Abstract
The existence of large medical image databases have made large amounts of neuroimaging data accessible and freely available to the research community. In this paper, we harness both the vast quantity of unlabeled anatomical MR brain scans from the 1000 Functional Connectomes Project (FCP1000) database and the smaller, but richly-annotated brain images from the LONI Probabilistic Brain Atlas (LPBA40) database to learn a statistical deformation model (SDM) of the nonrigid transformations in a semi-supervised learning (SSL) framework. We make use of 39 LPBA40 labeled MR datasets to create a set of supervised registrations and augment these results with a set of unsupervised registrations using 1247 unlabeled MRIs from the FCP1000. We show through leave-one-out cross validation that SSL of a nonrigid SDM results in a registration algorithm with significantly improved accuracy compared to standard, intensity-based registration, and does so with a 99 % reduction in transformation dimensionality.
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Onofrey, J.A., Staib, L.H., Papademetris, X. (2014). Semi-supervised Learning of Nonrigid Deformations for Image Registration. In: Menze, B., Langs, G., Montillo, A., Kelm, M., Müller, H., Tu, Z. (eds) Medical Computer Vision. Large Data in Medical Imaging. MCV 2013. Lecture Notes in Computer Science(), vol 8331. Springer, Cham. https://doi.org/10.1007/978-3-319-05530-5_2
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DOI: https://doi.org/10.1007/978-3-319-05530-5_2
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