Learning from multiple experts with random forests: Application to the segmentation of the midbrain in 3D ultrasound
Chatelain P, Pauly O, Peter L, Ahmadi SA, Plate A, Bötzel K, Navab N (2013)
Publication Type: Conference contribution
Publication year: 2013
Journal
Book Volume: 8150 LNCS
Pages Range: 230-237
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: JPN
ISBN: 9783642407628
DOI: 10.1007/978-3-642-40763-5_29
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. © 2013 Springer-Verlag.
Involved external institutions
Germany (DE)How to cite
APA:
Chatelain, P., Pauly, O., Peter, L., Ahmadi, S.A., Plate, A., Bötzel, K., & Navab, N. (2013). Learning from multiple experts with random forests: Application to the segmentation of the midbrain in 3D ultrasound. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 230-237). JPN.
MLA:
Chatelain, Pierre, et al. "Learning from multiple experts with random forests: Application to the segmentation of the midbrain in 3D ultrasound." Proceedings of the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, JPN 2013. 230-237.
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