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

TL;DR

This paper introduces a multi-task Swin Transformer model that simultaneously classifies motion artifacts and segments cardiac MRI images, improving diagnostic assessment and image quality evaluation.

Contribution

The paper presents a novel multi-task Swin Transformer approach for concurrent CMR segmentation and motion artifacts classification, enhancing efficiency and diagnostic accuracy.

Findings

Segmentation DICE coefficient of 0.871

Classification accuracy of 0.595

Effective multi-task learning framework

Abstract

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.