• Bo Zhang, Han Liu, Hong Luo, and Kejun Li.
• Department of Ultrasound, West China Second Hospital, Sichuan University/ Key Laboratory of Obstetrics & Gynecology, Pediatric Diseases, and Birth Defects of the Ministry of Education.
• Medicine (Baltimore). 2021 Jan 29; 100 (4): e24427e24427.

AbstractThe quality control of fetal sonographic (FS) images is essential for the correct biometric measurements and fetal anomaly diagnosis. However, quality control requires professional sonographers to perform and is often labor-intensive. To solve this problem, we propose an automatic image quality assessment scheme based on multitask learning to assist in FS image quality control. An essential criterion for FS image quality control is that all the essential anatomical structures in the section should appear full and remarkable with a clear boundary. Therefore, our scheme aims to identify those essential anatomical structures to judge whether an FS image is the standard image, which is achieved by 3 convolutional neural networks. The Feature Extraction Network aims to extract deep level features of FS images. Based on the extracted features, the Class Prediction Network determines whether the structure meets the standard and Region Proposal Network identifies its position. The scheme has been applied to 3 types of fetal sections, which are the head, abdominal, and heart. The experimental results show that our method can make a quality assessment of an FS image within less a second. Also, our method achieves competitive performance in both the segmentation and diagnosis compared with state-of-the-art methods.Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.

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