Accuracy and reproducibility of manual and semiautomated quantification of MS lesions by MRI
- 19 February 2003
- journal article
- research article
- Published by Wiley in Journal of Magnetic Resonance Imaging
- Vol. 17 (3) , 300-308
- https://doi.org/10.1002/jmri.10258
Abstract
Purpose: To evaluate the accuracy, reproducibility, and speed of two semiautomated methods for quantifying total white matter lesion burden in multiple sclerosis (MS) patients with respect to manual tracing and to other methods presented in recent literature.Materials and Methods: Two methods involving the use of MRI for semiautomated quantification of total lesion burden in MS patients were examined. The first method, geometrically constrained region growth (GEORG), requires user specification of lesion location. The second technique, directed multispectral segmentation (DMSS), requires only the location of a single exemplar lesion. Test data sets included both clinical MS data and MS brain phantoms.Results: The mean processing times were 60 minutes for manual tracing, 10 minutes for region growth, and 3 minutes for directed segmentation. Intra‐ and interoperator coefficients of variation (CVs) were 5.1% and 16.5% for manual tracing, 1.4% and 2.3% for region growth, and 1.5% and 5.2% for directed segmentation. The average deviations from manual tracing were 9% for region growth and 5.7% for directed segmentation.Conclusion: Both semiautomated methods were shown to have a significant advantage over manual tracing in terms of speed and precision. The accuracy of both methods was acceptable, given the high variability of the manual results. J. Magn. Reson. Imaging 2003;17:300–308.Keywords
This publication has 18 references indexed in Scilit:
- Role of magnetic resonance imaging in the diagnosis and monitoring of multiple sclerosis: Consensus report of the White Matter Study GroupJournal of Magnetic Resonance Imaging, 2002
- Magnetic resonance image registration in multiple sclerosis: Comparison with repositioning error and observer-based variabilityJournal of Magnetic Resonance Imaging, 2002
- Automated segmentation of multiple sclerosis lesions by model outlier detectionIEEE Transactions on Medical Imaging, 2001
- Segmentation and measurement of brain structures in MRI including confidence boundsMedical Image Analysis, 2000
- Design and construction of a realistic digital brain phantomIEEE Transactions on Medical Imaging, 1998
- Detection of subpixel anomalies in multispectral infrared imagery using an adaptive Bayesian classifierIEEE Transactions on Geoscience and Remote Sensing, 1998
- Multiple sclerosis lesion quantification using fuzzy-connectedness principlesIEEE Transactions on Medical Imaging, 1997
- A novel volumetric feature extraction technique with applications to MR imagesIEEE Transactions on Medical Imaging, 1997
- An extensible MRI simulator for post-processing evaluationPublished by Springer Nature ,1996
- Computer-assisted registration, segmentation, and 3D reconstruction from images of neuronal tissue sectionsIEEE Transactions on Medical Imaging, 1994