Eurographics/IEEE VGTC Symposium on Visualization : EUROVIS : [proceedings]. Eurographics/IEEE VGTC Symposium on Visualization最新文献

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CellTrackVis: analyzing the performance of cell tracking algorithms. CellTrackVis:分析细胞跟踪算法的性能。
W Li, X Zhang, A Stern, M Birtwistle, F Iuricich
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引用次数: 1
Visualization for Understanding Uncertainty in Activation Volumes for Deep Brain Stimulation 可视化理解脑深部刺激激活体积的不确定性
B. Hollister, Gordon Duffley, C. Butson, Chris R. Johnson, P. Rosen
{"title":"Visualization for Understanding Uncertainty in Activation Volumes for Deep Brain Stimulation","authors":"B. Hollister, Gordon Duffley, C. Butson, Chris R. Johnson, P. Rosen","doi":"10.2312/eurovisshort.20161158","DOIUrl":"https://doi.org/10.2312/eurovisshort.20161158","url":null,"abstract":"We have created the Neurostimulation Uncertainty Viewer (nuView or νView) tool for exploring data arising from deep brain stimulation (DBS). Simulated volume of tissue activated (VTA), using clinical electrode placements, are recorded along with patient outcomes in the Unified Parkinson's disease rating scale (UPDRS). The data is volumetric and sparse, with multi-value patient results for each activated voxel in the simulation. νView provides a collection of visual methods to explore the activated tissue to enhance understanding of electrode usage for improved therapy with DBS.","PeriodicalId":72959,"journal":{"name":"Eurographics/IEEE VGTC Symposium on Visualization : EUROVIS : [proceedings]. Eurographics/IEEE VGTC Symposium on Visualization","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83590702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Pattern Visualization of Human Connectome Data. 人类连接体数据的模式可视化。
Yishi Guo, Yang Wang, Shiaofen Fang, Hongyang Chao, Andrew J Saykin, Li Shen
{"title":"Pattern Visualization of Human Connectome Data.","authors":"Yishi Guo,&nbsp;Yang Wang,&nbsp;Shiaofen Fang,&nbsp;Hongyang Chao,&nbsp;Andrew J Saykin,&nbsp;Li Shen","doi":"10.2312/PE/EuroVisShort/EuroVisShort2012/078-083","DOIUrl":"https://doi.org/10.2312/PE/EuroVisShort/EuroVisShort2012/078-083","url":null,"abstract":"<p><p>The human brain is a complex network with countless connected neurons, and can be described as a \"connectome\". Existing studies on analyzing human connectome data are primarily focused on characterizing the brain networks with a small number of easily computable measures that may be inadequate for revealing complex relationship between brain function and its structural substrate. To facilitate large-scale connectomic analysis, in this paper, we propose a powerful and flexible volume rendering scheme to effectively visualize and interactively explore thousands of network measures in the context of brain anatomy, and to aid pattern discovery. We demonstrate the effectiveness of the proposed scheme by applying it to a real connectome data set.</p>","PeriodicalId":72959,"journal":{"name":"Eurographics/IEEE VGTC Symposium on Visualization : EUROVIS : [proceedings]. Eurographics/IEEE VGTC Symposium on Visualization","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4469287/pdf/nihms695796.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33403179","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: 2
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