@article{discovery10036377, note = {This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/}, pages = {117--134}, journal = {Neuroimage}, title = {Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction}, year = {2017}, volume = {166}, month = {October}, author = {Blaiotta, C and Freund, P and Cardoso, MJ and Ashburner, J}, url = {https://doi.org/10.1016/j.neuroimage.2017.10.060}, abstract = {In this paper we present a hierarchical generative model of medical image data, which can capture simultaneously the variability of both signal intensity and anatomical shapes across large populations. Such a model has a direct application for learning average-shaped probabilistic tissue templates in a fully automated manner. While in principle the generality of the proposed Bayesian approach makes it suitable to address a wide range of medical image computing problems, our work focuses primarily on neuroimaging applications. In particular we validate the proposed method on both real and synthetic brain MR scans including the cervical cord and demonstrate that it yields accurate alignment of brain and spinal cord structures, as compared to state-of-the-art tools for medical image registration. At the same time we illustrate how the resulting tissue probability maps can readily be used to segment, bias correct and spatially normalise unseen data, which are all crucial pre-processing steps for MR imaging studies.}, issn = {1095-9572}, keywords = {Atlas construction, Generative modelling, Image registration, Image segmentation, MRI} }