{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T20:44:07Z","timestamp":1765485847714},"reference-count":34,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["PED"],"published-print":{"date-parts":[[2015,2]]},"abstract":"\nOBJECT<\/jats:title>\nAccurate edge tracing segmentation remains an incompletely solved problem in brain image analysis. The authors propose a novel algorithm using a particle filter to follow the boundary of the brain in the style often used in autonomous air and ground vehicle navigation. Their goals were to create a versatile tool to segment brain and fluid in MRI and CT images of the developing brain, lay the foundation for an intelligent automated edge tracker that is modality independent, and segment normative data from MRI that can be applied to both MRI and CT.<\/jats:p><\/jats:sec>\n\nMETHODS<\/jats:title>\nSimulated MRI data sets were used to train and evaluate the particle filter segmentation algorithm. The method was then applied to produce normative growth curves for children and adolescents from 0 to 18 years of age for brain and fluid from MR images from the National Institutes of Health pediatric database and these data were compared to historical results. The authors further adapted this method for use with CT images of pediatric hydrocephalus and compared the results with hand-segmented data.<\/jats:p><\/jats:sec>\n\nRESULTS<\/jats:title>\nSegmentation of simulated MRI data with varied levels of noise (0%\u20139%) and spatial inhomogeneity (0%\u201340%) resulted in percent errors ranging from 0.06% to 5.38% for brain volume and 2.45% to 22.3% for fluid volume. The authors used this tool to create normal brain and CSF growth curves from MR images. The calculated growth curves showed excellent consistency with historical data. Additionally, compared with manual segmentation the particle filter accurately segmented brain and fluid volumes from CT scans of 5 pediatric patients with hydrocephalus (p < 0.001).<\/jats:p><\/jats:sec>\n\nCONCLUSIONS<\/jats:title>\nThe authors have produced the first normative brain and CSF growth curves for children and adolescents 0\u201318 years of age. In addition, this study includes the first use of a particle filter as an edge tracker in image segmentation and offers a semiautomatic method to segment both pediatric and adult brain data from MR and CT images. The particle filter has the potential to be further automated toward a clinical rather than research tool with both of these modalities. Because of its modality independence, it has the capability to allow CT to be a more effective diagnostic tool for neurological disorders, a task of substantial importance in emergency settings and in developing countries where CT is often the only available method of brain imaging.<\/jats:p><\/jats:sec>","DOI":"10.3171\/2014.9.peds12426","type":"journal-article","created":{"date-parts":[[2014,11,28]],"date-time":"2014-11-28T18:58:42Z","timestamp":1417201122000},"page":"113-124","source":"Crossref","is-referenced-by-count":30,"title":["Volumetric brain analysis in neurosurgery: Part 1. Particle filter segmentation of brain and cerebrospinal fluid growth dynamics from MRI and CT images"],"prefix":"10.3171","volume":"15","author":[{"given":"Jason G.","family":"Mandell","sequence":"first","affiliation":[{"name":"Center for Neural Engineering, Department of Engineering Science and Mechanics, and\u00a0"},{"name":"Departments of Bioengineering,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jack W.","family":"Langelaan","sequence":"additional","affiliation":[{"name":"Aerospace Engineering,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew G.","family":"Webb","sequence":"additional","affiliation":[{"name":"C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steven J.","family":"Schiff","sequence":"additional","affiliation":[{"name":"Center for Neural Engineering, Department of Engineering Science and Mechanics, and\u00a0"},{"name":"Neurosurgery, and"},{"name":"Physics, The Pennsylvania State University, University Park, Pennsylvania; and"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1754","reference":[{"key":"b1-peds12426","series-title":"IEEE Trans Med Imaging","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1109\/TMI.2004.826954","article-title":"An interacting multiple model probabilistic data association filter for cavity boundary extraction from ultrasound images","volume":"23","author":"Abolmaesumi","year":"2004"},{"key":"b2-peds12426","series-title":"Neuroimage","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1016\/j.neuroimage.2006.08.058","article-title":"The NIH MRI study of normal brain development (Objective-2): newborns, infants, toddlers, and preschoolers","volume":"35","author":"Almli","year":"2007"},{"key":"b3-peds12426","series-title":"Handbook of Medical Image Processing and Analysis","author":"Bankman","year":"2009"},{"key":"b4-peds12426","series-title":"IEEE Trans Med Imaging","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1109\/TMI.2007.893283","article-title":"Topology-preserving tissue classification of magnetic resonance brain images","volume":"26","author":"Bazin","year":"2007"},{"key":"b5-peds12426","series-title":"Epileptic Disord","first-page":"3","article-title":"MR imaging of epilepsy: state of the art at 1.5 T and potential of 3 T","volume":"5","author":"Briellmann","year":"2003"},{"key":"b6-peds12426","series-title":"NeuroImage","first-page":"S425","article-title":"Brain-Web: online interface to a 3D MRI simulated brain database. 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