Med Biol Eng Comput - Automatic segmentation of the fetal cerebellum on ultrasound volumes, using a 3D statistical shape model.

Tópicos

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Resumo

Previous work has shown that the segmentation of anatomical structures on 3D ultrasound data sets provides an important tool for the assessment of the fetal health. In this work, we present an algorithm based on a 3D statistical shape model to segment the fetal cerebellum on 3D ultrasound volumes. This model is adjusted using an ad hoc objective function which is in turn optimized using the Nelder-Mead simplex algorithm. Our algorithm was tested on ultrasound volumes of the fetal brain taken from 20 pregnant women, between 18 and 24 gestational weeks. An intraclass correlation coefficient of 0.8528 and a mean Dice coefficient of 0.8 between cerebellar volumes measured using manual techniques and the volumes calculated using our algorithm were obtained. As far as we know, this is the first effort to automatically segment fetal intracranial structures on 3D ultrasound data.

Resumo Limpo

previous work shown segment anatom structur d ultrasound data set provid import tool assess fetal health work present algorithm base d statist shape model segment fetal cerebellum d ultrasound volum model adjust use ad hoc object function turn optim use neldermead simplex algorithm algorithm test ultrasound volum fetal brain taken pregnant women gestat week intraclass correl coeffici mean dice coeffici cerebellar volum measur use manual techniqu volum calcul use algorithm obtain far know first effort automat segment fetal intracrani structur d ultrasound data

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