Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation
Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation
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DOI：
10.1016/j.neuroimage.2020.117402
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发表时间：
2021-01-01
期刊：
影响因子：
5.7
通讯作者：
Olivetti, Emanuele
中科院分区：
文献类型：
--
作者：
Berto, Giulia;Bullock, Daniel;Olivetti, Emanuele
人脑中白色物质束的虚拟描绘对于多种应用（例如，术前规划和连接组学）是至关重要的。大量的文献涉及的方法，自动段束从扩散磁共振成像（dMRI）数据间接，通过利用区域之间的连接性或纤维束成像技术获得的纤维路径的几何形状的想法，或直接，通过体积数据中的信息。尽管多年来自动分割方法有了显着的改进，但其分割质量仍不令人满意，特别是在处理具有非常不同特征的数据集时，例如不同的跟踪方法，束大小或数据质量。在这项工作中，我们提出了一种新的，监督的基于流线的分割方法，称为Classifyber，它结合了从地图集，连接模式和纤维路径的几何形状到一个简单的线性模型的信息。通过对从研究到临床领域的多个数据集进行广泛的实验，我们表明，与其他最先进的方法相比，Classifyber大大提高了分割的质量，更重要的是，它在非常多样化的环境中具有鲁棒性。我们提供了一个实现所提出的方法作为开源代码，以及Web服务。
Virtual delineation of white matter bundles in the human brain is of paramount importance for multiple applications, such as pre-surgical planning and connectomics. A substantial body of literature is related to methods that automatically segment bundles from diffusion Magnetic Resonance Imaging (dMRI) data indirectly, by exploiting either the idea of connectivity between regions or the geometry of fiber paths obtained with tractography techniques, or, directly, through the information in volumetric data. Despite the remarkable improvement in automatic segmentation methods over the years, their segmentation quality is not yet satisfactory, especially when dealing with datasets with very diverse characteristics, such as different tracking methods, bundle sizes or data quality. In this work, we propose a novel, supervised streamline-based segmentation method, called Classifyber, which combines information from atlases, connectivity patterns, and the geometry of fiber paths into a simple linear model. With a wide range of experiments on multiple datasets that span from research to clinical domains, we show that Classifyber substantially improves the quality of segmentation as compared to other state-of-the-art methods and, more importantly, that it is robust across very diverse settings. We provide an implementation of the proposed method as open source code, as well as web service.