Difference of Gaussians revolved along elliptical paths for ultrasound fetal head segmentation

•A new method for the fetal skull segmentation from 2-D ultrasound images.•The method is completely automatic and it requires no user interaction.•The idea is to construct a template image of the fetal skull parametrized by both the calvarial thickness (the thickness of the skull) and an ellipse modelling the contour of the skull.•This method won the fetal ultrasound segmentation challenge in ISBI 2012.•The accuracy automatic segmentations are similar to the accuracy of the manual segmentations.

We present a fully automatic method to segment the skull from 2-D ultrasound images of the fetal head and to compute the standard biometric measurements derived from the segmented images. The method is based on the minimization of a novel cost function. The cost function is formulated assuming that the fetal skull has an approximately elliptical shape in the image and that pixel values within the skull are on average higher than in surrounding tissues. The main idea is to construct a template image of the fetal skull parametrized by the ellipse parameters and the calvarial thickness. The cost function evaluates the match between the template image and the observed ultrasound image. The optimum solution that minimizes the cost is found by using a global multiscale, multistart Nelder–Mead algorithm. The method was qualitatively and quantitatively evaluated using 90 ultrasound images from a recent segmentation grand challenge. These images have been manually analyzed by three independent experts. The segmentation accuracy of the automatic method was similar to the inter-expert segmentation variability. The automatically derived biometric measurements were as accurate as the manual measurements. Moreover, the segmentation accuracy of the presented method was superior to the accuracy of the other automatic methods that have previously been evaluated using the same data.

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