TractSeg - Fast and accurate white matter tract segmentation

TL;DR

TractSeg introduces a fast, accurate CNN-based method for white matter tract segmentation directly from diffusion MRI data, eliminating complex preprocessing steps and outperforming existing techniques in speed and accuracy.

Contribution

The paper presents a novel CNN approach that segments white matter tracts directly from fODF peaks, simplifying and accelerating the process compared to traditional methods.

Findings

Much faster than existing methods

Provides unprecedented accuracy

Validated on 105 Human Connectome Project subjects

Abstract

The individual course of white matter fiber tracts is an important key for analysis of white matter characteristics in healthy and diseased brains. Uniquely, diffusion-weighted MRI tractography in combination with region-based or clustering-based selection of streamlines allows for the in-vivo delineation and analysis of anatomically well known tracts. This, however, currently requires complex, computationally intensive and tedious-to-set-up processing pipelines. TractSeg is a novel convolutional neural network-based approach that directly segments tracts in the field of fiber orientation distribution function (fODF) peaks without requiring tractography, image registration or parcellation. We demonstrate in 105 subjects from the Human Connectome Project that the proposed approach is much faster than existing methods while providing unprecedented accuracy. The code and data are openly available at https://github.com/MIC-DKFZ/TractSeg/ and https://doi.org/10.5281/zenodo.1088277, respectively.