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Dynamically balanced online random forests for interactive scribble-based segmentation

Wang, G; Zuluaga, MA; Pratt, R; Aertsen, M; Doel, T; Klusmann, M; David, AL; ... Ourselin, S; + view all (2016) Dynamically balanced online random forests for interactive scribble-based segmentation. In: Ourselin, S and Joskowicz, L and Sabuncu, M and Unal, G and Wells, W, (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. (pp. pp. 352-360). Springer International Publishing: Switzerland. Green open access

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Abstract

Interactive scribble-and-learning-based segmentation is attractive for its good performance and reduced number of user interaction. Scribbles for foreground and background are often imbalanced. With the arrival of new scribbles,the imbalance ratio may change largely. Failing to deal with imbalanced training data and a changing imbalance ratio may lead to a decreased sensitivity and accuracy for segmentation. We propose a generic Dynamically Balanced Online Random Forest (DyBa ORF) to deal with these problems,with a combination of a dynamically balanced online Bagging method and a tree growing and shrinking strategy to update the random forests. We validated DyBa ORF on UCI machine learning data sets and applied it to two different clinical applications: 2D segmentation of the placenta from fetal MRI and adult lungs from radiographic images. Experiments show it outperforms traditional ORF in dealing with imbalanced data with a changing imbalance ratio,while maintaining a comparable accuracy and a higher efficiency compared with its offline counterpart. Our results demonstrate that DyBa ORF is more suitable than existing ORF for learning-based interactive image segmentation.

Type: Proceedings paper
Title: Dynamically balanced online random forests for interactive scribble-based segmentation
ISBN-13: 9783319467221
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-46723-8_41
Publisher version: http://dx.doi.org/10.1007/978-3-319-46723-8_41
Language: English
Additional information: Copyright © Springer International Publishing AG 2016. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46723-8_41.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Maternal and Fetal Medicine
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/1501075
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