Abstract
We propose a simple strategy to improve automatic medical image segmentation. The key idea is that without deep understanding of a segmentation method, we can still improve its performance by directly calibrating its results with respect to manual segmentation. We formulate the calibration process as a bias correction problem, which is addressed by machine learning using training data. We apply this methodology on three segmentation problems/methods and show significant improvements for all of them.
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Carmichael, O.T., Aizenstein, H.A., Davis, S.W., Becker, J.T., Thompson, P.M., Meltzer, C.C., Liu, Y.: Atlas-Based Hippocampus Segmentation In Alzheimers Disease and Mild Cognitive Impairment. NeuroImage 27(4), 979–990 (2005)
Greund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P.M.B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–27. Springer, Heidelberg (1995)
Morra, J., Tu, Z., Apostolova, L., Green, A., Toga, A., Thompson, P.: Automatic subcortical segmentation using a contextual model. In: Proceedings of the 11th international Conf. on Medical Image Computing and Compter-Aided Intervention, pp. 194–201 (2008)
Pluta, J., Avants, B., Glynn, S., Awate, S., Gee, J., Detre, J.: Appearance and Incomplete Label Matching for Diffeomorphic Template Based Hippocampus Segmentation. Hippocampus 19(6), 565–571 (2009)
Scahill, R.I., Schott, J.M., Stevens, J.M., Rossor, M.N., Fox, N.C.: Mapping the evolution of regional atrophy in Alzheimer’s disease: unbiased analysis of fluidregistered serial MRI. Proc. Natl. Acad. Sci. U.S.A 99(7), 4703–4707 (2002)
Squire, L.R.: Memory and the hippocampus: A synthesis from findings with rats, monkeys, and humans. Psychological Review 99, 195–231 (1992)
Smith, S.: Fast robust automated brain extraction. Human Brain Mapping 17(3), 143–155 (2002)
Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm. IEEE Trans. on Medical Imaging 20(1), 45–57 (2001)
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Wang, H. et al. (2010). Standing on the Shoulders of Giants: Improving Medical Image Segmentation via Bias Correction. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15711-0_14
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DOI: https://doi.org/10.1007/978-3-642-15711-0_14
Publisher Name: Springer, Berlin, Heidelberg
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