In this paper, we propose an efficient framework for parcellation of white matter tractograms using discriminative dictionary learning. Key to our framework is the learning of a compact dictionary for each fiber bundle so that the streamlines within the bundle can be sufficiently represented. Dictionaries for multiple bundles are combined for whole-brain tractogram representation. These dictionaries are learned jointly to encourage inter-bundle incoherence for discriminative power. The proposed method allows tractograms to be assigned to more than one bundle, catering to scenarios where tractograms cannot be clearly separated. Experiments on a bundle-labeled HCP dataset and an infant dataset highlight the ability of our framework in grouping streamlines into anatomically plausible bundles.
|Title of host publication
|Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
|Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
|Springer Science and Business Media Deutschland GmbH
|Number of pages
|Published - 2020
|23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 2020 Oct 4 → 2020 Oct 8
|Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
|23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
|20/10/4 → 20/10/8
Bibliographical noteFunding Information:
This work was supported in part by NIH grants (NS093842, EB006733, and MH110274) and the efforts of the UNC/UMN Baby Connectome Project Consortium.
© 2020, Springer Nature Switzerland AG.
- Dictionary learning
- Diffusion MRI
- Fiber bundle
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)