Keywords: Segmentation, Segmentation
Object motion during the acquisition of magnetic resonance images can negatively impact image quality by introducing inconsistencies in the k-space data and in turn, blurring and ghosting artifacts on the acquired images. Such artifacts represent significant challenges in the clinical deployment of automated segmentation algorithms. In this work, we present an ensemble of approaches aimed at developing a robust and generalizable segmentation model, particularly tailored to handle the appearance of respiratory motion artifacts. We achieve this by introducing k-space based simulation for augmentation, as well as by reducing common errors across basal and apical slices with a region-focused segmentation approach.
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