Ultrasound fetal brain registration using weighted coherent point drift

Three dimensional ultrasound imaging has become the main modality for fetal health diagnostics, with extensive use in fetal brain imaging. According to the fetal position and the stage of development of the fetal skull, a specific plane of image acquisition is required. In most cases for a single plane of acquisition, the image quality is limited by the shadows produced by the skull. In this work a new method for registration of multiple views of 3D ultrasound of the fetal brain is reported, which results in improved imaging of the internal brain structures. In the initial stage, texture, intensity and edge features are used, with a support vector machine (SVM) for the segmentation of the skull in each of the 3D ultrasound views to be registered. The segmentation of each skull is modelled as a set of points with the centre determined with a Gaussian mixture model, where each point is assigned a probability of membership to a Gaussian determined by the posterior probability assigned by the SVM. Our method has shown improved results compared to intensity based registration, with a 52% reduction in the target registration error (TRE), and a 39% reduction in the TRE compared to feature based registration. These are encouraging results for the future development of an automatic method for registration and fusion of multiple views of 3D fetal ultrasound.