Improving myocardial pathology segmentation with U-Net plus plus and EfficientSeg from multi-sequence cardiac magnetic resonance images

Background: Myocardial pathology segmentation plays an utmost role in the diagnosis and treatment of myocardial infarction (MI). However, manual segmentation is time-consuming and labor-intensive, and requires a lot of professional knowledge and clinical experience.Methods: In this work, we develop an automatic and accurate coarse-to-fine myocardial pathology segmenta-tion framework based on the U-Net++ and EfficientSeg model. The U-Net++ network with deep supervision is first applied to delineate the cardiac structures from the multi-sequence cardiac magnetic resonance (CMR) images to generate a coarse segmentation map. Then the coarse segmentation map together with the three-sequence CMR data is sent to the EfficientSeg-B1 for fine segmentation, that is, further segmentation of myocardial scar and edema areas. In addition, we apply the Focal loss to replace the original cross-entropy term, for the purpose of encouraging the model to pay more attention to the pathological areas.Results: The proposed segmentation approach is tested on the public Myocardial Pathology Segmentation Challenge (MyoPS 2020) dataset. Experimental results demonstrate that our solution achieves an average Dice score of 0.7148 +/- 0.2213 for scar, an average Dice score of 0.7439 +/- 0.1011 for edema + scar, and the final average score of 0.7294 on the overall 20 testing sets, all of which have outperformed the first place method in the competition. Moreover, extensive ablation experiments are performed, which shows that the two-stage strategy with Focal loss greatly improves the segmentation quality of pathological areas.Conclusion: Given its effectiveness and superiority, our method can further facilitate myocardial pathology segmentation in medical practice.

Automated summary

This paper presents an automatic and accurate myocardial pathology segmentation framework based on the U-Net++ and EfficientSeg models. By utilizing a two-stage segmentation strategy and the Focal loss method, the proposed method achieves excellent performance in the Myocardial Pathology Segmentation Challenge. It can further facilitate myocardial pathology segmentation in medical practice.