  Automatic myocardial segmentation of contrast echocardiography has shown
great potential in the quantification of myocardial perfusion parameters.
Segmentation quality control is an important step to ensure the accuracy of
segmentation results for quality research as well as its clinical application.
Usually, the segmentation quality control happens after the data acquisition.
At the data acquisition time, the operator could not know the quality of the
segmentation results. On-the-fly segmentation quality control could help the
operator to adjust the ultrasound probe or retake data if the quality is
unsatisfied, which can greatly reduce the effort of time-consuming manual
correction. However, it is infeasible to deploy state-of-the-art DNN-based
models because the segmentation module and quality control module must fit in
the limited hardware resource on the ultrasound machine while satisfying strict
latency constraints. In this paper, we propose a hardware-aware neural
architecture search framework for automatic myocardial segmentation and quality
control of contrast echocardiography. We explicitly incorporate the hardware
latency as a regularization term into the loss function during training. The
proposed method searches the best neural network architecture for the
segmentation module and quality prediction module with strict latency.
