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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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)
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
e-ISSN
1611-3349
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
Verlag
Springer
Verlagsort
Berlin [u.a.]
Nichtpatentliteratur
Publikationen
Institut(e)
Institute of Computational Biology (ICB)