Abstract
In the field of computer aided medical image analysis, it is often difficult to obtain reliable ground truth for evaluating algorithms or supervising statistical learning procedures. In this paper we present a new method for training a classification forest from images labelled by variably performing experts, while simultaneously evaluating the performance of each expert. Our approach builds upon state-of-the-art randomized classification forest techniques for medical image segmentation and recent methods for the fusion of multiple expert decisions. By incorporating the performance evaluation within the training phase, we obtain a novel forest framework for learning from conflicting expert decisions, accounting for both inter- and intra-expert variability. We demonstrate on a synthetic example that our method allows to retrieve the correct segmentation among other incorrectly labelled images, and we present an application to the automatic segmentation of the midbrain in 3D transcranial ultrasound images.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation. IEEE Transactions on Medical Imaging 23, 903–921 (2004)
Commowick, O., Warfield, S.K.: Incorporating priors on expert performance parameters for segmentation validation and label fusion: A maximum a posteriori STAPLE. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 25–32. Springer, Heidelberg (2010)
Martin-Fernandez, M., Bouix, S., Ungar, L., McCarley, R.W., Shenton, M.E.: Two methods for validating brain tissue classifiers. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 515–522. Springer, Heidelberg (2005)
Commowick, O., Akhondi-Asl, A., Warfield, S.: Estimating a reference standard segmentation with spatially varying performance parameters: Local MAP STAPLE. IEEE Transactions on Medical Imaging 31(8), 1593–1606 (2012)
Raykar, V.C., Yu, S., Zhao, L.H., Valadez, G.H., Florin, C., Bogoni, L., Moy, L.: Learning from crowds. Journal of Machine Learning Research 11, 1297–1322 (2010)
Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)
Criminisi, A., Shotton, J., Bucciarelli, S.: Decision forests with long-range spatial context for organ localization in CT volumes. In: MICCAI Workshop on Probabilistic Models for Medical Image Analysis (2009)
Pauly, O., Ahmadi, S.-A., Plate, A., Boetzel, K., Navab, N.: Detection of substantia nigra echogenicities in 3D transcranial ultrasound for early diagnosis of parkinson disease. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 443–450. Springer, Heidelberg (2012)
Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Foundations and Trends in Computer Graphics and Vision 7(2-3), 81–227 (2012)
Pauly, O., Glocker, B., Criminisi, A., Mateus, D., Martinez-Moeller, A., Nekolla, S., Navab, N.: Fast multiple organs detection and localization in whole-body MR dixon sequences. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 239–247. Springer, Heidelberg (2011)
Geng, D.Y., Li, Y.X., Zee, C.S.: Magnetic resonance imaging-based volumetric analysis of basal ganglia nuclei and substantia nigra in patients with parkinson’s disease. Neurosurgery 58(2), 256–262 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chatelain, P. et al. (2013). Learning from Multiple Experts with Random Forests: Application to the Segmentation of the Midbrain in 3D Ultrasound. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40763-5_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-40763-5_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40762-8
Online ISBN: 978-3-642-40763-5
eBook Packages: Computer ScienceComputer Science (R0)