Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation

Abstract

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.

Evaluating the reliability of human brain white matter tractometry

“…Nevertheless, further exploration and extension of these results is limited. For example, if other researchers would be interested in comparing our TRR results to another tractometry pipeline (e.g., TRACULA (11), another popular tractometry pipeline) or another bundle recognition algorithm (e.g., TractSeg (51), which uses a neural network to recognize bundles, or Classifyber (52), which uses a linear classifier), their ability to do so would be limited, in the absence of the original raw and/or processed data. Using these resources, it should be possible to re-execute our workflows and replicate most of our results (53).…”

A systematic review of automated methods to perform white matter tract segmentation

“…Tractography (Mori et al, 1999;Basser et al, 2000) involves the algorithmic reconstruction of these WM pathways, generating a multitude of fibers (El Kouby et al, 2005) for each subject. This is followed by the delineation of the obtained fiber trajectories or streamlines into bundles or their association with anatomically well-defined tracts, a process commonly referred to as WM tract segmentation or dissection (Bullock et al, 2019).…”