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，各类用于心脏磁共振（CMR）分割的深度学习网络已被开发和分析。然而，几乎所有研究均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及运动负荷状态下实时自由呼吸CMR中左心室容积分析（通过分割）的准确性。回顾性分析了15名健康志愿者的电影CMR与实时自由呼吸CMR数据。将商业软件（comDL）和开源神经网络（nnU-Net）的分割结果与通过手动校正comDL分割生成的参考数据进行比较。评估了左心室内膜（LV）、左心室心肌（MYO）和右心室（RV）在收缩末期与舒张末期阶段的分割，并利用Dice系数（DC）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）及射血分数（EF）。对于电影CMR，nnU-Net与comDL的LV区域DC均超过0.95，MYO和RV区域超过0.9。对于实时CMR，nnU-Net的整体精度优于comDL。静息状态实时CMR中，nnU-Net的LV、MYO和RV的DC分别为0.94、0.89和0.90；nnU-Net与参考值的平均绝对差值为：EDV 2.9mL、ESV 3.5mL、EF 2.6%。运动负荷状态实时CMR中，nnU-Net的LV、MYO和RV的DC分别为0.92、0.85和0.83；平均绝对差值为：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可在实时CMR中表现良好。在静息状态实时自由呼吸CMR中，深度学习方法性能与电影CMR的观察者间差异相当，且可用于全自动分割。