{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T02:31:05Z","timestamp":1776825065312,"version":"3.51.2"},"reference-count":11,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T00:00:00Z","timestamp":1685404800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T00:00:00Z","timestamp":1685404800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pediatr Radiol"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This technical innovation assesses the adaptability of some common automated segmentation tools on abnormal pediatric magnetic resonance (MR) brain scans. We categorized 35 MR scans\u00a0by pathologic features: (1) \u201cnormal\u201d; (2) \u201catrophy\u201d; (3) \u201ccavity\u201d; (4) \u201cother.\u201d The following three\u00a0tools, (1) Computational Anatomy Toolbox version 12 (CAT12); (2) Statistical Parametic Mapping version 12 (SPM12); and (3) MRTool, were tested on each scan\u2014with default and adjusted settings. Success was determined by radiologist consensus on the surface accuracy. Automated segmentation failed in scans demonstrating severe surface brain pathology. Segmentation of the \u201ccavity\u201d group was ineffective, with success rates of 23.1% (CAT12), 69.2% (SPM12) and 46.2% (MRTool), even with refined settings and manual edits. Further investigation is required to improve this workflow and automated segmentation methodology for complex surface pathology.\n<\/jats:p>","DOI":"10.1007\/s00247-023-05692-9","type":"journal-article","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T11:02:27Z","timestamp":1685444547000},"page":"1854-1862","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Caution: shortcomings of traditional segmentation methods from magnetic resonance imaging brain scans intended for 3-dimensional surface modelling in children with pathology"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1036-2959","authenticated-orcid":false,"given":"Anith","family":"Chacko","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sean","family":"Schoeman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shyam Sunder B.","family":"Venkatakrishna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samuel","family":"Bolton","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew I. U.","family":"Shearn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Savvas","family":"Andronikou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,30]]},"reference":[{"key":"5692_CR1","doi-asserted-by":"publisher","first-page":"111","DOI":"10.4103\/ijri.IJRI_130_20","volume":"30","author":"A Chacko","year":"2020","unstructured":"Chacko A, Vedajallam S, Andronikou S, Simpson E, Thai NJ (2020) Accuracy of radiologists, nonradiologists, and laypeople for identifying children with cerebral cortical atrophy from \u201cMercator map\u201d curved reconstructions of MRIs of the brain. Indian J Radiol Imaging 30:111\u2013115","journal-title":"Indian J Radiol Imaging"},{"key":"5692_CR2","doi-asserted-by":"crossref","unstructured":"Andronikou S, Simpson E, Klemm M et al (2018) Technical report: 3D printing of the brain for use as a visual-aid tool to communicate MR imaging features of hypoxic ischaemic injury at term with non-physicians. Child's Nerv Syst 34:1573\u20131577","DOI":"10.1007\/s00381-018-3838-2"},{"key":"5692_CR3","doi-asserted-by":"crossref","unstructured":"Naftulin JS, Kimchi EY, Cash SS (2015) Streamlined, inexpensive 3D printing of the brain and skull. PLoS\u00a0One 10:e0136198","DOI":"10.1371\/journal.pone.0136198"},{"key":"5692_CR4","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1067\/j.cpradiol.2015.07.009","volume":"45","author":"A Marro","year":"2016","unstructured":"Marro A, Bandukwala T, Mak W (2016) Three-dimensional printing and medical imaging: a review of the methods and applications. 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