矩阵用matlab代码实现-TVB-Pypeline:使用Nipype将自动MRI处理管道移植到Python

上传者: 38631329 | 上传时间: 2021-05-28 14:03:07 | 文件大小: 4.69MB | 文件类型: ZIP
矩阵用matlab代码实现TVB-Pypeline--进行中! 该项目使用Nipype将我们当前的自动化MRI处理管道()映射到Python,从而使内部使用的工具箱易于互换。 有关管道的一般概述,请参见 请注意,该管道会进行大量分析,因此计算量很大。 在使用> 100个CPU内核的高性能群集计算机上进行测试。 安装: 管道使用主要依赖于Python 2.7的Nipype。 以下列表概述了在管道的当前状态下使用的Python工具箱。 有关安装和依赖关系解决的信息,请参见相应的文档页面。 由于Nipype / Python也充当通过Shell接口调用的工具箱的包装,因此,您还必须确保要使用的工具箱已安装在系统上,并且它们的二进制文件/库包含在Shell的搜索路径中。 对于预处理,使用以下工具箱: 当涉及到纤维束描记术时,有很多可用的工具。 它们的用法还非常依赖于如何记录dwMRI数据。 主要的分离点之一是在测量过程中施加的不同扩散梯度强度的数量(即,不同b值的数量)。 如果数据集只有一个大于零的单一值,那么人们会谈论单壳数据。 一旦涉及多个值(> 0),该数据就称为多外壳数据 当前,我们测

