Keywords: Machine Learning/Artificial Intelligence, Data Processing, Image Registration
In this research we investigated the performance of published neural networks for an affine registration of multimodal medical images and examined the networks' generalizability to new datasets. The neural networks were trained and evaluated using a synthetic multimodal dataset of three-dimensional CT and MRI volumes of the liver. We compared the Normalised Mutual Information, Dice coefficient and the Hausdorff distance across the neural networks described in the papers, using our CNN as a benchmark and the conventional affine registration method as a baseline. Seven networks improved the pre-registration Dice coefficient and are therefore able to generalise to new datasets.
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