{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:48:33Z","timestamp":1740138513463,"version":"3.37.3"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T00:00:00Z","timestamp":1630886400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T00:00:00Z","timestamp":1630886400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100004440","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["203139\/Z\/16\/Z"],"award-info":[{"award-number":["203139\/Z\/16\/Z"]}],"id":[{"id":"10.13039\/100004440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000364","name":"Stroke Association","doi-asserted-by":"publisher","award":["TSA LECT 2015\/02"],"award-info":[{"award-number":["TSA LECT 2015\/02"]}],"id":[{"id":"10.13039\/501100000364","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neuroinform"],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Cranial cavity extraction is often the first step in quantitative neuroimaging analyses. However, few automated, validated extraction tools have been developed for non-contrast enhanced CT scans (NECT). The purpose of this study was to compare and contrast freely available tools in an unseen dataset of real-world clinical NECT head scans in order to assess the performance and generalisability of these tools. This study included data from a demographically representative sample of 428 patients who had completed NECT scans following hospitalisation for stroke. In a subset of the scans (<jats:italic>n<\/jats:italic>\u00a0=\u200920), the intracranial spaces were segmented using automated tools and compared to the gold standard of manual delineation to calculate accuracy, precision, recall, and dice similarity coefficient (DSC) values. Further, three readers independently performed regional visual comparisons of the quality of the results in a larger dataset (<jats:italic>n<\/jats:italic>\u00a0=\u2009428). Three tools were found; one of these had unreliable performance so subsequent evaluation was discontinued. The remaining tools included one that was adapted from the FMRIB software library (fBET) and a convolutional neural network- based tool (rBET). Quantitative comparison showed comparable accuracy, precision, recall and DSC values (fBET: 0.984\u2009\u00b1\u20090.002; rBET: 0.984\u2009\u00b1\u20090.003; <jats:italic>p<\/jats:italic>\u00a0=\u20090.99) between the tools; however, intracranial volume was overestimated. Visual comparisons identified characteristic regional differences in the resulting cranial cavity segmentations. Overall fBET had highest visual quality ratings and was preferred by the readers in the majority of subject results (84%). However, both tools produced high quality extractions of the intracranial space and our findings should improve confidence in these automated CT tools. Pre- and post-processing techniques may further improve these results.<\/jats:p>","DOI":"10.1007\/s12021-021-09534-7","type":"journal-article","created":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T19:02:55Z","timestamp":1630954975000},"page":"587-598","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Comparison of Cranial Cavity Extraction Tools for Non-contrast Enhanced CT Scans in Acute Stroke Patients"],"prefix":"10.1007","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2846-4101","authenticated-orcid":false,"given":"L.","family":"Vass","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. J.","family":"Moore","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"T.","family":"Hanayik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G.","family":"Mair","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S. T.","family":"Pendlebury","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"N.","family":"Demeyere","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M.","family":"Jenkinson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,6]]},"reference":[{"key":"9534_CR1","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.neucom.2018.12.085","volume":"392","author":"Z Akkus","year":"2020","unstructured":"Akkus, Z., Kostandy, P., Philbrick, K. A., & Erickson, B. J. (2020). Robust brain extraction tool for CT head images. Neurocomputing, 392, 189\u2013195. https:\/\/doi.org\/10.1016\/j.neucom.2018.12.085.","journal-title":"Neurocomputing"},{"key":"9534_CR2","doi-asserted-by":"crossref","unstructured":"Bauer S., Fejes T., & Reyes M. (2012). Skull-Stripping Filter for ITK. The Insight Journal. January-December. http:\/\/hdl.handle.net\/10380\/3353; http:\/\/www.insight-journal.org\/browse\/publication\/859","DOI":"10.54294\/dp4mfp"},{"issue":"3","key":"9534_CR3","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.1016\/j.neuroimage.2010.03.012","volume":"51","author":"R de Boer","year":"2010","unstructured":"de Boer, R., Vrooman, H. A., Ikram, M. A., Vernooij, M. W., Breteler, M. M. B., van der Lugt, A., & Niessen, W. J. (2010). Accuracy and reproducibility study of automatic MRI brain tissue segmentation methods. NeuroImage, 51(3), 