Automatic Fetal Gestational Age Estimation from First Trimester Scans

Sevim Cengiz, Mohamed Bin Zayed University of Artificial Intelligence
Mohammad yaqub, Mohamed Bin Zayed University of Artificial Intelligence


Automatic Gestational Age (GA) estimation based on the Crown Rump Length (CRL) measurement is the preferred solution to overcome the challenges while using the last menstrual period (LMP) to date pregnancies. However, GA estimation based on CRL requires accurate placement of calipers on the fetal crown and rump which is not always a straightforward task, especially for an inexperienced sonographer. This paper proposes an accurate GA estimation method from fetal CRL images during the first trimester scan. The method addresses this problem by segmenting the fetus using a binary and multi-class U-Net. The fetal segmentation is used to compute the CRL. This is then followed by an estimation of GA from the automatic CRL measurement based of clinical information. The results from the multi-class segmentation achieves a more accurate precision, recall, Dice, and Jaccard. This has also led to a more accurate CRL measurement and hence more robust GA estimation.