Department of Ultrasound, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China.
School of Medicine, Huaqiao University, Quanzhou, Fujian, China.
Int J Gynaecol Obstet. 2024 Jun;165(3):916-928. doi: 10.1002/ijgo.15145. Epub 2023 Oct 8.
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.
目前，产前超声是筛查胎儿畸形的重要手段之一。在产前超声诊断过程中，准确识别胎儿面部超声标准切面对于面部畸形检测和疾病筛查至关重要。由于胎儿面部图像分布密集，无明显结构轮廓边界，结构检测框中间的结构区域小，面积重叠大，本文首次将胎儿面部标准切面及其结构识别视为通用目标检测任务，并将实时 YOLO v5s 应用于胎儿面部超声标准切面结构检测和分类任务中。首先，我们检测单个切片的结构，并以切片类的结构作为识别对象。其次，我们对三个标准平面进行结构检测实验；然后，在前期的基础上，收集了包括在多个胎儿超声检查中的所有部分的图像。在单类结构检测实验和三种标准平面的结构检测和分类实验中，Precision 和 Recall 指标数据的整体识别效果较好，分别为 98.3%和 98.1%，以及 99.3%和 98.2%。实验结果表明，该模型具有在不同数据中识别胎儿面部解剖结构和标准切面的能力，有助于医生自动快速筛选出每个胎儿面部超声的标准切面。
