In recent years, a variety of deep learning networks for cardiac MRI (CMR) segmentation have been developed and analyzed. However, nearly all of them are focused on cine CMR under breathold. In this work, accuracy of deep learning methods is assessed for volumetric analysis (via segmentation) of the left ventricle in real-time free-breathing CMR at rest and under exercise stress. Data from healthy volunteers (n=15) for cine and real-time free-breathing CMR were analyzed retrospectively. Segmentations of a commercial software (comDL) and a freely available neural network (nnU-Net), were compared to a reference created via the manual correction of comDL segmentation. Segmentation of left ventricular endocardium (LV), left ventricular myocardium (MYO), and right ventricle (RV) is evaluated for both end-systolic and end-diastolic phases and analyzed with Dice's coefficient (DC). The volumetric analysis includes LV end-diastolic volume (EDV), LV end-systolic volume (ESV), and LV ejection fraction (EF). For cine CMR, nnU-Net and comDL achieve a DC above 0.95 for LV and 0.9 for MYO, and RV. For real-time CMR, the accuracy of nnU-Net exceeds that of comDL overall. For real-time CMR at rest, nnU-Net achieves a DC of 0.94 for LV, 0.89 for MYO, and 0.90 for RV; mean absolute differences between nnU-Net and reference are 2.9mL for EDV, 3.5mL for ESV and 2.6% for EF. For real-time CMR under exercise stress, nnU-Net achieves a DC of 0.92 for LV, 0.85 for MYO, and 0.83 for RV; mean absolute differences between nnU-Net and reference are 11.4mL for EDV, 2.9mL for ESV and 3.6% for EF. Deep learning methods designed or trained for cine CMR segmentation can perform well on real-time CMR. For real-time free-breathing CMR at rest, the performance of deep learning methods is comparable to inter-observer variability in cine CMR and is usable or fully automatic segmentation.

翻译：近年来，针对心脏磁共振（CMR）分割的各种深度学习网络已得到开发和分析。然而，这些研究几乎全部聚焦于屏气状态下的电影CMR。本研究评估了深度学习方法在静息与运动负荷状态下，通过实时自由呼吸CMR对左心室进行容积分析（基于分割）的准确性。回顾性分析了健康志愿者（n=15）的电影CMR和实时自由呼吸CMR数据。将商业软件（comDL）和免费神经网络（nnU-Net）的分割结果与通过人工校正comDL分割创建的参考标准进行比较。针对收缩末期和舒张末期两个时相，评估了左心室内膜（LV）、左心室心肌（MYO）和右心室（RV）的分割效果，并采用Dice系数（DC）进行分析。容积分析包括左心室舒张末期容积（EDV）、左心室收缩末期容积（ESV）和左心室射血分数（EF）。对于电影CMR，nnU-Net和comDL在LV上的DC均高于0.95，在MYO和RV上均高于0.9。对于实时CMR，nnU-Net的整体精确度超过comDL。在静息实时CMR中，nnU-Net在LV、MYO和RV上的DC分别为0.94、0.89和0.90；nnU-Net与参考标准之间的平均绝对差值分别为：EDV 2.9mL、ESV 3.5mL、EF 2.6%。在运动负荷实时CMR中，nnU-Net在LV、MYO和RV上的DC分别为0.92、0.85和0.83；nnU-Net与参考标准之间的平均绝对差值分别为：EDV 11.4mL、ESV 2.9mL、EF 3.6%。专门为电影CMR分割设计或训练的深度学习方法，在实时CMR中同样表现良好。对于静息状态下的实时自由呼吸CMR，深度学习方法的性能与电影CMR中的观察者间变异性相当，且可用于或实现全自动分割。