Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography
This addresses the need for efficient, real-time quality control in clinical echocardiography to reduce manual effort, though it is incremental as it adapts existing methods to hardware constraints.
The paper tackled the problem of enabling real-time segmentation quality control for myocardial contrast echocardiography on resource-constrained ultrasound hardware by proposing a hardware-aware neural architecture search framework that incorporates latency as a regularization term, achieving feasibility for on-the-fly deployment.
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.