Automatic fetal face detection from ultrasound volumes via learning 3D and 2D information

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 dual-core 2.0 GHz computer.