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Cardiac Magnetic Resonance Imaging is commonly used for the assessment of thecardiac anatomy and function. The delineations of left and right ventricleblood pools and left ventricular myocardium are important for the diagnosis ofcardiac diseases. Unfortunately, the movement of a patient during the CMRacquisition procedure may result in motion artifacts appearing in the finalimage. Such artifacts decrease the diagnostic quality of CMR images and forceredoing of the procedure. In this paper, we present a Multi-task Swin UNEtTRansformer network for simultaneous solving of two tasks in the CMRxMotionchallenge: CMR segmentation and motion artifacts classification. We utilizeboth segmentation and classification as a multi-task learning approach whichallows us to determine the diagnostic quality of CMR and generate masks at thesame time. CMR images are classified into three diagnostic quality classes,whereas, all samples with non-severe motion artifacts are being segmented.Ensemble of five networks trained using 5-Fold Cross-validation achievessegmentation performance of DICE coefficient of 0.871 and classificationaccuracy of 0.595.

Multi-task Swin Transformer for Motion Artifacts Classification and Cardiac Magnetic Resonance Image Segmentation