Deep Learning Spatial Compounding from Multiple Fetal Head Ultrasound Acquisitions

Abstract

3D ultrasound systems have been widely used for fetal brain structures’ analysis. However, the obtained images present several artifacts such as multiplicative noise and acoustic shadows appearing as a function of acquisition angle. The purpose of this research is to merge several partially occluded ultrasound volumes, acquired by placing the transducer at different projections of the fetal head, to compound a new US volume containing the whole brain anatomy. To achieve this, the proposed methodology consists on the pipeline of four convolutional neural networks (CNN). Two CNNs are used to carry out fetal skull segmentations, by incorporating an incidence angle map and the segmented structures are then described with a Gaussian mixture model (GMM). For multiple US volumes registration, a feature set, based on distance maps computed from the GMM centroids is proposed. The third CNN learns the relation between distance maps of the volumes to be registered and estimates optimal rotation and translation parameters. Finally, the weighted root mean square is proposed as composition operator and weighting factors are estimated with the last CNN, which assigns a higher weight to those regions containing brain tissue and less ponderation to acoustic shadowed areas. The procedure was qualitatively and quantitatively validated in a set of fetal volumes obtained during gestation’s second trimester. Results show registration errors of 1.31 ± 0.2 mm and an increase of image sharpness of 34.9% compared to a single acquisition and of 25.2% compared to root mean square compounding.