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Segmentation of the quadratus lumborum muscle using statistical shape modeling
Craig M Engstrom1, Jurgen Fripp, Valer Jurcak
1School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia. craig@hms.uq.edu.au
Journal of Magnetic Resonance Imaging : JMRI
|May 19, 2011
Summary
Automated statistical shape modeling (SSM) accurately segments the quadratus lumborum (QL) muscle on MRI scans, offering a promising tool for paraspinal muscle analysis.
Area of Science:
- Medical Imaging
- Biomedical Engineering
- Anatomy
Background:
- Accurate segmentation of paraspinal muscles like the quadratus lumborum (QL) is crucial for morphometric analysis.
- Manual segmentation of QL from magnetic resonance (MR) images is time-consuming and subject to inter-observer variability.
Purpose of the Study:
- To compare automated segmentation of the quadratus lumborum (QL) using statistical shape modeling (SSM) against manual segmentation on MR images.
Main Methods:
- A hierarchical 3D-SSM scheme was developed for QL, psoas major (PS), and erector spinae+multifidus (ES+MT) segmentation.
- MR image preprocessing included bias field correction and partial volume interpolation.
- Image registration was used to create MR atlases for initializing and constraining the SSM segmentation.
Main Results:
- Automated SSM segmentation showed high spatial overlap with manual segmentation (median Dice similarity: 0.87).
- The mean average surface distance was low, indicating precise boundary delineation (1.26 mm right QL, 1.32 mm left QL).
Conclusions:
- The developed SSM scheme provides a reliable and automated method for QL segmentation.
- This approach shows promise for future automated morphometric analyses of paraspinal muscles using MR imaging.
