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.

深度学习网络在实时无呼吸心磁共振成像中对心脏进行体积分析的准确性较高，并且 nnU-Net 方法的表现超过了其他方法，可用于可靠的自动分割。