TractSeg ‐ Fast and accurate white matter tract segmentation

By Jakob Wasserthal, Peter F. Neher, Klaus Hermann Maier-Hein. 2018

625 citations recorded by the source corpus.

&NA; The individual course of white matter fiber tracts is an important factor for analysis of white matter characteristics in healthy and diseased brains. Diffusion‐weighted MRI tractography in combination with region‐based or clustering‐based selection of streamlines is a unique combination of tools which enables the in‐vivo delineation and analysis of anatomically well‐known tracts. This, however, currently requires complex, computationally intensive processing pipelines which take a lot of time to set up. TractSeg is a novel convolutional neural network‐based approach that directly segments tracts in the field of fiber orientation distribution function (fODF) peaks without using tractography, image registration or parcellation. We demonstrate that the proposed approach is much faster than existing methods while providing unprecedented accuracy, using a population of 105 subjects from the Human Connectome Project. We also show initial evidence that TractSeg is able to generalize to differently acquired data sets for most of the bundles. The code and data are openly available at https://github.com/MIC‐DKFZ/TractSeg/ and https://doi.org/10.5281/zenodo.1088277, respectively.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

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In Cognitive Neuroscience

1. Arvid Lundervold

In Radiology, Nuclear Medicine and Imaging

1. Arvid Lundervold
2. Florian Tramèr
3. Dan Boneh

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “etwork classication of brain functional connectomes [272] Multisite diagnostic classication of schizophrenia using discriminant deep learning with functional connectivity MRI Structural connectomes [273] A convolutional neural network-based approach (https://github.com/ MIC-DKFZ/TractSeg) that direc”