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 at rest and under exercise stress 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，多种用于心脏磁共振成像（CMR）分割的深度学习网络已被开发和分析。然而，几乎所有研究都聚焦于屏气下的电影CMR。本研究评估了深度学习方法在静息和运动负荷下实时自由呼吸CMR中对左心室进行容积分析（通过分割）的准确性。回顾性分析了15名健康志愿者在静息和运动负荷下的电影CMR及实时自由呼吸CMR数据。将商业软件（comDL）和开源神经网络（nnU-Net）的分割结果与基于comDL分割手动校正创建的参考进行对比。评估了左心室内膜（LV）、左心室心肌（MYO）和右心室（RV）在收缩末期和舒张末期的分割效果，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）、收缩末期容积（ESV）和射血分数（EF）。对于电影CMR，nnU-Net和comDL在LV上的DC均超过0.95，在MYO和RV上超过0.9。对于实时CMR，nnU-Net的准确性整体优于comDL。在静息状态下的实时CMR中，nnU-Net的LV、MYO和RV的DC分别为0.94、0.89和0.90；nnU-Net与参考之间的平均绝对差异为EDV 2.9mL、ESV 3.5mL、EF 2.6%。在运动负荷下的实时CMR中，nnU-Net的LV、MYO和RV的DC分别为0.92、0.85和0.83；nnU-Net与参考之间的平均绝对差异为EDV 11.4mL、ESV 2.9mL、EF 3.6%。专为电影CMR分割设计或训练的深度学习方法在实时CMR上也能表现良好。对于静息状态下实时自由呼吸CMR，深度学习方法的性能与电影CMR的观察者间变异性相当，可实现全自动或可用分割。