1047\u20131056. https:\/\/doi.org\/10.1016\/j.neuroimage.2010.03.012.","journal-title":"NeuroImage"},{"issue":"3","key":"9534_CR4","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1111\/ijs.12424","volume":"10","author":"AM Demchuk","year":"2015","unstructured":"Demchuk, A. M., Goyal, M., Menon, B. K., Eesa, M., Ryckborst, K. J., Kamal, N., Patil, S., Mishra, S., Almekhlafi, M., Randhawa, P. A., Roy, D., Willinsky, R., Montanera, W., Silver, F. L., Shuaib, A., Rempel, J., Jovin, T., Frei, D., Sapkota, B., Thornton, J. M., Poppe, A., Tampieri, D., Lum, C., Weill, A., Sajobi, T. T., Hill, M. D., & for the ESCAPE Trial Investigators. (2015). Endovascular treatment for small Core and anterior circulation proximal occlusion with emphasis on minimizing CT to recanalization times (ESCAPE) trial: Methodology. International Journal of Stroke: Official Journal of the International Stroke Society, 10(3), 429\u2013438. https:\/\/doi.org\/10.1111\/ijs.12424.","journal-title":"International Journal of Stroke: Official Journal of the International Stroke Society"},{"issue":"3","key":"9534_CR5","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1037\/pas0000082","volume":"27","author":"N Demeyere","year":"2015","unstructured":"Demeyere, N., Riddoch, M. J., Slavkova, E. D., Bickerton, W. L., & Humphreys, G. W. (2015). The Oxford cognitive screen (OCS): Validation of a stroke-specific short cognitive screening tool. Psychological Assessment, 27(3), 883\u2013894. https:\/\/doi.org\/10.1037\/pas0000082.","journal-title":"Psychological Assessment"},{"issue":"2","key":"9534_CR6","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1007\/s00415-015-7964-4","volume":"263","author":"N Demeyere","year":"2016","unstructured":"Demeyere, N., Riddoch, M. J., Slavkova, E. D., Jones, K., Reckless, I., Mathieson, P., & Humphreys, G. W. (2016). Domain-specific versus generalized cognitive screening in acute stroke. Journal of Neurology, 263(2), 306\u2013315. https:\/\/doi.org\/10.1007\/s00415-015-7964-4.","journal-title":"Journal of Neurology"},{"key":"9534_CR7","doi-asserted-by":"publisher","unstructured":"Demeyere, N., et al. (2019). Post-stroke cognition with the Oxford cognitive screen vs Montreal cognitive assessment: A multi-site randomized controlled study (OCS-CARE) [version 1; peer review: 1 approved, 1 approved with reservations]. AMRC Open Research, 1(12). https:\/\/doi.org\/10.12688\/amrcopenres.12882.1.","DOI":"10.12688\/amrcopenres.12882.1"},{"issue":"2","key":"9534_CR8","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1002\/hbm.20161","volume":"27","author":"C Fennema-Notestine","year":"2006","unstructured":"Fennema-Notestine, C., Ozyurt, I. B., Clark, C. P., Morris, S., Bischoff-Grethe, A., Bondi, M. W., Jernigan, T. L., Fischl, B., Segonne, F., Shattuck, D. W., Leahy, R. M., Rex, D. E., Toga, A. W., Zou, K. H., Morphometry BIRN, & Brown, G. G. (2006). Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: Effects of diagnosis, bias correction, and slice location. Human Brain Mapping, 27(2), 99\u2013113. https:\/\/doi.org\/10.1002\/hbm.20161.","journal-title":"Human Brain Mapping"},{"issue":"2","key":"9534_CR9","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1006\/nimg.2002.1132","volume":"17","author":"M Jenkinson","year":"2002","unstructured":"Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2), 825\u2013841. https:\/\/doi.org\/10.1006\/nimg.2002.1132.","journal-title":"NeuroImage"},{"issue":"2","key":"9534_CR10","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/S1361-8415(01)00036-6","volume":"5","author":"M Jenkinson","year":"2001","unstructured":"Jenkinson, M., & Smith, S. (2001). A global optimisation method for robust affine registration of brain images. Medical Image Analysis, 5(2), 143\u2013156. https:\/\/doi.org\/10.1016\/S1361-8415(01)00036-6.","journal-title":"Medical Image Analysis"},{"issue":"1","key":"9534_CR11","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.neuroimage.2010.01.091","volume":"51","author":"A Klein","year":"2010","unstructured":"Klein, A., Ghosh, S. S., Avants, B., Yeo, B. T. T., Fischl, B., Ardekani, B., Gee, J. C., Mann, J. J., & Parsey, R. V. (2010). Evaluation of volume-based and surface-based brain image registration methods. NeuroImage, 51(1), 214\u2013220. https:\/\/doi.org\/10.1016\/j.neuroimage.2010.01.091.","journal-title":"NeuroImage"},{"issue":"2","key":"9534_CR12","doi-asserted-by":"publisher","first-page":"113","DOI":"10.3171\/2014.9.PEDS12426","volume":"15","author":"JG Mandell","year":"2015","unstructured":"Mandell, J. G., et al. (2015). Volumetric brain analysis in neurosurgery: Part 1. Particle filter segmentation of brain and cerebrospinal fluid growth dynamics from MRI and CT images. Journal of Neurosurgery: Pediatrics PED, 15(2), 113\u2013124. https:\/\/doi.org\/10.3171\/2014.9.PEDS12426.","journal-title":"Journal of Neurosurgery: Pediatrics PED"},{"key":"9534_CR13","unstructured":"MATLAB (2020). Version 9.9.0 (R2020b).\u00a0The Mathworks Inc. Natick Massachusetts."