{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T04:46:38Z","timestamp":1793853998059,"version":"4.6.1"},"reference-count":105,"publisher":"Springer Science and Business Media LLC","issue":"8","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:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T00:00:00Z","timestamp":1685404800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s11517-023-02842-x","type":"journal-article","created":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T19:02:15Z","timestamp":1685386935000},"page":"1901-1927","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A systematic review of the techniques for automatic segmentation of the human upper airway using volumetric images"],"prefix":"10.1007","volume":"61","author":[{"given":"Payal","family":"Maken","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8592-9964","authenticated-orcid":false,"given":"Abhishek","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manoj Kumar","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,30]]},"reference":[{"key":"2842_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-007-4488-2_2","author":"J Tu","year":"2013","unstructured":"Tu J, Inthavong K, Ahmadi G (2013) The human respiratory system Springer, Dordrecht. Biol Med Phys, Biomed Eng. https:\/\/doi.org\/10.1007\/978-94-007-4488-2_2","journal-title":"Biol Med Phys, Biomed Eng"},{"key":"2842_CR2","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1016\/j.joms.2010.11.037","volume":"69","author":"Z Abramson","year":"2011","unstructured":"Abramson Z, Susarla SM, Lawler M, Bouchard C, Troulis M, Kaban LB (2011) Three-dimensional computed tomographic airway analysis of patients with obstructive sleep apnea treated by maxillomandibular advancement. J Oral Maxillofac Surg 69:677\u2013686. https:\/\/doi.org\/10.1016\/j.joms.2010.11.037","journal-title":"J Oral Maxillofac Surg"},{"key":"2842_CR3","doi-asserted-by":"publisher","unstructured":"Luepker RV, Lakshminarayan K (2011) Cardiovascular and cerebrovascular diseases, in: Roger Detels, Robert Beaglehole, Mary Ann Lansang, a., Gulliford, M. (Eds.), Oxford textbook of public health (5 ed.). Oxford University Press, United Kingdom https:\/\/doi.org\/10.1093\/med\/9780199218707.003.0059","DOI":"10.1093\/med\/9780199218707.003.0059"},{"key":"2842_CR4","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.smrv.2016.01.007","volume":"31","author":"BC Neelapu","year":"2017","unstructured":"Neelapu BC, Kharbanda OP, Sardana HK, Balachandran R, Sardana V, Kapoor P, Gupta A, Vasamsetti S (2017) Craniofacial and upper airway morphology in adult obstructive sleep apnea patients: a systematic review and meta-analysis of cephalometric studies. Sleep Med Rev 31:79\u201390. https:\/\/doi.org\/10.1016\/j.smrv.2016.01.007","journal-title":"Sleep Med Rev"},{"key":"2842_CR5","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1080\/17476348.2017.1360769","volume":"11","author":"MS Eapen","year":"2017","unstructured":"Eapen MS, Myers S, Walters EH, Sohal SS (2017) Airway inflammation in chronic obstructive pulmonary disease (COPD): a true paradox. Expert Rev Respir Med 11:827\u2013839. https:\/\/doi.org\/10.1080\/17476348.2017.1360769","journal-title":"Expert Rev Respir Med"},{"key":"2842_CR6","unstructured":"SleepApnea (2020) https:\/\/my.clevelandclinic.org\/health\/diseases\/8718-sleep-apnea. 3 March 2020"},{"key":"2842_CR7","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.ejim.2020.02.023","volume":"75","author":"C Sorino","year":"2020","unstructured":"Sorino C, Negri S, Spanevello A, Visca D, Scichilone N (2020) Inhalation therapy devices for the treatment of obstructive lung diseases: the history of inhalers towards the ideal inhaler. Eur J Intern Med 75:15\u201318. https:\/\/doi.org\/10.1016\/j.ejim.2020.02.023","journal-title":"Eur J Intern Med"},{"key":"2842_CR8","doi-asserted-by":"publisher","first-page":"196","DOI":"10.4236\/ojmi.2017.74019","volume":"07","author":"N Kabaliuk","year":"2017","unstructured":"Kabaliuk N, Nejati A, Loch C, Schwass D, Cater JE, Jermy MC (2017) Strategies for segmenting the upper airway in cone-beam computed tomography (CBCT) data. Open J MedImaging 07:196\u2013219. https:\/\/doi.org\/10.4236\/ojmi.2017.74019","journal-title":"Open J MedImaging"},{"key":"2842_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-007-4488-2","volume-title":"Computational fluid and particle dynamics in the human respiratory system","author":"JY Tu","year":"2013","unstructured":"Tu JY, Inthavong K, Ahmadi G (2013) Computational fluid and particle dynamics in the human respiratory system. Springer, Netherlands. https:\/\/doi.org\/10.1007\/978-94-007-4488-2"},{"key":"2842_CR10","doi-asserted-by":"publisher","unstructured":"Omer A, Yousef M, Mohammed A, Omer, Zidan M, Abbas W (2020) Volumetric analysis of the paranasal sinuses using CT among chronic sinusitis conditions. Int J Sci Res (IJSR) 9. https:\/\/doi.org\/10.21275\/SR20927154620","DOI":"10.21275\/SR20927154620"},{"key":"2842_CR11","doi-asserted-by":"publisher","first-page":"1269","DOI":"10.1007\/s11548-014-1134-5","volume":"10","author":"NL Bui","year":"2015","unstructured":"Bui NL, Ong SH, Foong KW (2015) Automatic segmentation of the nasal cavity and paranasal sinuses