Paper Info
Title
Invited:Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography
Abstract
Automatic myocardial segmentation of contrast echocardio-graphy 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. Onthe-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 hardwareaware 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.
Year
DOI
Venue
2021
10.1109/DAC18074.2021.9586158
2021 58TH ACM/IEEE DESIGN AUTOMATION CONFERENCE (DAC)
Keywords
DocType
ISSN
Neural Architecture Search, Image Segmentation, Quality Control, Contrast Echocardiography
Conference
0738-100X
Citations 
PageRank 
References 
0
0.34
0
Authors
8
Name
Order
Citations
PageRank
Dewen Zeng104.73
Yukun Ding204.73
Haiyun Yuan393.99
Meiping Huang4104.36
Xiaowei Xu513.07
Jian Zhuang610415.09
Jingtong Hu796376.16
Yiyu Shi855383.22