TractSeg - Fast and accurate white matter tract segmentation
Abstract: 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 o...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
4 August 2018
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| In: |
NeuroImage
Year: 2018, Volume: 183, Pages: 239-253 |
| ISSN: | 1095-9572 |
| DOI: | 10.1016/j.neuroimage.2018.07.070 |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1016/j.neuroimage.2018.07.070
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| Author Notes: | Jakob Wasserthal, Peter Neher, Klaus H. Maier-Hein |
| Summary: | Abstract: 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. |
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| Item Description: | Gesehen am 27.06.2019 |
| Physical Description: | Online Resource |
| ISSN: | 1095-9572 |
| DOI: | 10.1016/j.neuroimage.2018.07.070 |