A novel approach for left ventricle segmentation in tagged MRI

Automatic left ventricle (LV) segmentation from tagged cardiac magnetic resonance imaging is significant for evaluating heart function and providing follow-up treatments in clinical medicine. However, due to the complicated cardiac structure and extra interference, it is challenging for traditional methods to delineate the LV automatically and get accurate results. Therefore, we proposed the automatic LV segmentation algorithm combined with deep learning and curriculum learning strategy. The key technologies are described as follows: firstly, local sine-wave modeling (SinMod) is practiced to track cardiac motion information, implement automatic heart location and obtain the region of interest. Secondly, U-Net is utilized as the basic model to segment the LV endocardium and epicardium. Additionally, a new curriculum learning training strategy is adopted to improve segmentation accuracy. Finally, comparative results demonstrate the superior performance of our approach to those resulting from traditional methods.

Automated summary

An automatic left ventricle segmentation algorithm is proposed using deep learning and curriculum learning strategy for tagged cardiac magnetic resonance imaging. The algorithm utilizes local sine-wave modeling (SinMod) to track cardiac motion information, utilizes U-Net to segment LV endocardium and epicardium, and adopts a curriculum learning training strategy to improve segmentation accuracy. Superior performance to traditional methods is demonstrated through comparative results.