Quality assessment for fetal four-chamber ultrasound views based on two-stage segmentation-Reference-Cited by-同舟云学术

Quality assessment for fetal four-chamber ultrasound views based on two-stage segmentation

Published:2023 Issue:8 Volume:28 Page:2476-2490
ISSN:1006-8961
Container-title:Journal of Image and Graphics
language:en
Short-container-title:

Author:

Guangzhu Xu, ,Yifan Qian,Yang Wang,Rong Liu,Jun Zhou,Bangjun Lei

Publisher

Aerospace Information Research Institute, Chinese Academy of Sciences

Subject

Artificial Intelligence,Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Human-Computer Interaction

Reference31 articles.

1. Abdi A H，Luong C，Tsang T，Allan G，Nouranian S，Jue J，Hawley D，Fleming S，Gin K，Swift J，Rohling R and Abolmaesumi P. 2017. Automatic quality assessment of echocardiograms using convolutional neural networks:feasibility on the apical four-chamber view. IEEE Transactions on Medical Imaging，36（6）:1221-1230［DOI:10.1109/TMI.2017.2690836］

2. Baumgartner C F，Kamnitsas K，Matthew J，Fletcher T P，Smith S，Koch L M，Kainz B and Rueckert D. 2017. Sononet:real-time detection and localisation of fetal standard scan planes in freehand ultrasound. IEEE Transactions on Medical Imaging，36（11）:2204-2215［DOI:10.1109/TMI.2017.2712367］

3. Bochkovskiy A，Wang C Y and Liao H Y M. 2020. YOLOv4:optimal speed and accuracy of object detection［EB/OL］.［2022-03-28］.https://arxiv.org/pdf/2004.10934.pdf

4. Chen K，Wang J Q，Pang J M，Cao Y H，Xiong Y，Li X X，Sun S Y，Feng W S，Liu Z W，Xu J R，Zhang Z，Cheng D Z，Zhu C C，Cheng T H，Zhao Q J，Li B Y，Lu X，Zhu R，Wu Y，Dai J F，Wang J D，Shi J P，Ouyang W L，Loy C C and Lin D H. 2019.MMDetection:open MMLab detection toolbox and benchmark［EB/OL］.［2022-03-28］. https://arxiv.org/pdf/1906.07155.pdf

5. Chen Q，Wang Y M，Yang T，Zhang X Y，Cheng J and Sun J. 2021.You only look one-level feature//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition（CVPR）.Nashville，USA:IEEE.［DOI:10.1109/CVPR46437.2021.01284］