文件下载

资源详情

[{"title":"( 74 个子文件 4.69MB ) 矩阵用matlab代码实现-TVB-Pypeline:使用Nipype将自动MRI处理管道移植到Python","children":[{"title":"TVB-Pypeline-master","children":[{"title":"bm_functions","children":[{"title":"__init__.py <span style='color:#111;'> 275B </span>","children":null,"spread":false},{"title":"control_compSC.py <span style='color:#111;'> 465B </span>","children":null,"spread":false},{"title":"debugSCRow.py <span style='color:#111;'> 10.31KB </span>","children":null,"spread":false},{"title":"config.ini <span style='color:#111;'> 25B </span>","children":null,"spread":false},{"title":"connectivity2TVBFS.py <span style='color:#111;'> 7.93KB </span>","children":null,"spread":false},{"title":"generateMasks.py <span style='color:#111;'> 6.03KB </span>","children":null,"spread":false},{"title":"util_functions.py <span style='color:#111;'> 10.25KB </span>","children":null,"spread":false},{"title":"compSC_row.py <span style='color:#111;'> 10.61KB </span>","children":null,"spread":false},{"title":"compFC.py <span style='color:#111;'> 3.19KB </span>","children":null,"spread":false},{"title":"aggregateSC.py <span style='color:#111;'> 9.77KB </span>","children":null,"spread":false}],"spread":true},{"title":".gitignore <span style='color:#111;'> 27B </span>","children":null,"spread":false},{"title":"doc","children":[{"title":"overview.png <span style='color:#111;'> 310.12KB </span>","children":null,"spread":false}],"spread":true},{"title":"examples","children":[{"title":"runPipeOAR.sh <span style='color:#111;'> 1.48KB </span>","children":null,"spread":false},{"title":"oarSetup.sh <span style='color:#111;'> 1.22KB </span>","children":null,"spread":false}],"spread":true},{"title":"notebooks","children":[{"title":"mrtrix","children":[{"title":"mrtrix_thresholding_multiVox.ipynb <span style='color:#111;'> 11.67KB </span>","children":null,"spread":false},{"title":".ipynb_checkpoints","children":[{"title":"mrtrix_tracking-checkpoint.ipynb <span style='color:#111;'> 53.30KB </span>","children":null,"spread":false},{"title":"mrtrix_preproc-checkpoint.ipynb <span style='color:#111;'> 278.27KB </span>","children":null,"spread":false},{"title":"mrtrix_main-checkpoint.ipynb <span style='color:#111;'> 44.12KB </span>","children":null,"spread":false}],"spread":true},{"title":"backup_mrtrix_preproc.ipynb <span style='color:#111;'> 226.84KB </span>","children":null,"spread":false},{"title":"mrtrix_main.ipynb <span style='color:#111;'> 59.94KB </span>","children":null,"spread":false},{"title":"mrtrix_preproc_workflow_graph.dot <span style='color:#111;'> 2.95KB </span>","children":null,"spread":false},{"title":"mrtrix_thresholding_multiVox.py <span style='color:#111;'> 8.08KB </span>","children":null,"spread":false},{"title":"mrtrix_thresholding.ipynb <span style='color:#111;'> 83.54KB </span>","children":null,"spread":false},{"title":"mrtrix_tracking.ipynb <span style='color:#111;'> 7.49KB </span>","children":null,"spread":false},{"title":"mrtrix_preproc_workflow_graph.dot.png <span style='color:#111;'> 194.74KB </span>","children":null,"spread":false},{"title":"mrtrix_preproc.ipynb <span style='color:#111;'> 278.27KB </span>","children":null,"spread":false}],"spread":true},{"title":".ipynb_checkpoints","children":[{"title":"TVB_pipeline-checkpoint.ipynb <span style='color:#111;'> 10.97KB </span>","children":null,"spread":false},{"title":"trk2json-checkpoint.ipynb <span style='color:#111;'> 4.58KB </span>","children":null,"spread":false},{"title":"mrtrix_tracking-checkpoint.ipynb <span style='color:#111;'> 2.85KB </span>","children":null,"spread":false},{"title":"mrtrix_preproc-checkpoint.ipynb <span style='color:#111;'> 227.90KB </span>","children":null,"spread":false},{"title":"debugSCRow-checkpoint.ipynb <span style='color:#111;'> 13.90KB </span>","children":null,"spread":false},{"title":"seedTargetMaskMatch-checkpoint.ipynb <span style='color:#111;'> 12.77KB </span>","children":null,"spread":false},{"title":"mrtrix_main-checkpoint.ipynb <span style='color:#111;'> 4.42KB </span>","children":null,"spread":false},{"title":"preprocSub-checkpoint.ipynb <span style='color:#111;'> 514.85KB </span>","children":null,"spread":false}],"spread":true},{"title":"debugSCRow.ipynb <span style='color:#111;'> 8.79KB </span>","children":null,"spread":false},{"title":"Untitled.ipynb <span style='color:#111;'> 3.56KB </span>","children":null,"spread":false},{"title":"workflow_graph_detailed.dot.png <span style='color:#111;'> 310.77KB </span>","children":null,"spread":false},{"title":"debugSCagg.ipynb <span style='color:#111;'> 3.30KB </span>","children":null,"spread":false},{"title":"trk2json.ipynb <span style='color:#111;'> 69.47KB </span>","children":null,"spread":false},{"title":"mrtrix_preproc_workflow_graph.dot <span style='color:#111;'> 2.95KB </span>","children":null,"spread":false},{"title":"fmri","children":[{"title":"fMRI_preproc.ipynb <span style='color:#111;'> 205.49KB </span>","children":null,"spread":false},{"title":"workflow_graph.dot <span style='color:#111;'> 2.71KB </span>","children":null,"spread":false},{"title":"stat_result.json <span style='color:#111;'> 42.30KB </span>","children":null,"spread":false},{"title":"workflow_graph.dot.png <span style='color:#111;'> 146.31KB </span>","children":null,"spread":false}],"spread":false},{"title":"workflow_graph.dot <span style='color:#111;'> 5.17KB </span>","children":null,"spread":false},{"title":"seedTargetMaskMatch.ipynb <span style='color:#111;'> 27.77KB </span>","children":null,"spread":false},{"title":"preprocSub.ipynb <span style='color:#111;'> 24.48KB </span>","children":null,"spread":false},{"title":"TVB_pipeline.ipynb <span style='color:#111;'> 12.30KB </span>","children":null,"spread":false},{"title":"workflow_graph.dot.png <span style='color:#111;'> 358.42KB </span>","children":null,"spread":false},{"title":"mrtrix_preproc_workflow_graph.dot.png <span style='color:#111;'> 194.74KB </span>","children":null,"spread":false},{"title":"workflow_graph_detailed.dot <span style='color:#111;'> 4.64KB </span>","children":null,"spread":false}],"spread":false},{"title":"README.md <span style='color:#111;'> 11.37KB </span>","children":null,"spread":false},{"title":".idea","children":[{"title":"misc.xml <span style='color:#111;'> 207B </span>","children":null,"spread":false},{"title":"encodings.xml <span style='color:#111;'> 166B </span>","children":null,"spread":false},{"title":"workspace.xml <span style='color:#111;'> 54.12KB </span>","children":null,"spread":false},{"title":"TVB-Pypeline.iml <span style='color:#111;'> 286B </span>","children":null,"spread":false},{"title":"vcs.xml <span style='color:#111;'> 182B </span>","children":null,"spread":false},{"title":"scopes","children":[{"title":"scope_settings.xml <span style='color:#111;'> 139B </span>","children":null,"spread":false}],"spread":true},{"title":"inspectionProfiles","children":[{"title":"Project_Default.xml <span style='color:#111;'> 1.21KB </span>","children":null,"spread":false},{"title":"profiles_settings.xml <span style='color:#111;'> 235B </span>","children":null,"spread":false}],"spread":true},{"title":".name <span style='color:#111;'> 12B </span>","children":null,"spread":false},{"title":"modules.xml <span style='color:#111;'> 278B </span>","children":null,"spread":false}],"spread":true},{"title":"workflows","children":[{"title":"mrtrix","children":[{"title":"mrtrix_tracking.py <span style='color:#111;'> 7.25KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 19B </span>","children":null,"spread":false},{"title":"mrtrix_preproc.py <span style='color:#111;'> 6.94KB </span>","children":null,"spread":false},{"title":"mrtrix_main.py <span style='color:#111;'> 2.85KB </span>","children":null,"spread":false}],"spread":true},{"title":"TVB_pipeline.py <span style='color:#111;'> 13.42KB </span>","children":null,"spread":false},{"title":"feat","children":[{"title":"__init__.py <span style='color:#111;'> 20B </span>","children":null,"spread":false},{"title":"fmri_preproc.py <span style='color:#111;'> 9.12KB </span>","children":null,"spread":false}],"spread":true},{"title":"TVB_workflow_graph_detailed.dot.png <span style='color:#111;'> 1.39MB </span>","children":null,"spread":false},{"title":"TVB_workflow_graph.dot.png <span style='color:#111;'> 839.08KB </span>","children":null,"spread":false},{"title":"TVB_workflow_graph_detailed.dot <span style='color:#111;'> 20.35KB </span>","children":null,"spread":false},{"title":"TVB_workflow_graph.dot <span style='color:#111;'> 9.55KB </span>","children":null,"spread":false},{"title":"preprocSub.py <span style='color:#111;'> 19.57KB </span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明