Segmentation of fetal envelope from 3D ultrasound images based on pixel intensity statistical distribution and shape priors

This paper presents a novel shape-guided variational segmentation method for extracting the fetus envelope on 3D obstetric ultrasound images. Indeed, due to the inherent low quality of these images, classical segmentation methods tend to fail at segmenting these data. To compensate for the lack of contrast and of explicit boundaries, we introduce a segmentation framework that combines three different types of information: pixel intensity distribution, shape prior on the fetal envelope and a back model varying with fetus age. The intensity distributions, different for each tissue, and the shape prior, encoded with Legendre moments, are added as energy terms in the functional to be optimized. The back model is used in a post-processing step. Results on 3D ultrasound data are presented and compared to a set of manual segmentations. Both visual and quantitative comparisons show the satisfactory results obtained by this method on the tested data.