Abstract：The transthalamic plane of fetal is used to measure the biparietal diameter and head circumference of the fetus, and these two measurement parameters play an important role in predicting the fetal weight. Clinically, the plane has always been manually acquired by the ultrasound doctor, and the quality of the manually obtained plane is highly dependent on the clinical work experience of the doctor, which is time consuming, and the poor image quality for plane often happen. In order to overcome the problem of manual acquisition, we proposed a novel method for the quality assessment of fetal head ultrasound images based onfaster region-based convolutional neural networks (faster R-CNN), aiming to help doctors automatically, quickly and accurately obtain the standard transthalamic plane. Frist, we set up an evaluation protocol with the team of ultrasound experts and build a database of fetal head ultrasound images through data-enhanced methods. Second, faster R-CNN could learn from the training data to extract useful features, and through the use of joint training and alternative optimization, so that the regional proposal networks (RPN) module and fast R-CNN module shared the convolution layer features and built a complete end-to-end CNN object detection model to detect the key anatomical structures. Finally, the transthalamic plane was automatically scored by the results of the detected anatomical structure, and according to the score results, it was automatically determined whether the plane was a standard one. We collected 513 ultrasound planes, 80% was used as a training dataset and 20% as a test dataset. Our method could accurately locate the five anatomical structures of the transthalamic plane with an average accuracy of 80.7% and the examination time of each ultrasound image was approximately 0.27 s, which indicated that it was feasible to perform automated quality control of fetal head ultrasound images by the proposed method.
林泽慧, 雷柏英, 姜峰, 倪东, 陈思平, 李胜利, 汪天富. 基于更快速的区域卷积神经网络的胎儿头围超声图像质量控制[J]. 中国生物医学工程学报, 2019, 38(4): 392-400.
Lin Zehui, Lei Baiying, Jiang Feng, Ni Dong, Chen Siping, Li Shengli, Wang Tianfu. Quality Assessment of Fetal Head Ultrasound Images Based on Faster R-CNN. Chinese Journal of Biomedical Engineering, 2019, 38(4): 392-400.
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