Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts
This work addresses artifact resilience in cardiac MRI segmentation, which is crucial for accurate structural and functional analysis, but it is incremental as it builds on existing methods with fine-tuning and augmentations.
The paper tackled the problem of cardiac MRI ventricle segmentation under respiratory motion artifacts by fine-tuning pretrained networks with data augmentations mimicking artifacts, resulting in significant improvements of up to 0.06 Dice score and 4mm Hausdorff distance.
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).