from cone-beam CT images. Int J Comput Assist Radiol Surg 10:1269\u20131277. https:\/\/doi.org\/10.1007\/s11548-014-1134-5","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"2842_CR12","doi-asserted-by":"publisher","first-page":"1877","DOI":"10.1007\/s11548-017-1650-1","volume":"12","author":"BC Neelapu","year":"2017","unstructured":"Neelapu BC, Kharbanda OP, Sardana V, Gupta A, Vasamsetti S, Balachandran R, Rana SS, Sardana HK (2017) A pilot study for segmentation of pharyngeal and sino-nasal airway subregions by automatic contour initialization. Int J Comput Assist Radiol Surg 12:1877\u20131893. https:\/\/doi.org\/10.1007\/s11548-017-1650-1","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"2842_CR13","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1016\/j.oooo.2017.08.020","volume":"124","author":"BC Neelapu","year":"2017","unstructured":"Neelapu BC, Kharbanda OP, Sardana HK, Gupta A, Vasamsetti S, Balachandran R, Rana SS, Sardana V (2017) The reliability of different methods of manual volumetric segmentation of pharyngeal and sinonasal subregions. Oral Surg Oral Med Oral Pathol Oral Radiol 124:577\u2013587. https:\/\/doi.org\/10.1016\/j.oooo.2017.08.020","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radiol"},{"key":"2842_CR14","doi-asserted-by":"publisher","first-page":"45706","DOI":"10.1038\/srep45706","volume":"7","author":"Z Zheng","year":"2017","unstructured":"Zheng Z, Liu H, Xu Q, Wu W, Du L, Chen H, Zhang Y, Liu D (2017) Computational fluid dynamics simulation of the upper airway response to large incisor retraction in adult class I bimaxillary protrusion patients. Sci Rep 7:45706. https:\/\/doi.org\/10.1038\/srep45706","journal-title":"Sci Rep"},{"key":"2842_CR15","doi-asserted-by":"publisher","first-page":"248","DOI":"10.7599\/hmr.2016.36.4.248","volume":"36","author":"B-J Yi","year":"2016","unstructured":"Yi B-J, Yoon H-S (2016) Review of computer-aided sinus surgery. Hanyang Medical Reviews 36:248. https:\/\/doi.org\/10.7599\/hmr.2016.36.4.248","journal-title":"Hanyang Medical Reviews"},{"key":"2842_CR16","unstructured":"Sieroslawska A. Ken Hub, Paranasal sinuses. https:\/\/www.kenhub.com\/en\/library\/anatomy\/the-paranasal-sinuses. September 30, 2021"},{"key":"2842_CR17","unstructured":"Norton NSNFH, Elsevier\/Saunders (2012) Netter\u2019s head and neck anatomy for dentistry. http:\/\/www.clinicalkey.com\/dura\/browse\/bookChapter\/3-s2.0-C20110041741"},{"key":"2842_CR18","doi-asserted-by":"publisher","first-page":"387","DOI":"10.7494\/csci.2019.20.4.3163","volume":"20","author":"A Gupta","year":"2019","unstructured":"Gupta A (2019) Current research opportunities of image processing and computer vision. Comput Sci 20:387\u2013410. https:\/\/doi.org\/10.7494\/csci.2019.20.4.3163","journal-title":"Comput Sci"},{"key":"2842_CR19","doi-asserted-by":"publisher","unstructured":"Piva A (2013) An overview on image forensics. ISRN Signal Process 2013. https:\/\/doi.org\/10.1155\/2013\/496701","DOI":"10.1155\/2013\/496701"},{"key":"2842_CR20","doi-asserted-by":"publisher","first-page":"51","DOI":"10.2478\/cait-2019-0015","volume":"19","author":"P Maken","year":"2019","unstructured":"Maken P, Gupta A, Gupta MK (2019) A study on various techniques involved in gender prediction system: a comprehensive review. Cybernet Inf Technol 19:51\u201373. https:\/\/doi.org\/10.2478\/cait-2019-0015","journal-title":"Cybernet Inf Technol"},{"key":"2842_CR21","doi-asserted-by":"publisher","first-page":"49","DOI":"10.14257\/ijmue.2007.2.2.04","volume":"2","author":"B Kang","year":"2007","unstructured":"Kang B (2007) A review on image & video processing. Int J Multimed Ubiquit Eng 2:49\u201364. https:\/\/doi.org\/10.14257\/ijmue.2007.2.2.04","journal-title":"Int J Multimed Ubiquit Eng"},{"key":"2842_CR22","doi-asserted-by":"publisher","unstructured":"Maken P (2019) An elementary study on various techniques involved in face recognition systems : a review. Int J Sci Res Comput Sci, Eng Inf Technol 519\u2013525. https:\/\/doi.org\/10.32628\/cseit1951116","DOI":"10.32628\/cseit1951116"},{"key":"2842_CR23","doi-asserted-by":"publisher","first-page":"24573","DOI":"10.1007\/s11042-021-10837-9","volume":"80","author":"P Maken","year":"2021","unstructured":"Maken P, Gupta A (2021) A method for automatic classification of gender based on text- independent handwriting. Multimed Tools Appl 80:24573\u201324602. https:\/\/doi.org\/10.1007\/s11042-021-10837-9","journal-title":"Multimed Tools Appl"},{"key":"2842_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2021.10.006","author":"M Pandey","year":"2021","unstructured":"Pandey M, Gupta A (2021) A systematic review of the automatic kidney segmentation methods in abdominal images. Biocybernetics Biomed Eng. https:\/\/doi.org\/10.1016\/j.bbe.2021.10.006","journal-title":"Biocybernetics Biomed Eng"},{"key":"2842_CR25","doi-asserted-by":"publisher","unstructured":"Tuia D, Camps-Valls G (2009) Recent advances in remote sensing image processing.https:\/\/doi.org\/10.1109\/ICIP.2009.5414281","DOI":"10.1109\/ICIP.2009.5414281"},{"key":"2842_CR26","first-page":"64","volume":"7","author":"M Kumar","year":"2015","unstructured":"Kumar M, Shanavas M, Sidappa A, Kiran M (2015) Cone beam computed tomography - know its secrets. J Int