A Self-supervised Learning Based Framework for Automatic Heart Failure Classification on Cine Cardiac Magnetic Resonance Image

Heart failure (HF) is a serious syndrome, with high rates of mortality. Accurate classification of HF according to the left ventricular ejection faction (EF) plays an important role in the clinical treatment. Compared to echocardiography, cine cardiac magnetic resonance images (Cine-CMR) can estimate more accurate EF, whereas rare studies focus on the application of Cine-CMR. In this paper, a self-supervised learning framework for HF classification called SSLHF was proposed to automatically classify the HF patients into HF patients with preserved EF and HF patients with reduced EF based on Cine-CMR. In order to enable the classification network better learn the spatial and temporal information contained in the CineCMR, the SSLHF consists of two stages: self-supervised image restoration and HF classification. In the first stage, an image restoration proxy task was designed to help a U-Net like network mine the HF information in the spatial and temporal dimensions. In the second stage, a HF classification network whose weights were initialized by the encoder part of the U-Net like network was trained to complete the HF classification. Benefitting from the proxy task, the SSLHF achieved an AUC of 0.8505 and an ACC of 0.8208 in the 5-fold cross-validation.