{
  "id": "2009.04392",
  "version": "v1",
  "published": "2020-09-09T16:20:02.000Z",
  "updated": "2020-09-09T16:20:02.000Z",
  "title": "Learning Anatomical Segmentations for Tractography from Diffusion MRI",
  "authors": [
    "Christian Ewert",
    "David Kügler",
    "Anastasia Yendiki",
    "Martin Reuter"
  ],
  "comment": "Christian Ewert and David K\\\"ugler contributed equally. Accepted at MICCAI 2020 International Workshop on Computational Diffusion MRI",
  "categories": [
    "eess.IV",
    "cs.LG"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-09-09T16:20:02.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "diffusion mri",
      "learning anatomical segmentations",
      "diffusion space avoids non-linear distortions",
      "demonstrate consistent segmentation results",
      "tractography pipelines"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}