Oral Health 7:64\u201368","journal-title":"J Int Oral Health"},{"key":"2842_CR27","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/s10029-017-1725-5","volume":"22","author":"LF Kroese","year":"2018","unstructured":"Kroese LF, Sneiders D, Kleinrensink GJ, Muysoms F, Lange JF (2018) Comparing different modalities for the diagnosis of incisional hernia: a systematic review. Hernia 22:229\u2013242. https:\/\/doi.org\/10.1007\/s10029-017-1725-5","journal-title":"Hernia"},{"key":"2842_CR28","first-page":"37","volume":"4","author":"H Kasban","year":"2015","unstructured":"Kasban H, El-bendary M, Salama D (2015) A comparative study of medical imaging techniques. Int J Inf Sci Intell Syst 4:37\u201358","journal-title":"Int J Inf Sci Intell Syst"},{"key":"2842_CR29","doi-asserted-by":"publisher","first-page":"3753","DOI":"10.1038\/s41598-020-60520-6","volume":"10","author":"Q Yang","year":"2020","unstructured":"Yang Q, Li N, Zhao Z, Fan X, Chang EIC, Xu Y (2020) MRI cross-modality image-to-image translation. Sci Rep 10:3753. https:\/\/doi.org\/10.1038\/s41598-020-60520-6","journal-title":"Sci Rep"},{"key":"2842_CR30","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1146\/annurev-bioeng-071114-040723","volume":"17","author":"JJ Vaquero","year":"2015","unstructured":"Vaquero JJ, Kinahan P (2015) Positron emission tomography: current challenges and opportunities for technological advances in clinical and preclinical imaging systems. Annu Rev Biomed Eng 17:385\u2013414. https:\/\/doi.org\/10.1146\/annurev-bioeng-071114-040723","journal-title":"Annu Rev Biomed Eng"},{"key":"2842_CR31","doi-asserted-by":"publisher","first-page":"1737","DOI":"10.1007\/s11548-015-1173-6","volume":"10","author":"A Gupta","year":"2015","unstructured":"Gupta A, Kharbanda OP, Sardana V, Balachandran R, Sardana HK (2015) A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images. Int J Comput Assist Radiol Surg 10:1737\u20131752. https:\/\/doi.org\/10.1007\/s11548-015-1173-6","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"2842_CR32","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1007\/s11548-015-1334-7","volume":"11","author":"A Gupta","year":"2016","unstructured":"Gupta A, Kharbanda OP, Sardana V, Balachandran R, Sardana HK (2016) Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm. Int J Comput Assist Radiol Surg 11:1297\u20131309. https:\/\/doi.org\/10.1007\/s11548-015-1334-7","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"2842_CR33","unstructured":"Gupta A, Sardana H K, Kharbanda O P, Sardana V (2016) Method for automatic detection of anatomical landmarks in volumetric data Council of Scientific and Industrial Research, National Informatics Centre Department of Electronics and Information Technology, US Patent.\u00a0https:\/\/patents.google.com\/patent\/US20160203604A1\/en"},{"key":"2842_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1259\/dmfr.20170054","volume":"47","author":"BC Neelapu","year":"2018","unstructured":"Neelapu BC, Kharbanda OP, Sardana V, Gupta A, Vasamsetti S, Balachandran R, Sardana HK (2018) Automatic localization of three-dimensional cephalometric landmarks on CBCT images by extracting symmetry features of the skull. Dentomaxillofac Radiol 47:1\u201312. https:\/\/doi.org\/10.1259\/dmfr.20170054","journal-title":"Dentomaxillofac Radiol"},{"key":"2842_CR35","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.ajodo.2016.06.027","volume":"151","author":"A Gupta","year":"2017","unstructured":"Gupta A, Kharbanda O, Balachandran R, Sardana V, Kalra S, Chaurasia S, Sardana H (2017) Precision of manual landmark identification between as-received and oriented volume-rendered cone-beam computed tomography images. Am J Orthod Dentofac Orthop 151:118\u2013131. https:\/\/doi.org\/10.1016\/j.ajodo.2016.06.027","journal-title":"Am J Orthod Dentofac Orthop"},{"key":"2842_CR36","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-021-11609-1","author":"A Gupta","year":"2022","unstructured":"Gupta A (2022) RegCal: registration-based calibration method to perform linear measurements on PA (posteroanterior) cephalogram- a pilot study. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-021-11609-1","journal-title":"Multimed Tools Appl"},{"key":"2842_CR37","unstructured":"Neelapu BC, Kumar H, Sardana, Kharbanda OP, Sardana V, Gupta A, Vasamsetti S (n.d.) Method and system for automatic volumetric-segmentation of human upper respiratory tract. COUNCIL OF SCIENTIFIC & INDUSTRIAL RESEARCH (IN), US Patent.\u00a0https:\/\/patents.google.com\/patent\/US20190066303A1\/en"},{"key":"2842_CR38","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.1016\/j.ijom.2011.06.017","volume":"40","author":"R Guijarro-Martinez","year":"2011","unstructured":"Guijarro-Martinez R, Swennen GR (2011) Cone-beam computerized tomography imaging and analysis of the upper airway: a systematic review of the literature. Int J Oral Maxillofac Surg 40:1227\u20131237. https:\/\/doi.org\/10.1016\/j.ijom.2011.06.017","journal-title":"Int J Oral Maxillofac Surg"},{"key":"2842_CR39","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1259\/dmfr\/79433138","volume":"41","author":"NA Alsufyani","year":"2012","unstructured":"Alsufyani NA, Flores-Mir C, Major PW (2012) Three-dimensional segmentation of the upper airway using cone beam CT: a systematic review. Dentomaxillofac Radiol 41:276\u2013284. https:\/\/doi.org\/10.1259\/dmfr\/79433138","journal-title":"Dentomaxillofac Radiol"},{"key":"2842_CR40","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.21608\/edj.2020.25972.1074","volume":"66","author":"S El Khateeb","year":"2020","unstructured":"El Khateeb S (2020) Three-dimensional image segmentation of upper airway by cone beam CT: a review of literature. Egypt Dental J 66:1527\u20131535. https:\/\/doi.org\/10.21608\/edj.2020.25972.1074","journal-title":"Egypt Dental J"},{"key":"2842_CR41","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1186\/s13643-021-01626-4","volume":"10","author":"MJ Page","year":"2021","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hrobjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev 10:89. https:\/\/doi.org\/10.1186\/s13643-021-01626-4","journal-title":"Syst Rev"},{"key":"2842_CR42","doi-asserted-by":"publisher","unstructured":"Dastidar P, Heinonen T, Numminen J, Rautiainen M, Laasonen E (1999) Semi-automatic segmentation of computed tomographic images in volumetric estimation of nasal airway. Eur Arch Oto-Rhino-Laryngology: Off J Eur Federation Oto-Rhino-Laryngological Soc (EUFOS): Affiliated German Soc Oto-Rhino-Laryngology-Head Neck Surg 256:192\u2013198. https:\/\/doi.org\/10.1007\/s004050050138","DOI":"10.1007\/s004050050138"},{"key":"2842_CR43","doi-asserted-by":"publisher","unstructured":"Iwamoto Y, Xiong K, Kitamura T, Han XH, Matsushiro N, Nishimura H, Chen YW (2019) Automatic segmentation of the paranasal sinus from computer tomography images using a probabilistic atlas and a fully convolutional network, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2789\u20132792 https:\/\/doi.org\/10.1109\/EMBC.2019.8856703","DOI":"10.1109\/EMBC.2019.8856703"},{"key":"2842_CR44","first-page":"17","volume":"4","author":"IE Igbinosa","year":"2014","unstructured":"Igbinosa IE (2014) Automated Tool For The Extraction Of Healthy Sinus Area. Int J Eng Appl Sci 4:17\u201328","journal-title":"Int J Eng Appl Sci"},{"key":"2842_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/5689301","volume":"2020","author":"Z Deng","year":"2020","unstructured":"Deng Z, Wang B, Zhu Z (2020) BE-FNet: 3D bounding box estimation feature pyramid network for accurate and efficient maxillary sinus segmentation. Math Probl Eng 2020:1\u201316. https:\/\/doi.org\/10.1155\/2020\/5689301","journal-title":"Math Probl Eng"},{"key":"2842_CR46","doi-asserted-by":"publisher","first-page":"1457","DOI":"10.1007\/s11548-020-02228-6","volume":"15","author":"J Xu","year":"2020","unstructured":"Xu J, Wang S, Zhou Z, Liu J, Jiang X, Chen X (2020) Automatic CT image segmentation of maxillary sinus based on VGG network and improved V-Net. Int J Comput Assist Radiol Surg 15:1457\u20131465. https:\/\/doi.org\/10.1007\/s11548-020-02228-6","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"2842_CR47","doi-asserted-by":"publisher","unstructured":"Milletari F, Navab N, Ahmadi S-AJFICoDV (2016) V-Net: fully convolutional neural networks for volumetric medical image segmentation. 565\u2013571.\u00a0https:\/\/doi.org\/10.1109\/3DV.2016.79","DOI":"10.1109\/3DV.2016.79"},{"key":"2842_CR48","doi-asserted-by":"publisher","unstructured":"Liu M, Luo H, Tan Y, Wang X, Chen W (2018) Improved V-Net based image segmentation for 3D neuron reconstruction, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 443\u2013448 https:\/\/doi.org\/10.1109\/BIBM.2018.8621126","DOI":"10.1109\/BIBM.2018.8621126"},{"key":"2842_CR49","doi-asserted-by":"publisher","unstructured":"Kuo C-F J, Leu Y-S, Hu D-J, Huang C-C, Siao J-J, Leon K B P, (2020) Application of intelligent automatic segmentation and 3D reconstruction of inferior turbinate and maxillary sinus from computed tomography and analyze the relationship between volume and nasal lesion. Biomed Signal Process Control 57. https:\/\/doi.org\/10.1016\/j.bspc.2019.101660","DOI":"10.1016\/j.bspc.2019.101660"},{"key":"2842_CR50","doi-asserted-by":"publisher","unstructured":"Li KR, Hsung T-C, Yeung AWK, Bornstein M (2020) On segmentation of maxillary sinus membrane using automatic vertex screening, pp. 108\u2013111 https:\/\/doi.org\/10.1109\/VCIP49819.2020.9301845","DOI":"10.1109\/VCIP49819.2020.9301845"},{"key":"2842_CR51","doi-asserted-by":"publisher","unstructured":"Gharieb RR, Gendy G, Abdelfattah A (2016) Image segmentation using fuzzy C-means algorithm incorporating weighted local complement membership and local data distances, 2016 World Symposium on Computer Applications & Research (WSCAR), pp. 6\u201311 https:\/\/doi.org\/10.1109\/WSCAR.2016.18","DOI":"10.1109\/WSCAR.2016.18"},{"key":"2842_CR52","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1109\/TBME.2017.2720184","volume":"65","author":"TC Hsung","year":"2018","unstructured":"Hsung TC, Lo J, Chong MM, Goto TK, Cheung LK (2018) Orbit segmentation by surface reconstruction with automatic sliced vertex screening. IEEE Trans Biomed Eng 65:828\u2013838. https:\/\/doi.org\/10.1109\/TBME.2017.2720184","journal-title":"IEEE Trans Biomed Eng"},{"key":"2842_CR53","doi-asserted-by":"publisher","unstructured":"Xiong K, Kitamura T, Iwamoto Y, Han X, Matsushiro N, Nishimura H, Chen Y (2018) Semi-automatic segmentation of paranasal sinus from CT images using fully convolutional networks, 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE), pp. 268\u2013269 https:\/\/doi.org\/10.1109\/GCCE.2018.8574753","DOI":"10.1109\/GCCE.2018.8574753"},{"key":"2842_CR54","doi-asserted-by":"publisher","unstructured":"Deng Z, Kitamura T, Matsushiro N, Nishimura H, Zhu Z, Xu J, Xiong K, and Chen Y-W (2018) Semi-automatic segmentation of paranasal sinuses from CT images using active contour with group similarity constraintshttps:\/\/doi.org\/10.1007\/978-3-319-59397-5","DOI":"10.1007\/978-3-319-59397-5"},{"key":"2842_CR55","doi-asserted-by":"publisher","unstructured":"Park J, Hwang J, Ryu J, Nam I, Kim S-A, Cho B-H, Shin S-H, Lee J-Y (2021) Deep learning based airway segmentation using key point prediction. Appl Sci 11. https:\/\/doi.org\/10.3390\/app11083501","DOI":"10.3390\/app11083501"},{"key":"2842_CR56","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1016\/j.ajodo.2020.05.017","volume":"159","author":"R Leonardi","year":"2021","unstructured":"Leonardi R, Lo Giudice A, Farronato M, Ronsivalle V, Allegrini S, Musumeci G, Spampinato C (2021) Fully automatic segmentation of sinonasal cavity and pharyngeal airway based on convolutional neural networks. Am J Orthod Dentofacial Orthop 159:824\u2013835. https:\/\/doi.org\/10.1016\/j.ajodo.2020.05.017","journal-title":"Am J Orthod Dentofacial Orthop"},{"key":"2842_CR57","doi-asserted-by":"publisher","DOI":"10.1111\/ocr.12480","author":"C Sin","year":"2021","unstructured":"Sin C, Akkaya N, Aksoy S, Orhan K, Oz U (2021) A deep learning algorithm proposal to automatic pharyngeal airway detection and segmentation on CBCT images. Orthod Craniofac Res. https:\/\/doi.org\/10.1111\/ocr.12480","journal-title":"Orthod Craniofac Res"},{"key":"2842_CR58","doi-asserted-by":"publisher","first-page":"e6246","DOI":"10.7717\/peerj.6246","volume":"7","author":"C Zhang","year":"2019","unstructured":"Zhang C, Bruggink R, Baan F, Bronkhorst E, Maal T, He H, Ongkosuwito EM (2019) A new segmentation algorithm for measuring CBCT images of nasal airway: a pilot study. PeerJ 7:e6246. https:\/\/doi.org\/10.7717\/peerj.6246","journal-title":"PeerJ"},{"key":"2842_CR59","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.compbiomed.2018.12.008","volume":"105","author":"W Keustermans","year":"2019","unstructured":"Keustermans W, Huysmans T, Schmelzer B, Sijbers J, Dirckx JJ (2019) Matlab((R)) toolbox for semi-automatic segmentation of the human nasal cavity based on active shape modeling. Comput Biol Med 105:27\u201338. https:\/\/doi.org\/10.1016\/j.compbiomed.2018.12.008","journal-title":"Comput Biol Med"},{"key":"2842_CR60","doi-asserted-by":"crossref","unstructured":"Xiong K, Kitamura T, Iwamoto Y, Han X, Matsushiro N, Nishimura H, Chen YJItGCoCE (2018) Semi-automatic segmentation of paranasal sinus from CT images using fully convolutional networks. 268\u2013269","DOI":"10.1109\/GCCE.2018.8574753"},{"key":"2842_CR61","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1016\/j.ajodo.2010.12.019","volume":"140","author":"S Stratemann","year":"2011","unstructured":"Stratemann S, Huang JC, Maki K, Hatcher D, Miller AJ (2011) Three-dimensional analysis of the airway with cone-beam computed tomography. Am J Orthod Dentofacial Orthop 140:607\u2013615. https:\/\/doi.org\/10.1016\/j.ajodo.2010.12.019","journal-title":"Am J Orthod Dentofacial Orthop"},{"key":"2842_CR62","doi-asserted-by":"publisher","first-page":"3","DOI":"10.4103\/0971-6203.58777","volume":"35","author":"N Sharma","year":"2010","unstructured":"Sharma N, Aggarwal LM (2010) Automated medical image segmentation techniques. J Med Phys 35:3\u201314. https:\/\/doi.org\/10.4103\/0971-6203.58777","journal-title":"J Med Phys"},{"key":"2842_CR63","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.neucom.2015.12.073","volume":"186","author":"S Zhou","year":"2016","unstructured":"Zhou S, Wang J, Zhang S, Liang Y, Gong Y (2016) Active contour model based on local and global intensity information for medical image segmentation. Neurocomputing 186:107\u2013118. https:\/\/doi.org\/10.1016\/j.neucom.2015.12.073","journal-title":"Neurocomputing"},{"key":"2842_CR64","doi-asserted-by":"publisher","unstructured":"Qing C, Liu H, Qian Y, Li J, Duan X, Yang Y-H (2018) Local and global active contour model for image segmentation with intensity inhomogeneity. IEEE Access PP 1\u20131. https:\/\/doi.org\/10.1109\/ACCESS.2018.2871846","DOI":"10.1109\/ACCESS.2018.2871846"},{"key":"2842_CR65","doi-asserted-by":"publisher","unstructured":"Park J-E, Bae S-H, Choi Y-J, Choi W-C, Kim H-W, Lee U-L (2017) The structural changes of pharyngeal airway contributing to snoring after orthognathic surgery in skeletal class III patients. Maxillofacial Plastic and Reconstructive Surgery 39. https:\/\/doi.org\/10.1186\/s40902-017-0120-6","DOI":"10.1186\/s40902-017-0120-6"},{"key":"2842_CR66","doi-asserted-by":"publisher","first-page":"79","DOI":"10.5624\/isd.2011.41.2.79","volume":"41","author":"T-Y Kim","year":"2011","unstructured":"Kim T-Y, Baik J-S, Park J-Y, Chae H-S, Huh K-H, Choi S-C (2011) Determination of midsagittal plane for evaluation of facial asymmetry using three-dimensional computed tomography. Imaging Sci Dent 41:79\u201384. https:\/\/doi.org\/10.5624\/isd.2011.41.2.79","journal-title":"Imaging Sci Dent"},{"key":"2842_CR67","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1109\/42.918469","volume":"20","author":"Y Liu","year":"2001","unstructured":"Liu Y, Collins R, Rothfus W (2001) Robust midsagittal plane extraction from normal and pathological 3-D neuroradiology images. Med Imaging, IEEE Trans 20:175\u2013192. https:\/\/doi.org\/10.1109\/42.918469","journal-title":"Med Imaging, IEEE Trans"},{"key":"2842_CR68","doi-asserted-by":"publisher","unstructured":"Mancas M, Gosselin B, Macq B (2006) Segmentation using a region growing thresholdinghttps:\/\/doi.org\/10.1117\/12.587995","DOI":"10.1117\/12.587995"},{"key":"2842_CR69","doi-asserted-by":"publisher","first-page":"487","DOI":"10.15274\/NRJ-2014-10067","volume":"27","author":"S Salerno","year":"2014","unstructured":"Salerno S, Gagliardo C, Vitabile S, La Militello C, Tona G, Giuffre M, Lo Casto A, Midiri M (2014) Semi-automatic volumetric segmentation of the upper airways in patients with pierre robin sequence. Neuroradiol J 27:487\u2013494","journal-title":"Neuroradiol J"},{"key":"2842_CR70","doi-asserted-by":"publisher","unstructured":"Zou KH, Warfield SK, Bharatha A, Tempany CMC, Kaus MR, Haker SJ, Wells WM, 3rd, Jolesz FA, Kikinis R (2004) Statistical validation of image segmentation quality based on a spatial overlap index. Acad Radiol 11:178-189.https:\/\/doi.org\/10.1016\/s1076-6332(03)00671-8","DOI":"10.1016\/s1076-6332(03)00671-8"},{"issue":"Suppl 1","key":"2842_CR71","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1097\/SCS.0b013e318193d521","volume":"20","author":"Z Abramson","year":"2009","unstructured":"Abramson Z, Susarla S, Troulis M, Kaban L (2009) Age-related changes of the upper airway assessed by 3-dimensional computed tomography. J Craniofac Surg 20(Suppl 1):657\u2013663. https:\/\/doi.org\/10.1097\/SCS.0b013e318193d521","journal-title":"J Craniofac Surg"},{"key":"2842_CR72","doi-asserted-by":"publisher","first-page":"3245","DOI":"10.1007\/s11831-020-09497-z","volume":"28","author":"M Ashok","year":"2021","unstructured":"Ashok M, Gupta A (2021) A systematic review of the techniques for the automatic segmentation of organs-at-risk in thoracic computed tomography images. Arch Comput Methods Eng 28:3245\u20133267. https:\/\/doi.org\/10.1007\/s11831-020-09497-z","journal-title":"Arch Comput Methods Eng"},{"key":"2842_CR73","doi-asserted-by":"publisher","unstructured":"Ashok M, Gupta A (2021) Deep learning-based techniques for the automatic segmentation of organs in thoracic computed tomography images: a comparative study, 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), pp. 198\u2013202 https:\/\/doi.org\/10.1109\/ICAIS50930.2021.9396016","DOI":"10.1109\/ICAIS50930.2021.9396016"},{"key":"2842_CR74","doi-asserted-by":"publisher","unstructured":"Trivedi M, Gupta A (2021) Automatic monitoring of the growth of plants using deep learning-based leaf segmentation. Int J Appl Sci Eng\u00a018:2. https:\/\/doi.org\/10.6703\/IJASE.202106_18(2).003","DOI":"10.6703\/IJASE.202106_18(2).003"},{"key":"2842_CR75","doi-asserted-by":"publisher","unstructured":"Beksi W J, Papanikolopoulos N (2016) 3D region segmentation using topological persistence, 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1079\u20131084 https:\/\/doi.org\/10.1109\/IROS.2016.7759183","DOI":"10.1109\/IROS.2016.7759183"},{"key":"2842_CR76","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1016\/j.patcog.2009.08.002","volume":"43","author":"X-F Wang","year":"2010","unstructured":"Wang X-F, Huang D-S, Xu H (2010) An efficient local Chan-Vese model for image segmentation. Pattern Recogn 43:603\u2013618. https:\/\/doi.org\/10.1016\/j.patcog.2009.08.002","journal-title":"Pattern Recogn"},{"key":"2842_CR77","unstructured":"Li L, Ross P, Kruusmaa M (2013) Ultrasound image segmentation by Bhattacharyya distance with Rayleigh distribution. IEEE, 2013 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA).\u00a0Poznan, Poland, pp 149\u2013153"},{"key":"2842_CR78","doi-asserted-by":"publisher","unstructured":"Marginean R, Andreica A, Diosan L, B\u00e1lint Z (2019) Autonomous image segmentation by competitive unsupervised GrowCut, 2019 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pp. 313\u2013319 https:\/\/doi.org\/10.1109\/SYNASC49474.2019.00051","DOI":"10.1109\/SYNASC49474.2019.00051"},{"key":"2842_CR79","doi-asserted-by":"publisher","unstructured":"Ghosh P, Antani SK, Long LR, Thoma GR (2011) Unsupervised grow-cut: cellular automata-based medical image segmentation, 2011 IEEE First International Conference on Healthcare Informatics, Imaging and Systems Biology, pp. 40\u201347 https:\/\/doi.org\/10.1109\/HISB.2011.44","DOI":"10.1109\/HISB.2011.44"},{"key":"2842_CR80","first-page":"345","volume":"27","author":"M Liang","year":"2011","unstructured":"Liang M, Yueju X, De-yun K, Guoying L, Ke H, Qi-Fu L, Kai W (2011) Litchi image segmentation algorithm based on sparse field level set. Trans Chin Soc Agric Eng 27:345\u2013349","journal-title":"Trans Chin Soc Agric Eng"},{"key":"2842_CR81","doi-asserted-by":"publisher","unstructured":"Maynard RL, Downes N (2019) Chapter 10 - Nasal cavity, in: Maynard RL, Downes N (Eds.), Anatomy and histology of the laboratory rat in toxicology and biomedical research. Academic Press, pp. 109\u2013121 https:\/\/doi.org\/10.1016\/B978-0-12-811837-5.00010-1","DOI":"10.1016\/B978-0-12-811837-5.00010-1"},{"key":"2842_CR82","doi-asserted-by":"publisher","unstructured":"Huang R, Li A, Bi L, Li C, Young P, King G, Feng DD, Kim J (2016) A locally constrained statistical shape model for robust nasal cavity segmentation in computed tomography, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 1334\u20131337 https:\/\/doi.org\/10.1109\/ISBI.2016.7493513","DOI":"10.1109\/ISBI.2016.7493513"},{"key":"2842_CR83","doi-asserted-by":"publisher","first-page":"977","DOI":"10.1007\/s11548-013-0839-1","volume":"8","author":"V Tr\u00e9villot","year":"2013","unstructured":"Tr\u00e9villot V, Sobral R, Dombre E, Poignet P, Herman B, Crampette L (2013) Innovative endoscopic sino-nasal and anterior skull base robotics. Int J Comput Assist Radiol Surg 8:977\u2013987. https:\/\/doi.org\/10.1007\/s11548-013-0839-1","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"2842_CR84","doi-asserted-by":"publisher","unstructured":"Liu X, Song L, Liu S, Zhang Y (2021) A review of deep-learning-based medical image segmentation methods. Sustainability 13. https:\/\/doi.org\/10.3390\/su13031224","DOI":"10.3390\/su13031224"},{"key":"2842_CR85","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1054\/ijom.2002.0279","volume":"31","author":"T Muto","year":"2002","unstructured":"Muto T, Takeda S, Kanazawa M, Yamazaki A, Fujiwara Y, Mizoguchi I (2002) The effect of head posture on the pharyngeal airway space (PAS). Int J Oral Maxillofac Surg 31:579\u2013583. https:\/\/doi.org\/10.1054\/ijom.2002.0279","journal-title":"Int J Oral Maxillofac Surg"},{"key":"2842_CR86","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1259\/dmfr\/31005700","volume":"37","author":"P Sutthiprapaporn","year":"2008","unstructured":"Sutthiprapaporn P, Tanimoto K, Ohtsuka M, Nagasaki T, Iida Y, Katsumata A (2008) Positional changes of oropharyngeal structures due to gravity in the upright and supine positions. Dentomaxillofac Radiol 37:130\u2013135. https:\/\/doi.org\/10.1259\/dmfr\/31005700","journal-title":"Dentomaxillofac Radiol"},{"key":"2842_CR87","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1016\/j.procs.2015.09.027","volume":"65","author":"NM Zaitoun","year":"2015","unstructured":"Zaitoun NM, Aqel MJ (2015) Survey on image segmentation techniques. Proc Comput Sci 65:797\u2013806. https:\/\/doi.org\/10.1016\/j.procs.2015.09.027","journal-title":"Proc Comput Sci"},{"key":"2842_CR88","doi-asserted-by":"publisher","first-page":"28","DOI":"10.2749\/IJETTCS.361.944","volume":"4","author":"VY Borole","year":"2015","unstructured":"Borole VY, Nimbhore SS, Kawthekar DSS (2015) Image processing techniques for brain tumor detection\u202f: a review. Int J Emerg Trends Technol Comput Sci (IJETTCS) 4:28\u201332. https:\/\/doi.org\/10.2749\/IJETTCS.361.944","journal-title":"Int J Emerg Trends Technol Comput Sci (IJETTCS)"},{"key":"2842_CR89","doi-asserted-by":"publisher","unstructured":"Guo Y (2010) Computer-aided detection of breast cancer using ultrasound images, Computer Science. Utah State University, Logan, UT, ALL GRADUATE THESES AND DISSERTATIONS, pp. 2\u2013131 https:\/\/doi.org\/10.26076\/7f62-c23f","DOI":"10.26076\/7f62-c23f"},{"key":"2842_CR90","doi-asserted-by":"publisher","first-page":"168781401769294","DOI":"10.1177\/1687814017692947","volume":"9","author":"Z Wang","year":"2017","unstructured":"Wang Z, Liu Y-J (2017) Active contour model by combining edge and region information discrete dynamic systems. Adv Mech Eng 9:168781401769294. https:\/\/doi.org\/10.1177\/1687814017692947","journal-title":"Adv Mech Eng"},{"key":"2842_CR91","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.artmed.2018.08.008","volume":"95","author":"J Bernal","year":"2019","unstructured":"Bernal J, Kushibar K, Asfaw DS, Valverde S, Oliver A, Mart\u00ed R, Llad\u00f3 X (2019) Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review. Artif Intell Med 95:64\u201381. https:\/\/doi.org\/10.1016\/j.artmed.2018.08.008","journal-title":"Artif Intell Med"},{"key":"2842_CR92","doi-asserted-by":"publisher","first-page":"4489","DOI":"10.14419\/ijet.v7i4.19005","volume":"7","author":"M Jena","year":"2018","unstructured":"Jena M, Mishra S, Mishra D (2018) A survey on applications of machine learning techniques for medical image segmentation. Int J Eng Technol 7:4489\u20134495. https:\/\/doi.org\/10.14419\/ijet.v7i4.19005","journal-title":"Int J Eng Technol"},{"key":"2842_CR93","doi-asserted-by":"publisher","first-page":"290","DOI":"10.14429\/dsj.60.356","volume":"60","author":"D Chaudhuri","year":"2010","unstructured":"Chaudhuri D, Agrawal A (2010) Split-and-merge procedure for image segmentation using bimodality detection approach. Defence Sci J 60:290\u2013301. https:\/\/doi.org\/10.14429\/dsj.60.356","journal-title":"Defence Sci J"},{"key":"2842_CR94","doi-asserted-by":"publisher","unstructured":"Bach Cuadra M, Duay V, JP T (2015) Atlas-based segmentation, Paragios N, Duncan J, Ayache N (eds) Handbook of biomedical imaging. Springer, Boston, MA, pp. 221\u2013244 https:\/\/doi.org\/10.1007\/978-0-387-09749-7_12","DOI":"10.1007\/978-0-387-09749-7_12"},{"key":"2842_CR95","first-page":"423","volume-title":"Image post-processing and analysis diagnostic radiology physics: a handbook for teachers and students","author":"PA Yushkevich","year":"2014","unstructured":"Yushkevich PA (2014) Image post-processing and analysis diagnostic radiology physics: a handbook for teachers and students. IAEA, International Atomic Energy Agency (IAEA), pp 423\u2013456"},{"key":"2842_CR96","unstructured":"Nigretto J-C, medium.com (2021) Advantages of U-Net for image segmentation.