  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.
