title: Agreement-based semi-supervised learning for skull stripping.
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).
authors.affiliation: Medical Imaging Informatics, University of California, Los Angeles, USA. jeiglesias@ucla.edu
authors.forename: Juan Eugenio
authors.initials: JE
authors.lastname: Iglesias
authors.forename: Cheng-Yi
authors.initials: CY
authors.lastname: Liu
authors.forename: Paul
authors.initials: P
authors.lastname: Thompson
authors.forename: Zhuowen
authors.initials: Z
authors.lastname: Tu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012886:Skull / Q000033:anatomy & histology*; D013382:Subtraction Technique