Agreement-based semi-supervised learning for skull stripping

Authors:
Juan Eugenio Iglesias;Cheng-Yi Liu;Paul Thompson;Zhuowen Tu
Affiliations:
Medical Imaging Informatics, University of California, Los Angeles;Laboratory of Neuroimaging, University of California, Los Angeles;Laboratory of Neuroimaging, University of California, Los Angeles;Laboratory of Neuroimaging, University of California, Los Angeles
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Year:
2010

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Abstract

Learning-based approaches have become increasingly practical in medical imaging. For a supervised learning strategy, the quality of the trained algorithm (usually a classifier) is heavily dependent on the amount, as well as quality, of the available training data. It is often very time-consuming to obtain the ground truth manual delineations. In this paper, we propose a semi-supervised learning algorithm and show its application to skull stripping in brain MRI. The resulting method takes advantage of existing state-of-the-art systems, such as BET and FreeSurfer, to sample unlabeled data in an agreement-based framework. Using just two labeled and a set of unlabeled MRI scans, a voxel-based random forest classifier is trained to perform the skull stripping. Our system is practical, and it displays significant improvement over supervised approaches, BET and FreeSurfer in two datasets (60 test images).