Keywords: Machine Learning/Artificial Intelligence, Data Analysis, Image RegistrationGlioma growth may be quantified with longitudinal image registration. However, the large mass-effects and tissue changes across images suposse an added challenge. Here, we propose a longitudinal, learning-based and groupwise registration method for the accurate and unbiased registration of glioma MRI. We evaluate on a dataset from the Glioma Longitudinal AnalySiS consortium and compare to classical registration methods. We achieve comparable Dice coeffients, with more detailed registrations, while significantly reducing the runtime to under a minute. The proposed methods may serve as an alternative to classical toolboxes, to provide further insight into glioma growth .
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