{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T11:19:25Z","timestamp":1768303165099,"version":"3.49.0"},"reference-count":38,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T00:00:00Z","timestamp":1623801600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100014440","name":"Ministerio de Ciencia, Innovaci\u00f3n y Universidades","doi-asserted-by":"publisher","award":["DPI2017\u201086696\u2010R"],"award-info":[{"award-number":["DPI2017\u201086696\u2010R"]}],"id":[{"id":"10.13039\/100014440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014440","name":"Ministerio de Ciencia, Innovaci\u00f3n y Universidades","doi-asserted-by":"publisher","award":["PRE2018\u2010083507"],"award-info":[{"award-number":["PRE2018\u2010083507"]}],"id":[{"id":"10.13039\/100014440","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Manual brain extraction from magnetic resonance (MR) images is time\u2010consuming and prone to intra\u2010 and inter\u2010rater variability. Several automated approaches have been developed to alleviate these constraints, including deep learning pipelines. However, these methods tend to reduce their performance in unseen magnetic resonance imaging (MRI) scanner vendors and different imaging protocols.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To present and evaluate for clinical use PARIETAL, a pre\u2010trained deep learning brain extraction method. We compare its reproducibility in a scan\/rescan analysis and its robustness among scanners of different manufacturers.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Retrospective.<\/jats:p><\/jats:sec><jats:sec><jats:title>Population<\/jats:title><jats:p>Twenty\u2010one subjects (12 women) with age range 22\u201348\u2009years acquired using three different MRI scanner machines including scan\/rescan in each of them.<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>T1\u2010weighted images acquired in a 3\u2010T Siemens with magnetization prepared rapid gradient\u2010echo sequence and two 1.5\u2009T scanners, Philips and GE, with spin\u2010echo and spoiled gradient\u2010recalled (SPGR) sequences, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>Analysis of the intracranial cavity volumes obtained for each subject on the three different scanners and the scan\/rescan acquisitions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Parametric permutation tests of the differences in volumes to rank and statistically evaluate the performance of PARIETAL compared to state\u2010of\u2010the\u2010art methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The mean absolute intracranial volume differences obtained by PARIETAL in the scan\/rescan analysis were 1.88\u2009mL, 3.91\u2009mL, and 4.71\u2009mL for Siemens, GE, and Philips scanners, respectively. PARIETAL was the best\u2010ranked method on Siemens and GE scanners, while decreasing to Rank 2 on the Philips images. Intracranial differences for the same subject between scanners were 5.46\u2009mL, 27.16\u2009mL, and 30.44\u2009mL for GE\/Philips, Siemens\/Philips, and Siemens\/GE comparison, respectively. The permutation tests revealed that PARIETAL was always in Rank 1, obtaining the most similar volumetric results between scanners.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>PARIETAL accurately segments the brain and it generalizes to images acquired at different sites without the need of training or fine\u2010tuning it again. PARIETAL is publicly available.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>2<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy Stage<\/jats:title><jats:p>2<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.27776","type":"journal-article","created":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T02:50:30Z","timestamp":1623898230000},"page":"1991-2000","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Assessing the Accuracy and Reproducibility of <scp>PARIETAL<\/scp>: A Deep Learning Brain Extraction Algorithm"],"prefix":"10.1002","volume":"59","author":[{"given":"Sergi","family":"Valverde","sequence":"first","affiliation":[{"name":"Research Institute of Computer Vision and Robotics University of Girona  Girona Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Llucia","family":"Coll","sequence":"additional","affiliation":[{"name":"Research Institute of Computer Vision and Robotics University of Girona  Girona Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3082-3572","authenticated-orcid":false,"given":"Liliana","family":"Valencia","sequence":"additional","affiliation":[{"name":"Research Institute of Computer Vision and Robotics University of Girona  Girona Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Albert","family":"Cl\u00e8rigues","sequence":"additional","affiliation":[{"name":"Research Institute of Computer Vision and Robotics University of Girona  Girona Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0115-0647","authenticated-orcid":false,"given":"Arnau","family":"Oliver","sequence":"additional","affiliation":[{"name":"Research Institute of Computer Vision and Robotics University of Girona  Girona Spain"},{"name":"REEM, Red Espa\u00f1ola de Esclerosis M\u00faltiple"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2148-6751","authenticated-orcid":false,"given":"Joan C.","family":"Vilanova","sequence":"additional","affiliation":[{"name":"Girona Magnetic Resonance Center  Girona Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Llu\u00eds","family":"Rami\u00f3\u2010Torrent\u00e0","sequence":"additional","affiliation":[{"name":"REEM, Red Espa\u00f1ola de Esclerosis M\u00faltiple"},{"name":"Multiple Sclerosis and Neuroimmunology Unit, Neurology Department Dr. Josep Trueta University Hospital, Institut d'Investigaci\u00f3 Biom\u00e8dica  Girona Spain"},{"name":"Medical Sciences Department University of Girona  Girona Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u00c0lex","family":"Rovira","sequence":"additional","affiliation":[{"name":"Magnetic Resonance Unit, Department of Radiology Vall d'Hebron University Hospital  Barcelona Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xavier","family":"Llad\u00f3","sequence":"additional","affiliation":[{"name":"Research Institute of Computer Vision and Robotics University of Girona  Girona Spain"},{"name":"REEM, Red Espa\u00f1ola de Esclerosis M\u00faltiple"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,6,16]]},"reference":[{"key":"e_1_2_9_2_1","unstructured":"PayanA MontanaG.Predicting Alzheimer's disease: A neuroimaging study with 3D convolutional neural networks.arXivpreprint arXiv:150202506 2015."},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2528821"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2538465"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2004.03.032"},{"key":"e_1_2_9_6_1","doi-asserted-by":"crossref","unstructured":"VaishaliS RaoKK RaoGVS.A review on noise reduction methods for brain MRI images. 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