Cardiac Magnetic Resonance Imaging is commonly used for the assessment of the
cardiac anatomy and function. The delineations of left and right ventricle
blood pools and left ventricular myocardium are important for the diagnosis of
cardiac diseases. Unfortunately, the movement of a patient during the CMR
acquisition procedure may result in motion artifacts appearing in the final
image. Such artifacts decrease the diagnostic quality of CMR images and force
redoing of the procedure. In this paper, we present a Multi-task Swin UNEt
TRansformer network for simultaneous solving of two tasks in the CMRxMotion
challenge: CMR segmentation and motion artifacts classification. We utilize
both segmentation and classification as a multi-task learning approach which
allows us to determine the diagnostic quality of CMR and generate masks at the
same 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 achieves
segmentation performance of DICE coefficient of 0.871 and classification
accuracy of 0.595.

本研究提出了一种多任务的网络 ——Multi-task Swin UNEt TRansformer 网络，用于解决在 CMR 运动挑战中的 CMR 分割和运动伪影分类，实现了同时确定 CMR 的诊断质量和生成掩模的目的。通过 5 倍交叉验证训练 5 个网络的集合，我们实现了 DICE 系数为 0.871 的分割性能和 0.595 的分类准确性。