Improved Quantitative Spatial Analysis of Spontaneous Muscular Activities using Label Uncertainty and Feature Analysis
Motivation: Understanding the visual representation of spontaneous activities in DWI. Goal(s): Automatically identifying visual differences in patterns of spontaneous muscular activities. Approach: Deep-learning based detection and segmentation with subsequent feature analysis. Results: Feasibility of feature-based clustering in individual subjects was shown. Impact: Investigation of a pipeline for automated image processing for exploring differences in spontaneous muscular activities visible in DWI.
Authors
- Günter Steidle
- Petros Martirosian (ORCID: https://orcid.org/0000-0001-8383-0883)
- Fritz Schick (ORCID: https://orcid.org/0000-0002-4231-3406)
- Martin Schwartz (ORCID: https://orcid.org/0000-0002-9006-7844)
- Bin Yang (ORCID: https://orcid.org/0000-0002-1974-6553)
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2024-11-26
- DOI
- https://doi.org/10.58530/2024/1697
- Primary Topic
- Industrial Vision Systems and Defect Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00