},{"issue":"10","key":"9534_CR14","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1001\/jama.2018.12498","volume":"320","author":"BK Menon","year":"2018","unstructured":"Menon, B. K., al-Ajlan, F. S., Najm, M., Puig, J., Castellanos, M., Dowlatshahi, D., Calleja, A., Sohn, S. I., Ahn, S. H., Poppe, A., Mikulik, R., Asdaghi, N., Field, T. S., Jin, A., Asil, T., Boulanger, J. M., Smith, E. E., Coutts, S. B., Barber, P. A., Bal, S., Subramanian, S., Mishra, S., Trivedi, A., Dey, S., Eesa, M., Sajobi, T., Goyal, M., Hill, M. D., Demchuk, A. M., & for the INTERRSeCT Study Investigators. (2018). Association of Clinical, imaging, and Thrombus characteristics with recanalization of visible intracranial occlusion in patients with acute ischemic stroke. JAMA, 320(10), 1017\u20131026. https:\/\/doi.org\/10.1001\/jama.2018.12498.","journal-title":"JAMA"},{"key":"9534_CR15","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1016\/j.neuroimage.2015.03.074","volume":"114","author":"J Muschelli","year":"2015","unstructured":"Muschelli, J., Ullman, N. L., Mould, W. A., Vespa, P., Hanley, D. F., & Crainiceanu, C. M. (2015). Validated automatic brain extraction of head CT images. NeuroImage, 114, 379\u2013385. https:\/\/doi.org\/10.1016\/j.neuroimage.2015.03.074.","journal-title":"NeuroImage"},{"key":"9534_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cmpb.2019.04.030","volume":"176","author":"M Najm","year":"2019","unstructured":"Najm, M., Kuang, H., Federico, A., Jogiat, U., Goyal, M., Hill, M. D., Demchuk, A., Menon, B. K., & Qiu, W. (2019). Automated brain extraction from head CT and CTA images using convex optimization with shape propagation. Computer Methods and Programs in Biomedicine, 176, 1\u20138. https:\/\/doi.org\/10.1016\/j.cmpb.2019.04.030.","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"9534_CR17","doi-asserted-by":"publisher","unstructured":"Preim, B., & Botha, C. (2014). Chapter 4 - image analysis for medical visualization. In B. Preim & C. Botha (Eds.), Visual computing for medicine (2nd ed., pp. 111\u2013175). Morgan Kaufmann. https:\/\/doi.org\/10.1016\/B978-0-12-415873-3.00004-3.","DOI":"10.1016\/B978-0-12-415873-3.00004-3"},{"issue":"4","key":"9534_CR18","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1016\/j.neuroimage.2012.03.020","volume":"61","author":"C Rorden","year":"2012","unstructured":"Rorden, C., Bonilha, L., Fridriksson, J., Bender, B., & Karnath, H. O. (2012). Age-specific CT and MRI templates for spatial normalization. NeuroImage, 61(4), 957\u2013965. https:\/\/doi.org\/10.1016\/j.neuroimage.2012.03.020.","journal-title":"NeuroImage"},{"issue":"3","key":"9534_CR19","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1002\/hbm.10062","volume":"17","author":"SM Smith","year":"2002","unstructured":"Smith, S. M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17(3), 143\u2013155. https:\/\/doi.org\/10.1002\/hbm.10062.","journal-title":"Human Brain Mapping"},{"issue":"3","key":"9534_CR20","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.cmpb.2007.02.006","volume":"86","author":"J Solomon","year":"2007","unstructured":"Solomon, J., Raymont, V., Braun, A., Butman, J. A., & Grafman, J. (2007). User-friendly software for the analysis of brain lesions (ABLe). Computer Methods and Programs in Biomedicine, 86(3), 245\u2013254. https:\/\/doi.org\/10.1016\/j.cmpb.2007.02.006.","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"1","key":"9534_CR21","doi-asserted-by":"publisher","first-page":"e77810","DOI":"10.1371\/journal.pone.0077810","volume":"9","author":"Y Wang","year":"2014","unstructured":"Wang, Y., Nie, J., Yap, P. T., Li, G., Shi, F., Geng, X., Guo, L., Shen, D., & for the Alzheimer's Disease Neuroimaging Initiative. (2014). Knowledge-guided robust MRI brain extraction for diverse large-scale neuroimaging studies on humans and non-human primates. PLoS One, 9(1), e77810\u2013e77810. https:\/\/doi.org\/10.1371\/journal.pone.0077810.","journal-title":"PLoS One"}],"container-title":["Neuroinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-021-09534-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12021-021-09534-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-021-09534-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T23:09:55Z","timestamp":1665270595000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12021-021-09534-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,6]]},"references-count":21,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["9534"],"URL":"https:\/\/doi.org\/10.1007\/s12021-021-09534-7","relation":{},"ISSN":["1539-2791","1559-0089"],"issn-type":[{"type":"print","value":"1539-2791"},{"type":"electronic","value":"1559-0089"}],"subject":[],"published":{"date-parts":[[2021,9,6]]},"assertion":[{"value":"14 June 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 September 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"LV, MJM, TH, STP, ND and GM report no conflicts of interest. MJ receives royalties from Oxford University Innovations for the commercial use of the FSL software.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}