Multimodal brain image analysis and mathematical foundations of computational anatomy : 4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17,...最新文献

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Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy: 4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings 多模态脑图像分析和计算解剖学的数学基础:第四届国际研讨会,MBIA 2019,第七届国际研讨会,MFCA 2019,与MICCAI 2019一起举行,中国深圳,2019年10月17日,论文集
Dajiang Zhu, Jingwen Yan, Heng Huang, Li Shen, P. Thompson, C. Westin, X. Pennec, S. Joshi, M. Nielsen, Tom Fletcher, S. Durrleman, S. Sommer
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引用次数: 9
BrainPainter: A software for the visualisation of brain structures, biomarkers and associated pathological processes. BrainPainter:脑结构、生物标记和相关病理过程的可视化软件。
Răzvan V Marinescu, Arman Eshaghi, Daniel C Alexander, Polina Golland
{"title":"BrainPainter: A software for the visualisation of brain structures, biomarkers and associated pathological processes.","authors":"Răzvan V Marinescu,&nbsp;Arman Eshaghi,&nbsp;Daniel C Alexander,&nbsp;Polina Golland","doi":"10.1007/978-3-030-33226-6_13","DOIUrl":"https://doi.org/10.1007/978-3-030-33226-6_13","url":null,"abstract":"<p><p>We present BrainPainter, a software that automatically generates images of highlighted brain structures given a list of numbers corresponding to the output colours of each region. Compared to existing visualisation software (i.e. Freesurfer, SPM, 3D Slicer), BrainPainter has three key advantages: (1) it does not require the input data to be in a specialised format, allowing BrainPainter to be used in combination with any neuroimaging analysis tools, (2) it can visualise both cortical and subcortical structures and (3) it can be used to generate movies showing dynamic processes, e.g. propagation of pathology on the brain. We highlight three use cases where BrainPainter was used in existing neuroimaging studies: (1) visualisation of the degree of atrophy through interpolation along a user-defined gradient of colours, (2) visualisation of the progression of pathology in Alzheimer's disease as well as (3) visualisation of pathology in subcortical regions in Huntington's disease. Moreover, through the design of BrainPainter we demonstrate the possibility of using a powerful 3D computer graphics engine such as Blender to generate brain visualisations for the neuroscience community. Blender's capabilities, e.g. particle simulations, motion graphics, UV unwrapping, raster graphics editing, raytracing and illumination effects, open a wealth of possibilities for brain visualisation not available in current neuroimaging software. BrainPainter is customisable, easy to use, and can run straight from the web browser: https://brainpainter.csail.mit.edu, as well as from source-code packaged in a docker container: https://github.com/mrazvan22/brain-coloring. It can be used to visualise biomarker data from any brain imaging modality, or simply to highlight a particular brain structure for e.g. anatomy courses.</p>","PeriodicalId":87328,"journal":{"name":"Multimodal brain image analysis and mathematical foundations of computational anatomy : 4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17,...","volume":"11846 ","pages":"112-120"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7853665/pdf/nihms-1586275.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25329242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 31
Surface Foliation Based Brain Morphometry Analysis. 基于表面叶理的脑形态分析。
Chengfeng Wen, Na Lei, Ming Ma, Xin Qi, Wen Zhang, Yalin Wang, Xianfeng Gu
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引用次数: 2
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