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纤维束监督分类的作用

Supervised Segmentation of Fiber Tracts
课程网址: http://videolectures.net/simbad2011_olivetti_segmentation/  
主讲教师: Emanuele Olivetti
开课单位: 布鲁诺凯斯勒基金会
开课时间: 2011-10-17
课程语种: 英语
中文简介:
在这项工作中,我们研究了来自纤维束成像数据的监督管道分割问题,这是从扩散磁共振图像中提取的大脑连通性的矢量表示。我们报告基于数据集的案例研究,其中对于三个受试者的每个纤维束成像,八个主要解剖学区域的分割由专家神经解剖学家手动操作。编码不相似区域的域特定距离不允许定义正半确定核函数。我们表明,基于这种距离的不相似表示能够成功地设计分类器。该方法提供了稳健的编码,使用线性分类器证明是有效的。我们的实证分析表明,我们获得了比以前提出的方法更好的道分割。
课程简介: In this work we study the problem of supervised tract segmentation from tractography data, a vectorial representation of the brain connectivity extracted from diffusion magnetic resonance images. We report a case study based on a dataset where for each tractography of three subjects the segmentation of eight major anatomical tracts was manually operated by expert neuroanatomists. Domain specific distances that encodes the dissimilarity of tracts do not allow to define a positive semi-definite kernel function. We show that a dissimilarity representation based on such distances enables the successful design of a classifier. This approach provides a robust encoding which proves to be effective using a linear classifier. Our empirical analysis shows that we obtain better tract segmentation than previously proposed methods.
关 键 词: 纤维束成像; 监督管道; 磁共振图像; 线性分类器
课程来源: 视频讲座网
最后编审: 2020-06-08:cxin
阅读次数: 26