title: Learning from multiple experts with random forests: application to the segmentation of the midbrain in 3D ultrasound.
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.
authors.affiliation: Computer Aided Medical Procedures, Technische Universität München, Germany. pierre.chatelain@cs.tum.edu
authors.forename: Pierre
authors.initials: P
authors.lastname: Chatelain
authors.affiliation: Computer Aided Medical Procedures, Technische Universität München, Germany.
authors.forename: Olivier
authors.initials: O
authors.lastname: Pauly
authors.affiliation: Computer Aided Medical Procedures, Technische Universität München, Germany.
authors.forename: Loïc
authors.initials: L
authors.lastname: Peter
authors.affiliation: Computer Aided Medical Procedures, Technische Universität München, Germany.
authors.forename: Seyed-Ahmad
authors.initials: SA
authors.lastname: Ahmadi
authors.affiliation: Department of Neurology, Ludwig-Maximilians-Universität München, Germany.
authors.forename: Annika
authors.initials: A
authors.lastname: Plate
authors.affiliation: Department of Neurology, Ludwig-Maximilians-Universität München, Germany.
authors.forename: Kai
authors.initials: K
authors.lastname: Bötzel
authors.affiliation: Computer Aided Medical Procedures, Technische Universität München, Germany.
authors.forename: Nassir
authors.initials: N
authors.lastname: Navab
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D004453:Echoencephalography / Q000379:methods*; D005103:Expert Systems; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008636:Mesencephalon / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity