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The method involves first translating the medical image volumetric data into a three\u2010dimensional computational domain of a porous material. A velocity field is then obtained from numerical simulations of incompressible fluid flow in the porous material, and finally a velocity iso\u2010surface provides the surface description of the target object. The approach is tested on CT images of eight patient\u2010specific cases, where cerebral aneurysms, nasal cavities (NC), and an aortic arch (AA) are the objects of interest. In the aneurysm cases, the results are compared against constant greyscale thresholding and manual segmentation. The manual segmentations of the aneurysms are validated by a clinical practitioner. Only a qualitative comparison is available for the NC, and the AA geometries. The results show that the proposed method is effective and capable of extracting the target object in a noisy domain. A sensitivity study is carried out to verify the method's performance with respect to modelling or user choices. The segmentation by the proposed method is also evaluated by performing computational fluid dynamics simulation, including a near\u2010wall flow analysis, to ensure that the segmented geometry and the resulting computed solution are representative and meaningful.<\/jats:p>","DOI":"10.1002\/cnm.3580","type":"journal-article","created":{"date-parts":[[2022,2,10]],"date-time":"2022-02-10T06:44:17Z","timestamp":1644475457000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A porosity model for medical image segmentation of vessels"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9757-5852","authenticated-orcid":false,"given":"Vahid","family":"Goodarzi Ardakani","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering University of Bristol  Bristol UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3291-2827","authenticated-orcid":false,"given":"Alberto M.","family":"Gambaruto","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering University of Bristol  Bristol UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5719-799X","authenticated-orcid":false,"given":"Goncalo","family":"Silva","sequence":"additional","affiliation":[{"name":"IDMEC, Mechatronics Department University of \u00c9vora  \u00c9vora Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6895-1318","authenticated-orcid":false,"given":"Ricardo","family":"Pereira","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery Coimbra University Hospital Center, Faculty of Medicine, University of Coimbra  Coimbra Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.3934\/mbe.2011.8.409"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/fld.1642"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1115\/1.2354209"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2005.844159"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2015.08.014"},{"key":"e_1_2_7_7_1","first-page":"144","volume-title":"Sketch\u2010based editing tools for tumour segmentation in 3D medical images","author":"Heckel F","year":"2013"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2009.05.004"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-015-9839-8"},{"key":"e_1_2_7_10_1","doi-asserted-by":"crossref","unstructured":"PGhosh&MMitchell. 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