Shape model and marginal space of 3D ultrasound volume data for automatically detecting a fetal head

Nowadays, 3D ultrasound imaging has been increasingly used in clinics for fetal examination. However, it is cumbersome and time-consuming, even for an experienced clinician, to manually locate the fetal head and the mid-sagittal plane. In this paper, we introduce a totally automatic method for fetal head detection, which is based on a shape model and the marginal space learning framework. We approximate the shape of the fetal head as an oriented sphere defined by 7 parameters, turning the detection task into a process of parameter estimation. As the number of hypotheses increases exponentially with the dimensionality of the parameter space, exhaustive searching is computationally complex and time consuming. To reduce the number of the test hypotheses, we use a marginal space framework, which learns the parameters on sub-spaces in a sequential way. Haar features and steerable features are used in the learning based method. Once trained successfully, our method can be used to locate the fetal head from 3D ultrasound images, with no need for any other information.