I. Introduction
It is extremely crucial for the parents to know if their babies are healthy or not during the pregnancy. The earlier the fetal abnormality could be found, the more chance the abnormality could be cured. Therefore, increasing attention and tremendous efforts have been put on improving the harmlessness, effectiveness, and robustness of fetal abnormality screening by researchers.
Although radiological examinations are known to be more precise on revealing abnormalities of human body, the side-effects are also significant. To avoid such potential, threaten to the fetus and the mothers, a less risky technology is required for the screening. Thus, ultrasound which has been proved to be less harmfulness, less expensive, and more convenient than other radiological techniques is widely utilized for the fetal abnormality screening. However, ultrasound examination is less automatic than other radiological exams as the sonographer needs manually hold the probe to conduct the examination. Therefore, more experienced and skillful sonographers are required for the ultrasound screening otherwise the results could not be reliable. Unfortunately, the shortage of experienced sonographers has been a severe problem ever since the born of ultrasound, and a qualified sonographer may need years for training which is extremely time consuming. To solve these problems, computer aided technologies are urgently demanded.
In the screening, the priority for a sonographer is to find a series of clinical standard planes which are a set of anatomical views of the fetus. Then, the diagnosis is to be made based on both subjective evaluation which is the observation of the sonographer, and objective evaluation which includes several physical measurements. Obviously, the final diagnosis is highly depending on the quality of standard anatomical views acquired by the sonographer. In other words, as far as the standard fetal anatomical planes can be precisely obtained, the diagnosis is to be more accurate and more reliable.
For the retrospective studies of the fetal growth and diseases, well organized and categorically arranged retrospective data are always required. However, that could usually be not true since all of the historical data are stored without any appropriate arrangement for most of the hospitals. Therefore, a technique which can automatically differentiate and separate the historical data according to the clinical meaning or usage is to be beneficial to the researches.
In this work, a deep neural network based framework is presented for classifying various types of standard anatomical planes of fetal head, i.e., Transventricular plane (TV), Transthalamic plane (TT), Transcerebellar plane (TC), Coronal view of eyes (Eyes), Coronal view of nose (Nose), and other non-standard fetal ultrasound images (Background). In the proposed framework, a YOLO based object detection network is applied to locate the head region in ultrasound image, and a set of powerful classification networks are utilized with model stacking technique to give a final judgement on each image based on the detected head regions. The contributions of this work are as follows: first of all, this is the first piece of work using deep learning technology to identify TV, TT, and TC to the best knowledge; secondly, this is the first piece of work which successfully applied YOLO on this topic; thirdly, the design of combining object detection network, object classification network, and model stacking technique is novel to this area; finally, the proposed framework achieves the state-of-the-art performance.
The rest of this paper is arranged as follows. In Sec. II, related works are briefly introduced. Then, the proposed framework is to be presented in Sec. III. Experimental results and discussion are reported in Sec. IV. The conclusion is finally made in Sec. V.