College of Engineering, Huaqiao University, Quanzhou, China.
Department of Ultrasound, The First Hospital of Quanzhou Affiliated to Fujian Medical University, Quanzhou, China.
J Ultrasound Med. 2023 Aug;42(8):1859-1880. doi: 10.1002/jum.16209. Epub 2023 Mar 9.
Ultrasound screening during early pregnancy is vital in preventing congenital disabilities. For example, nuchal translucency (NT) thickening is associated with fetal chromosomal abnormalities, particularly trisomy 21 and fetal heart malformations. Obtaining accurate ultrasound standard planes of a fetal face during early pregnancy is the key to subsequent biometry and disease diagnosis. Therefore, we propose a lightweight target detection network for early pregnancy fetal facial ultrasound standard plane recognition and quality assessment.
First, a clinical control protocol was developed by ultrasound experts. Second, we constructed a YOLOv4 target detection algorithm based on the backbone network as GhostNet and added attention mechanisms CBAM and CA to the backbone and neck structure. Finally, key anatomical structures in the image were automatically scored according to a clinical control protocol to determine whether they were standard planes.
We reviewed other detection techniques and found that the proposed method performed well. The average recognition accuracy for six structures was 94.16%, the detection speed was 51 FPS, and the model size was 43.2 MB, and a reduction of 83% compared with the original YOLOv4 model was obtained. The precision for the standard median sagittal plane was 97.20%, and the accuracy for the standard retro-nasal triangle view was 99.07%.
The proposed method can better identify standard or non-standard planes from ultrasound image data, providing a theoretical basis for automatic acquisition of standard planes in the prenatal diagnosis of early pregnancy fetuses.
早孕期超声筛查对于预防先天残疾至关重要。例如，颈项透明层（NT）增厚与胎儿染色体异常有关，特别是 21 三体和胎儿心脏畸形。在早孕期获得胎儿面部的准确超声标准切面是后续进行生物测量和疾病诊断的关键。因此，我们提出了一种用于早孕期胎儿面部超声标准切面识别和质量评估的轻量级目标检测网络。
首先，超声专家制定了临床控制方案。其次，我们基于 GhostNet 构建了一个基于骨干网络的 YOLOv4 目标检测算法，并在骨干和颈部结构中添加了注意力机制 CBAM 和 CA。最后，根据临床控制方案自动对图像中的关键解剖结构进行评分，以确定它们是否为标准切面。
我们回顾了其他检测技术，发现所提出的方法表现良好。六个结构的平均识别准确率为 94.16%，检测速度为 51 FPS，模型大小为 43.2MB，与原始 YOLOv4 模型相比减少了 83%。标准正中矢状切面的精度为 97.20%，标准鼻后三角切面的准确率为 99.07%。
该方法能够更好地从超声图像数据中识别标准或非标准切面，为早孕期胎儿产前诊断中标准切面的自动获取提供了理论依据。
