  Methods that are resilient to artifacts in the cardiac magnetic resonance
imaging (MRI) while performing ventricle segmentation, are crucial for ensuring
quality in structural and functional analysis of those tissues. While there has
been significant efforts on improving the quality of the algorithms, few works
have tackled the harm that the artifacts generate in the predictions. In this
work, we study fine tuning of pretrained networks to improve the resilience of
previous methods to these artifacts. In our proposed method, we adopted the
extensive usage of data augmentations that mimic those artifacts. The results
significantly improved the baseline segmentations (up to 0.06 Dice score, and
4mm Hausdorff distance improvement).
