research, especially in the field of radio(geno)mics, has helped us understand fundamental mechanisms of neurologic diseases. Such research is integrally based on advanced algorithms to derive extensive radiomic features and integrate them into diagnostic and predictive models. To exploit the benefit of such complex algorithms, their swift translation into clinical practice is required, currently hindered by their complicated nature. brain-CaPTk is a modular platform, with components spanning across image processing, segmentation, feature extraction, and machine learning, that facilitates such translation, enabling quantitative analyses without requiring substantial computational background. Thus, brain-CaPTk can be seamlessly integrated into the typical quantification, analysis and reporting workflow of a radiologist, underscoring its clinical potential. This paper describes currently available components of brain-CaPTk and example results from their application in glioblastoma.</p>","PeriodicalId":72455,"journal":{"name":"Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-75238-9_12","citationCount":"50","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/978-3-319-75238-9_12","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2018/2/17 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 50

Abstract

Quantitative research, especially in the field of radio(geno)mics, has helped us understand fundamental mechanisms of neurologic diseases. Such research is integrally based on advanced algorithms to derive extensive radiomic features and integrate them into diagnostic and predictive models. To exploit the benefit of such complex algorithms, their swift translation into clinical practice is required, currently hindered by their complicated nature. brain-CaPTk is a modular platform, with components spanning across image processing, segmentation, feature extraction, and machine learning, that facilitates such translation, enabling quantitative analyses without requiring substantial computational background. Thus, brain-CaPTk can be seamlessly integrated into the typical quantification, analysis and reporting workflow of a radiologist, underscoring its clinical potential. This paper describes currently available components of brain-CaPTk and example results from their application in glioblastoma.

Abstract Image

Abstract Image

Abstract Image

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
脑癌成像表型组学工具包(Brain - captk):胶质母细胞瘤定量分析的互动平台。
定量研究,特别是在无线电(基因)组学领域,帮助我们了解神经系统疾病的基本机制。这种研究完全基于先进的算法,以获得广泛的放射学特征,并将其整合到诊断和预测模型中。为了利用这种复杂算法的好处,需要将其迅速转化为临床实践,目前由于其复杂性而受到阻碍。brain-CaPTk是一个模块化平台,具有跨越图像处理、分割、特征提取和机器学习的组件,可以促进这种翻译,实现定量分析,而无需大量的计算背景。因此,脑- captk可以无缝集成到放射科医生典型的量化、分析和报告工作流程中,凸显其临床潜力。本文介绍了目前可用的脑- captk成分及其在胶质母细胞瘤中的应用实例结果。
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
Leveraging 2D Deep Learning ImageNet-trained models for Native 3D Medical Image Analysis. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 8th International Workshop, BrainLes 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Revised Selected Papers Optimization of Deep Learning Based Brain Extraction in MRI for Low Resource Environments. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part II
×
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