Learning from Multiple Experts with Random Forests: Application to the Segmentation of the Midbrain in 3D Ultrasound

Pierre Chatelain^{1, 2}, Olivier Pauly^{1, 3}, Loïc Peter¹, Seyed-Ahmad Ahmadi¹, Annika Plate⁴, Kai Bötzel⁴, and Nassir Navab¹

¹Computer Aided Medical Procedures, Technische Universität München, Germany
pierre.chatelain@cs.tum.edu

²Ecole Normale Supérieure de Cachan, Antenne de Bretagne, France

³Institute of Biomathematics and Biometry, Helmholtz Zentrum München, Germany

⁴Department of Neurology, Ludwig-Maximilians-Universität München, Germany

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.

LNCS 8150, p. 230 ff.

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