Abstract
3D ultrasound imaging has been increasingly used in
clinics for fetal examination. However, manually searching
for the optimal view of the fetal face in 3D ultrasound volumes
is cumbersome and time-consuming even for expert
physicians and sonographers. In this paper we propose a
learning-based approach which combines both 3D and 2D
information for automatic and fast fetal face detection from
3D ultrasound volumes. Our approach applies a new technique
– constrained marginal space learning – for 3D face
mesh detection, and combines a boosting-based 2D profile
detection to refine 3D face pose. To enhance the rendering
of the fetal face, an automatic carving algorithm is proposed
to remove all obstructions in front of the face based
on the detected face mesh. Experiments are performed on
a challenging 3D ultrasound data set containing 1010 fetal
volumes. The results show that our system not only achieves
excellent detection accuracy but also runs very fast – it can
detect the fetal face from the 3D data in 1 second on a dualcore
2.0 GHz computer.