Adaptive Preprocessing for Generalization in Cardiac MR Image Segmentation

Recent advances in deep learning have shown the capability to accurately segment cardiac structures in magnetic resonance images. However, while these models provide a good segmentation performance for the specified datasets, their generalization with respect to unseen data across different MRI scanners, vendors or clinics is still under investigation. Previous work that aims to increase the generalization performance provides proof that emphasizing the model design on a uniform preprocessing step may be more beneficial than searching for a better neural architecture. In this paper we build upon this idea and show that a carefully designed preprocessing pipeline plays an important role in enabling the neural network to generalize to the large variety in MRI images. We evaluate our model in the context of the Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image (M&Ms) Segmentation Challenge.