title: Automatic segmentation approach to extracting neonatal cerebral ventricles from 3D ultrasound images.
abstract: Preterm neonates with a very low birth weight of less than 1,500 g are at increased risk for developing intraventricular hemorrhage (IVH). Progressive ventricle dilatation of IVH patients may cause increased intracranial pressure, leading to neurological damage, such as neurodevelopmental delay and cerebral palsy. The technique of 3D ultrasound (US) imaging has been used to quantitatively monitor the ventricular volume in IVH neonates, which may elucidate the ambiguity surrounding the timing of interventions in these patients as 2D clinical US imaging relies on linear measurement and visual estimation of ventricular dilation from a series of 2D slices. To translate 3D US imaging into the clinical setting, a fully automated segmentation algorithm is necessary to extract the ventricular system from 3D neonatal brain US images. In this paper, an automatic segmentation approach is proposed to delineate lateral ventricles of preterm neonates from 3D US images. The proposed segmentation approach makes use of phase congruency map, multi-atlas initialization technique, atlas selection strategy, and a multiphase geodesic level-sets (MGLS) evolution combined with a spatial shape prior derived from multiple pre-segmented atlases. Experimental results using 30 IVH patient images show that the proposed GPU-implemented approach is accurate in terms of the Dice similarity coefficient (DSC), the mean absolute surface distance (MAD), and maximum absolute surface distance (MAXD). To the best of our knowledge, this paper reports the first study on automatic segmentation of the ventricular system of premature neonatal brains from 3D US images.
authors.affiliation: Robarts Research Institute, University of Western Ontario, London, ON, Canada; Department of Clinical Neuroscience, University of Calgary, Calgary, AB, Canada. Electronic address: qiu.wu.ch@gmail.com.
authors.forename: Wu
authors.initials: W
authors.lastname: Qiu
authors.affiliation: Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China. Electronic address: ym.chen@my.cityu.edu.hk.
authors.forename: Yimin
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Robarts Research Institute, University of Western Ontario, London, ON, Canada; Medical Biophysics, University of Western Ontario, London, ON, Canada.
authors.forename: Jessica
authors.initials: J
authors.lastname: Kishimoto
authors.affiliation: Neurosurgery, Department of Clinical Neurological Sciences, University of Western Ontario, London, ON, Canada.
authors.forename: Sandrine
authors.initials: S
authors.lastname: de Ribaupierre
authors.affiliation: Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China.
authors.forename: Bernard
authors.initials: B
authors.lastname: Chiu
authors.affiliation: Robarts Research Institute, University of Western Ontario, London, ON, Canada; Medical Biophysics, University of Western Ontario, London, ON, Canada.
authors.forename: Aaron
authors.initials: A
authors.lastname: Fenster
authors.affiliation: Robarts Research Institute, University of Western Ontario, London, ON, Canada; Medical Biophysics, University of Western Ontario, London, ON, Canada; Mathematics and Statistics School, Xidian University, Xian, China. Electronic address: jyuan@robarts.ca.
authors.forename: Jing
authors.initials: J
authors.lastname: Yuan
mesh_terms: D000465:Algorithms; D002552:Cerebral Ventricles / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007231:Infant, Newborn; D007234:Infant, Premature; D012680:Sensitivity and Specificity; D014463:Ultrasonography / Q000379:methods*
keywords: 3D Ultrasound imaging; Automatic neonatal cerebral ventricle segmentation; Convex optimization; Multi-phase geodesic level-sets