Effective extraction of ventricles and myocardium objects from cardiac magnetic resonance images with a multi-task learning U-Net

•A U-Net based Multi-task deep learning framework (MTL-UNet) was proposed.•An edge detection module and fusion module are combined for improved segmentation.•Combined loss functions for accurate extraction of various semantic objects in MRI.•Improved results on extraction of ventricles and myocardium in the ACDC’17 dataset. Accurate extraction of semantic objects such as ventricles and myocardium from magnetic resonance (MR) images is one essential but very challenging task for the diagnosis of the cardiac diseases. To tackle this problem, in this paper, an automatic end-to-end supervised deep learning framework is proposed, using a multi-task learning based U-Net (MTL-UNet). Specifically, an edge extraction module and a fusion-based module are introduced for effectively capturing the contextual information such as continuous edges and consistent spatial patterns in terms of intensity and texture features. With a weighted triple loss including the dice loss, the cross-entropy loss and the edge loss, the accuracy of object segmentation and extraction has been effectively improved. Extensive experiments on the publicly available ACDC 2017 dataset have validated the efficacy and efficiency of the proposed MTL-UNet model. [Display omitted]