Automatic imaging biomarkers in foetal development using multi-task deep learning framework
Ultrasonography is widely used in the field of obstetrics, which is a popular way of assessing the state of foetal development (e.g. gestational age (GA) estimation, foetal weight (FW) estimation) and safety of the pregnancy. The operator usually performs an array of measurements during ultrasound (US) scan sessions. The scans are safe and can be carried out at any stage of your pregnancy. Over the years, some researchers have demonstrated that deep learning algorithms are used to reduce operator dependent errors and improve the accuracy of foetal well-being assessment, but the use of AI is still in a stage of infancy.
Foetal biometric measurement is a standard examination during pregnancy used for the foetal growth monitoring and estimation of gestational age. The most important foetal measurements include the measurements of biparietal diameter (BPD), head circumference (HC), femur length (FL) and abdominal circumference (AC). To obtain these proper measurements, it requires the use of standardised planes. The operator needs substantial knowledge and experience to identify the standardised planes that are foetal abdomen (FASP), brain (FBSP) and femur (FFESP) standard planes. However, expert resources are scarce, especially in underdeveloped countries. We believe that deep learning algorithms can be a valuable tool to tackle these challenges. Biometry measurements are performed on ultrasound images that have standardised planes. After that, these biometric parameters can be used to evaluate foetus growth and estimate the following parameters: GA and FW.
The first phase of this research is using a literature review to understand the recent research results
related to foetal ultrasound deep learning applications. The second phase of this research is exploring multi-task neural networks for automatically classifying and segmenting foetal body parts in 2D ultrasound images. Foetal body parts include the foetal head, abdomen and femur. The third phase of this research is developing a novel deep learning framework for measuring the foetal body parts, BPD, HC, FL and AC. These foetal biometrics are used to assess foetal growth.
The results of this research may reduce the rates of misdiagnosis for foetus growth monitoring. Thus, it contributes to the improvement of the quality of medical services and ultimately benefits patients.