Learning Anatomical Segmentations for Tractography from Diffusion MRI

• URL: http://arxiv.org/abs/2009.04392v1
• Date: Wed, 9 Sep 2020 16:20:02 GMT
• Title: Learning Anatomical Segmentations for Tractography from Diffusion MRI
• Authors: Christian Ewert and David K\"ugler and Anastasia Yendiki and Martin Reuter
• Abstract summary: We introduce fast, deep learning-based segmentation of 170 anatomical regions directly on diffusion-weighted MR images. We demonstrate consistent segmentation results between 0.70 and 0.87 Dice depending on the tissue type.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning approaches for diffusion MRI have so far focused primarily on voxel-based segmentation of lesions or white-matter fiber tracts. A drawback of representing tracts as volumetric labels, rather than sets of streamlines, is that it precludes point-wise analyses of microstructural or geometric features along a tract. Traditional tractography pipelines, which do allow such analyses, can benefit from detailed whole-brain segmentations to guide tract reconstruction. Here, we introduce fast, deep learning-based segmentation of 170 anatomical regions directly on diffusion-weighted MR images, removing the dependency of conventional segmentation methods on T 1-weighted images and slow pre-processing pipelines. Working natively in diffusion space avoids non-linear distortions and registration errors across modalities, as well as interpolation artifacts. We demonstrate consistent segmentation results between 0 .70 and 0 .87 Dice depending on the tissue type. We investigate various combinations of diffusion-derived inputs and show generalization across different numbers of gradient directions. Finally, integrating our approach to provide anatomical priors for tractography pipelines, such as TRACULA, removes hours of pre-processing time and permits processing even in the absence of high-quality T 1-weighted scans, without degrading the quality of the resulting tract estimates.

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