Abstract
The validity of segmentation is an important issue in image processing because it has a direct impact on surgical planning. Binary manual segmentation is not only time-consuming but also lacks the ability of differentiating subtle intensity variations among voxels, particularly for those on the border of a tumor and for different tumor types. Previously we have developed an automated segmentation method that yields voxel-wise continuous probabilistic measures, indicating a level of tumor presence. The goal of this work is to examine three accuracy metrics based on two-sample statistical methods, against the estimated composite latent ground truth derived from several experts’ manual segmentation by a maximum likelihood algorithm. We estimated the distribution functions of the tumor and control voxel data parametrically by assuming a mixture of two beta distributions with different shape parameters. We derived the resulting receiver operating characteristic curves, Dice similarity coefficients, and mutual information, over all possible decision thresholds. Based on each validation metric, an optimal threshold was then computed via maximization. We illustrated these methods on MR imaging data from nine brain tumor cases, three with meningiomas, three astrocytomas, and three other low-grade gliomas. The automated segmentation yielded satisfactory accuracy, with varied optimal thresholds.
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Keywords
- Ground Truth
- Mutual Information
- Receiver Operating Characteristic Curve
- Optimal Threshold
- Automate Segmentation
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References
Warfield S. K., Westin C.-F., Guttmann, C. R. G., Albert, M., Jolesz, F. A., Kikinis, R.: Fractional segmentation of white matter. In Proceedings of Second International Conference on Medical Imaging Computing and Computer Assisted Interventions, Cambridge, UK (1999) 62–71.
Zijdenbos, A. P., Dawant, B.M., Margolin, R. A., Palmer, A. C.: Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Transactions on Medical Imaging 13 (1994) 716–724.
Jaccard, P.: The distribution of flora in the alpine zone. New Phytologist 11 (1912) 37–50.
Dice, L. R.: Measures of the amount of ecologic association between species. Ecology, 26 (1945) 297–302.
Zou, K. H., Hall W. J., Shapiro, D.E.: Smooth nonparametric receiver operating characteristic curves for continuous diagnostic tests. Statistics in Medicine 16 (1997) 2143–2156.
Zou, K. H., Hall W.J.: Two transformation models for estimating an ROC curve derived from continuous data. Journal of Applied Statistics 27 (2000) 621–631.
Dempster, A. P, Laird, N. M., Rubin, D. B.: Maximum-likelihood from incomplete data via the EM algorithm. J. Royal Statistical Society (Ser. B) 39 (1977) 34–37.
McLachlan G. J., Krishnan, T.: The EM Algorithm and Extensions. Wiley, New York (1997).
Cover, T. MM, Thomas J.A.: Elements of Information Theory. John Wiley & Sons, Inc., New York (1991).
Kaus, M., Warfield S. K., Nabavi A., Black, P. M., Jolesz, F. A., Kikinis, R.: Automated segmentation of MRI of brain tumors. Radiology 218 (2001) 586–591.
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© 2002 Springer-Verlag Berlin Heidelberg
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Zou, K.H., Wells, W.M., Kaus, M.R., Kikinis, R., Jolesz, F.A., Warfield, S.K. (2002). Statistical Validation of Automated Probabilistic Segmentation against Composite Latent Expert Ground Truth in MR Imaging of Brain Tumors. In: Dohi, T., Kikinis, R. (eds) Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002. MICCAI 2002. Lecture Notes in Computer Science, vol 2488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45786-0_39
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DOI: https://doi.org/10.1007/3-540-45786-0_39
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