Recent advances in deep learning based image segmentation methods have
enabled real-time performance with human-level accuracy. However, occasionally
even the best method fails due to low image quality, artifacts or unexpected
behaviour of black box algorithms. Being able to predict segmentation quality
in the absence of ground truth is of paramount importance in clinical practice,
but also in large-scale studies to avoid the inclusion of invalid data in
subsequent analysis.
In this work, we propose two approaches of real-time automated quality
control for cardiovascular MR segmentations using deep learning. First, we
train a neural network on 12,880 samples to predict Dice Similarity
Coefficients (DSC) on a per-case basis. We report a mean average error (MAE) of
0.03 on 1,610 test samples and 97% binary classification accuracy for
separating low and high quality segmentations. Secondly, in the scenario where
no manually annotated data is available, we train a network to predict DSC
scores from estimated quality obtained via a reverse testing strategy. We
report an MAE=0.14 and 91% binary classification accuracy for this case.
Predictions are obtained in real-time which, when combined with real-time
segmentation methods, enables instant feedback on whether an acquired scan is
analysable while the patient is still in the scanner. This further enables new
applications of optimising image acquisition towards best possible analysis
results.

通过深度学习模型建立 DSC （Dice Similarity Coefficients） 预测模型，用于评估心血管 MR 图像的分割质量；同时提出一种无标记数据的反向测试策略，预测 DSC 并进行二元分类判别心血管 MR 图像分割的质量，实现实时分割方法的筛选为可分析图像，最大限度提高图像分析结果的优化。

分割质量实时预测

Real-time Prediction of Segmentation Quality

We propose an automatic method using dilated convolutional neural networks
(CNNs) for segmentation of the myocardium and blood pool in cardiovascular MR
(CMR) of patients with congenital heart disease (CHD).
Ten training and ten test CMR scans cropped to an ROI around the heart were
provided in the MICCAI 2016 HVSMR challenge. A dilated CNN with a receptive
field of 131x131 voxels was trained for myocardium and blood pool segmentation
in axial, sagittal and coronal image slices. Performance was evaluated within
the HVSMR challenge.
Automatic segmentation of the test scans resulted in Dice indices of
0.80$\pm$0.06 and 0.93$\pm$0.02, average distances to boundaries of
0.96$\pm$0.31 and 0.89$\pm$0.24 mm, and Hausdorff distances of 6.13$\pm$3.76
and 7.07$\pm$3.01 mm for the myocardium and blood pool, respectively.
Segmentation took 41.5$\pm$14.7 s per scan.
In conclusion, dilated CNNs trained on a small set of CMR images of CHD
patients showing large anatomical variability provide accurate myocardium and
blood pool segmentations.

本文介绍了一种自动分割方法，使用扩张型卷积神经网络 (CNN) 对患有先天性心脏病 (CHD) 的患者的心血管磁共振 (CMR) 中的心肌和血池进行分割，并对方法进行了有效性评估。该方法在对仅含有候选机关的图像进行处理的过程中表现良好并适应解剖变异。