Title
A Generic Quality Control Framework for Fetal Ultrasound Cardiac Four-chamber Planes.
Abstract
Quality control/assessment of ultrasound (US) images is an essential step in clinical diagnosis. This process is usually done manually, suffering from some drawbacks, such as dependence on operator's experience and extensive labors, as well as high inter- and intra-observer variation. Automatic quality assessment of US images is therefore highly desirable. Fetal US cardiac four-chamber plane (CFP) is one of the most commonly used cardiac views, which was used in the diagnosis of heart anomalies in the early 1980s. In this paper, we propose a generic deep learning framework for automatic quality control of fetal US CFPs. The proposed framework consists of three networks: (1) a basic CNN (B-CNN), roughly classifying four-chamber views from the raw data; (2) a deeper CNN (D-CNN), determining the gain and zoom of the target images in a multi-task learning manner; and (3) the aggregated residual visual block net (ARVBNet), detecting the key anatomical structures on a plane. Based on the output of the three networks, overall quantitative score of each CFP is obtained, so as to achieve fully automatic quality control. Experiments on a fetal US dataset demonstrated our proposed method achieved a highest mean average precision (mAP) of 93.52% at a fast speed of 101 frames per second (FPS). In order to demonstrate the adaptability and generalization capacity, the proposed detection network (i.e., ARVBNet) has also been validated on the PASCAL VOC dataset, obtaining a highest mAP of 81.2% when input size is approximately 300 × 300.
Year
DOI
Venue
2020
10.1109/JBHI.2019.2948316
IEEE journal of biomedical and health informatics
Keywords
DocType
Volume
Quality control,Anatomical structure,Quality assessment,Ultrasonic imaging,Real-time systems
Journal
24
Issue
ISSN
Citations 
4
2168-2194
2
PageRank 
References 
Authors
0.36
0
7
Name
Order
Citations
PageRank
Jinbao Dong120.36
Shengfeng Liu220.70
Yimei Liao320.36
Huaxuan Wen420.36
Bai Ying Lei511924.99
Shengli Li618418.06
Tianfu Wang738255.46