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Learning from multiple experts with random forests: Application to the segmentation of the midbrain in 3D ultrasound.
Lect. Notes Comput. Sc. 8150, 230-237 (2013)
DOI Verlagsversion bestellen
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
ISSN (print) / ISBN 0302-9743
Konferenztitel 16th International Conference on Medical Image Computing and Computer Assisted Intervention, 22 - 26 September 2013, Nagoya, Japan
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 8150, Seiten: 230-237
Verlagsort Berlin [u.a.]
Institut(e) Institute of Computational Biology (ICB)