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.

来自标记心脏磁共振成像的自动左心室 (LV) 分割对于评估心脏功能和提供临床医学的后续治疗具有重要意义。然而，由于复杂的心脏结构和额外的干扰，传统方法很难自动勾画左室并获得准确的结果。因此，我们提出了结合深度学习和课程学习策略的自动LV分割算法。关键技术描述如下：首先，通过局部正弦波建模（SinMod）来跟踪心脏运动信息，实现心脏自动定位并获得感兴趣区域。其次，利用U-Net作为分割LV心内膜和心外膜的基本模型。此外，采用新的课程学习训练策略来提高切分准确率。最后，比较结果证明了我们的方法比传统方法产生的方法的优越性能。