Detection of fetal facial anatomy in standard ultrasonographic sections based on real‐time target detection network

• Published in: International journal of gynecology and obstetrics Vol. 165; no. 3; pp. 916 - 928
• Main Authors: Liu, Zhonghua, Yu, Weifeng, Wu, Xiuming, Yang, Tong, Lyu, Guorong, Liu, Peizhong, Xue, Hao
• Format: Journal Article
• Language: English
• Published: United States 01.06.2024
• Subjects: deep learning; Face - anatomy & histology; Face - diagnostic imaging; Face - embryology; Female; fetal face ultrasound standard plane; Fetus - anatomy & histology; Fetus - diagnostic imaging; Humans; Pregnancy; real‐time detection; single‐class structure detection; standard plane structure inspection; Ultrasonography, Prenatal - methods; real‐time detection; deep learning; single‐class structure detection; standard plane structure inspection; fetal face ultrasound standard plane
• ISSN: 0020-7292; 1879-3479; 1879-3479
• DOI: 10.1002/ijgo.15145

At present, prenatal ultrasound is one of the important means for screening fetal malformations. In the process of prenatal ultrasound diagnosis, the accurate recognition of fetal facial ultrasound standard plane is crucial for facial malformation detection and disease screening. Due to the dense distribution of fetal facial images, no obvious structure contour boundary, small structure area, and large area overlap in the middle of the structure detection frame, this paper regards the fetal facial standard plane and its structure recognition as a universal target detection task for the first time, and applies real‐time YOLO v5s to the fetal facial ultrasound standard plane structure detection and classification task. First, we detect the structure of a single slice, and take the structure of a slice class as the recognition object. Second, we carry out structural detection experiments on three standard planes; then, on the basis of the previous stage, the images of all parts included in the ultrasound examination of multiple fetuses were collected. In the single‐class structure detection experiment and the structure detection and classification experiment of three types of standard planes, the overall recognition effect of Precision and Recall index data is better, with Precision being 98.3% and 98.1%, and Recall being 99.3% and 98.2%, respectively. The experimental results show that the model has the ability to identify fetal facial anatomy and standard sections in different data, which can help the physician to automatically and quickly screen out the standard sections of each fetal facial ultrasound. Synopsis By automatically identifying key anatomical structures within ultrasound images, it provides a theoretical basis for doctors to choose standard planes.