\u00a0https:\/\/jean-charles-nigretto.medium.com\/advantages-of-u-net-for-image-segmentation-8ce869d28b4d.\u00a0Accessed 22 Jan 2021"},{"key":"2842_CR97","first-page":"301","volume-title":"V-Net and U-Net for ischemic stroke lesion segmentation in a small dataset of perfusion data","author":"GR Pinheiro","year":"2019","unstructured":"Pinheiro GR, Voltoline R, Bento M, Rittner L (2019) V-Net and U-Net for ischemic stroke lesion segmentation in a small dataset of perfusion data. Springer International Publishing, Cham, pp 301\u2013309"},{"key":"2842_CR98","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1148\/radiol.2271020198","volume":"227","author":"LF Donnelly","year":"2003","unstructured":"Donnelly LF, Surdulescu V, Chini BA, Casper KA, Poe SA, Amin RS (2003) Upper airway motion depicted at cine MR imaging performed during sleep: comparison between young patients with and those without obstructive sleep apnea. Radiology 227:239\u2013245. https:\/\/doi.org\/10.1148\/radiol.2271020198","journal-title":"Radiology"},{"key":"2842_CR99","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/s0034-5687(97)02529-2","volume":"108","author":"C Guilleminault","year":"1997","unstructured":"Guilleminault C, Hill MH, Simmons FB, Powell N, Riley R, Stoohs R (1997) Passive constriction of the upper airway during central apneas: fiberoptic and EMG investigations. Respir Physiol 108:11\u201322. https:\/\/doi.org\/10.1016\/s0034-5687(97)02529-2","journal-title":"Respir Physiol"},{"key":"2842_CR100","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1164\/rccm.200507-1148OC","volume":"173","author":"JJ Armstrong","year":"2006","unstructured":"Armstrong JJ, Leigh MS, Sampson DD, Walsh JH, Hillman DR, Eastwood PR (2006) Quantitative upper airway imaging with anatomic optical coherence tomography. Am J Respir Crit Care Med 173:226\u2013233. https:\/\/doi.org\/10.1164\/rccm.200507-1148OC","journal-title":"Am J Respir Crit Care Med"},{"key":"2842_CR101","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s11517-019-02050-6","volume":"58","author":"K Souadih","year":"2020","unstructured":"Souadih K, Belaid A, Ben Salem D, Conze PH (2020) Automatic forensic identification using 3D sphenoid sinus segmentation and deep characterization. Med Biol Eng Comput 58:291\u2013306. https:\/\/doi.org\/10.1007\/s11517-019-02050-6","journal-title":"Med Biol Eng Comput"},{"key":"2842_CR102","doi-asserted-by":"publisher","unstructured":"Sinha A, Leonard S, Reiter A, Ishii M, Taylor RH, Hager GD, (2016) Automatic segmentation and statistical shape modeling of the paranasal sinuses to estimate natural variations. Proc SPIE Int Soc Opt Eng 9784. https:\/\/doi.org\/10.1117\/12.2217337","DOI":"10.1117\/12.2217337"},{"key":"2842_CR103","doi-asserted-by":"publisher","first-page":"1894","DOI":"10.3390\/life12111894","volume":"12","author":"IA Tsolakis","year":"2022","unstructured":"Tsolakis IA, Kolokitha O-E, Papadopoulou E, Tsolakis AI, Kilipiris EG, Palomo JM (2022) Artificial intelligence as an aid in CBCT airway analysis: a systematic review. Life- MDPI 12:1894","journal-title":"Life- MDPI"},{"key":"2842_CR104","doi-asserted-by":"publisher","first-page":"103705","DOI":"10.1016\/j.jdent.2021.103705","volume":"111","author":"S Shujaat","year":"2021","unstructured":"Shujaat S, Jazil O, Willems H, Van Gerven A, Shaheen E, Politis C, Jacobs R (2021) Automatic segmentation of the pharyngeal airway space with convolutional neural network. J Dent 111:103705. https:\/\/doi.org\/10.1016\/j.jdent.2021.103705","journal-title":"J Dent"},{"key":"2842_CR105","doi-asserted-by":"publisher","first-page":"e53","DOI":"10.1016\/j.ajodo.2022.01.011","volume":"162","author":"H-N Cho","year":"2022","unstructured":"Cho H-N, Gwon E, Kim K-A, Baek S-H, Kim N, Kim S-J (2022) Accuracy of convolutional neural networks-based automatic segmentation of pharyngeal airway sections according to craniofacial skeletal pattern. Am J Orthod Dentofac Orthop 162:e53\u2013e62. https:\/\/doi.org\/10.1016\/j.ajodo.2022.01.011","journal-title":"Am J Orthod Dentofac Orthop"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02842-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-02842-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02842-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T23:28:03Z","timestamp":1689118083000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-02842-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,30]]},"references-count":105,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["2842"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-02842-x","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,30]]},"assertion":[{"value":"22 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}