  Measurement of head biometrics from fetal ultrasonography images is of key
importance in monitoring the healthy development of fetuses. However, the
accurate measurement of relevant anatomical structures is subject to large
inter-observer variability in the clinic. To address this issue, an automated
method utilizing Fully Convolutional Networks (FCN) is proposed to determine
measurements of fetal head circumference (HC) and biparietal diameter (BPD). An
FCN was trained on approximately 2000 2D ultrasound images of the head with
annotations provided by 45 different sonographers during routine screening
examinations to perform semantic segmentation of the head. An ellipse is fitted
to the resulting segmentation contours to mimic the annotation typically
produced by a sonographer. The model's performance was compared with
inter-observer variability, where two experts manually annotated 100 test
images. Mean absolute model-expert error was slightly better than
inter-observer error for HC (1.99mm vs 2.16mm), and comparable for BPD (0.61mm
vs 0.59mm), as well as Dice coefficient (0.980 vs 0.980). Our results
demonstrate that the model performs at a level similar to a human expert, and
learns to produce accurate predictions from a large dataset annotated by many
sonographers. Additionally, measurements are generated in near real-time at
15fps on a GPU, which could speed up clinical workflow for both skilled and
trainee sonographers.
