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3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue
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Unveiling the third dimension in morphometry with automated quantitative volumetric computations
Lawrence R Frank1,2, Timothy B Rowe3, Doug M Boyer4
1Institute for Engineering in Medicine, Center for Scientific Computation in Imaging, University of California San Diego, 8950 Villa La Jolla Dr., Suite B227, La Jolla, CA, 92037, USA. lfrank@ucsd.edu.
Scientific Reports
|July 15, 2021
Summary
New computational methods, Shape Analysis for Phenomics from Imaging Data (SAPID), rapidly analyze complex 3D imaging data. SAPID enables efficient shape quantification and comparison for large datasets, advancing phenomics research.
Area of Science:
- Biomedical Imaging
- Computational Biology
- Morphometrics
Background:
- Computed tomography (CT) and related technologies generate increasingly large and complex 3D datasets.
- Existing computational methods struggle to keep pace with the analysis of these advanced imaging data volumes.
- Efficient characterization and comparison of 3D shapes are crucial for scientific applications.
Purpose of the Study:
- To introduce novel computational methods for capturing, quantifying, and comparing volumetric information from complex 3D datasets.
- To present the Shape Analysis for Phenomics from Imaging Data (SAPID) method, designed for efficient shape analysis.
- To demonstrate the utility of SAPID for automated segmentation, characterization, and comparative analyses of anatomical structures.
Main Methods:
- Developed spherical wave decomposition (SWD) for fast, automated shape characterization within 3D datasets.
- Implemented symplectomorphic registration with phase space regularization by entropy spectrum pathways (SYMREG) for non-linear volumetric registration.
- Integrated SWD and SYMREG into the Shape Analysis for Phenomics from Imaging Data (SAPID) method.
Main Results:
- SAPID enables rapid, quantitative segmentation and characterization of individual 3D datasets.
- The method facilitates both inter- and intra-specific comparative analyses of anatomical structures.
- SAPID demonstrates significant potential when applied to large collections of 3D data, enabling analysis beyond pairwise comparisons.
Conclusions:
- SAPID offers a powerful computational framework for analyzing large-scale 3D imaging data in phenomics.
- The method supports the generation of normative morphologies for quantifying variations in complex 3D anatomical structures.
- SAPID advances the field of quantitative 3D shape analysis, particularly for large data repositories.
