Deep U-Net architecture with curriculum learning for myocardial pathology segmentation in multi-sequence cardiac magnetic resonance images

Myocardial pathology segmentation is essential for the diagnosis and treatment of patients suffering from myocardial infarction. In this work, we propose an end-to-end deep learning based segmentation method for automatically delineating the area of left ventricle (LV) myocardial infarct and edema regions. The proposed method uses the 6 layers deep U-Net architecture as the segmentation backbone, which adopts a hierarchical feature representation with symmetrical encoder–decoder paths. Skip connections are added between encoder and decoder paths, to concatenate low-level and high-level information for better feature representation. Moreover, three other modules, direction field module (DFM), channel self-attention module (CAM) and selective kernel module (SKM), have also been implemented for further exploration of performance improvement. The proposed method is tested on the public MyoPS 2020 (myocardial pathology segmentation combining multi-sequence cardiac magnetic resonance) challenge dataset. Compared with extra self-attention module or selective kernel module, plain deep U-Net with curriculum learning achieves better results on testing dataset. Extensive ablation experiments are performed to explore the optimal depth of U-Net, multiple loss functions and different data augmentation methods. Using the official evaluation kit, our solution outperforms state-of-the-art single stage approaches, and achieves comparable performance with other advanced multi-stage methods. The evaluation results demonstrate our method's effectiveness on myocardial pathology segmentation in multi-sequence cardiac magnetic resonance (CMR) data, and the superiority to the current state-of-the-art single stage methods.