{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T11:10:06Z","timestamp":1791803406310,"version":"4.3.4"},"reference-count":42,"publisher":"IOP Publishing","issue":"2","license":[{"start":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T00:00:00Z","timestamp":1611100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T00:00:00Z","timestamp":1611100800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2021,1,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Electromagnetic-based navigation bronchoscopy requires accurate and robust estimation of the bronchoscope position inside the bronchial tree. However, respiratory motion, coughing, patient movement, and airway deformation inflicted by bronchoscope significantly hinder the accuracy of intraoperative bronchoscopic localization. In this study, a real-time and automatic registration procedure was proposed to superimpose the current location of the bronchoscope to corresponding locations on a centerline extracted from bronchial computed tomography (CT) images. A centerline-guided Gaussian mixture model (CG-GMM) was introduced to register a bronchoscope\u2019s position concerning extracted centerlines. A GMM was fitted to bronchoscope positions where the orientation likelihood was chosen to assign the membership probabilities of the mixture model, which led to preserving the global and local structures. The problem was formulated and solved under the expectation maximization framework, where the feature correspondence and spatial transformation are estimated iteratively. Validation was performed on a dynamic phantom with four different respiratory motions and four human real bronchoscopy (RB) datasets. Results of the experiments conducted on the bronchial phantom showed that the average positional tracking error using the proposed approach was equal to 1.98\n                    <jats:inline-formula>\n                      <jats:tex-math>\n                        \n                      <\/jats:tex-math>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mo>\u00b1<\/mml:mo>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    0.98 mm that was reduced in comparison with independent electromagnetic tracking (EMT), iterative closest point (ICP), and coherent point drift (CPD) methods by 64%, 58%, and 53%, respectively. In the patient assessment part of the study, the average positional tracking error was 4.73\n                    <jats:inline-formula>\n                      <jats:tex-math>\n                        \n                      <\/jats:tex-math>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mo>\u00b1<\/mml:mo>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    4.76 mm and compared to ICP, and CPD methods showed 31.4% improvement of successfully tracked frames. Our approach introduces a novel method for real-time respiratory motion compensation that provides reliable guidance during bronchoscopic interventions and, thus could increase the diagnostic yield of transbronchial biopsy.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/abca07","type":"journal-article","created":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T17:31:19Z","timestamp":1605202279000},"page":"025001","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Bronchoscope motion tracking using centerline-guided Gaussian mixture model in navigated bronchoscopy"],"prefix":"10.1088","volume":"66","author":[{"given":"Saeedeh","family":"Navaei Lavasani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7554-5545","authenticated-orcid":false,"given":"Parastoo","family":"Farnia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ebrahim","family":"Najafzadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samaneh","family":"Saghatchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehdi","family":"Samavati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamidreza","family":"Abtahi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammadreza","family":"Deevband","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alireza","family":"Ahmadian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2021,1,20]]},"reference":[{"key":"pmbabca07bib1","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1109\/34.993558","type":"journal-article","article-title":"Shape matching and object recognition using shape contexts","volume":"24","author":"Belongie","year":"2002","journal-title":"IEEE Trans. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2020-04-16","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2020-11-12","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2021-01-20","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}