  Most existing deep learning-based frameworks for image segmentation assume
that a unique ground truth is known and can be used for performance evaluation.
This is true for many applications, but not all. Myocardial segmentation of
Myocardial Contrast Echocardiography (MCE), a critical task in automatic
myocardial perfusion analysis, is an example. Due to the low resolution and
serious artifacts in MCE data, annotations from different cardiologists can
vary significantly, and it is hard to tell which one is the best. In this case,
how can we find a good way to evaluate segmentation performance and how do we
train the neural network? In this paper, we address the first problem by
proposing a new extended Dice to effectively evaluate the segmentation
performance when multiple accepted ground truth is available. Then based on our
proposed metric, we solve the second problem by further incorporating the new
metric into a loss function that enables neural networks to flexibly learn
general features of myocardium. Experiment results on our clinical MCE data set
demonstrate that the neural network trained with the proposed loss function
outperforms those existing ones that try to obtain a unique ground truth from
multiple annotations, both quantitatively and qualitatively. Finally, our
grading study shows that using extended Dice as an evaluation metric can better
identify segmentation results that need manual correction compared with using
Dice.
