{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:55:27Z","timestamp":1784739327683,"version":"3.55.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,3,25]],"date-time":"2023-03-25T00:00:00Z","timestamp":1679702400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,25]],"date-time":"2023-03-25T00:00:00Z","timestamp":1679702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Science and technology bureau of guanshanhu district","award":["No. 2021-01"],"award-info":[{"award-number":["No. 2021-01"]}]},{"name":"Science and technology bureau of guanshanhu district","award":["No. 2021-01"],"award-info":[{"award-number":["No. 2021-01"]}]},{"name":"Science and technology bureau of guanshanhu district","award":["No. 2021-01"],"award-info":[{"award-number":["No. 2021-01"]}]},{"name":"Science and technology bureau of guanshanhu district","award":["No. 2021-01"],"award-info":[{"award-number":["No. 2021-01"]}]},{"name":"Science and technology bureau of guanshanhu district","award":["No. 2021-01"],"award-info":[{"award-number":["No. 2021-01"]}]},{"name":"Science and technology bureau of guanshanhu district","award":["No. 2021-01"],"award-info":[{"award-number":["No. 2021-01"]}]},{"name":"Science and technology bureau of guanshanhu district","award":["No. 2021-01"],"award-info":[{"award-number":["No. 2021-01"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Although the morphological changes of sella turcica have been drawing increasing attention, the acquirement of linear parameters of sella turcica relies on manual measurement. Manual measurement is laborious, time-consuming, and may introduce subjective bias. This paper aims to develop and evaluate a deep learning-based model for automatic segmentation and measurement of sella turcica in cephalometric radiographs.\n<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>1129 images were used to develop a deep learning-based segmentation network for automatic sella turcica segmentation. Besides, 50 images were used to test the generalization ability of the model. The performance of the segmented network was evaluated by the dice coefficient. Images in the test datasets were segmented by the trained segmentation network, and the segmentation results were saved in binary images. Then the extremum points and corner points were detected by calling the function in the OpenCV library to obtain the coordinates of the four landmarks of the sella turcica. Finally, the length, diameter, and depth of the sella turcica can be obtained by calculating the distance between the two points and the distance from the point to the straight line. Meanwhile, images were measured manually using <jats:italic>Digimizer<\/jats:italic>. Intraclass correlation coefficients (ICCs) and Bland\u2013Altman plots were used to analyze the consistency between automatic and manual measurements to evaluate the reliability of the proposed methodology.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The dice coefficient of the segmentation network is 92.84%. For the measurement of sella turcica, there is excellent agreement between the automatic measurement and the manual measurement. In Test1, the ICCs of length, diameter and depth are 0.954, 0.953, and 0.912, respectively. In Test2, ICCs of length, diameter and depth are 0.906, 0.921, and 0.915, respectively. In addition, Bland\u2013Altman plots showed the excellent reliability of the automated measurement method, with the majority measurements differences falling within\u2009\u00b1\u20091.96 SDs intervals around the mean difference and no bias was apparent.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Our experimental results indicated that the proposed methodology could complete the automatic segmentation of the sella turcica efficiently, and reliably predict the length, diameter, and depth of the sella turcica. Moreover, the proposed method has generalization ability according to its excellent performance on Test2.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-023-00998-4","type":"journal-article","created":{"date-parts":[[2023,3,25]],"date-time":"2023-03-25T03:02:59Z","timestamp":1679713379000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Deep learning-based automatic sella turcica segmentation and morphology measurement in X-ray images"],"prefix":"10.1186","volume":"23","author":[{"given":"Qi","family":"Feng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ju-xiang","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi-jun","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong-chao","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,3,25]]},"reference":[{"issue":"9","key":"998_CR1","doi-asserted-by":"publisher","first-page":"977","DOI":"10.1007\/s00276-020-02481-z","volume":"42","author":"G Akay","year":"2020","unstructured":"Akay G, Eren I, Karadag O, Gungor K. Three-dimensional assessment of the sella turcica: comparison between cleft lip and palate patients and skeletal malocclusion classes. Surg Radiol Anat. 2020;42(9):977\u201383.","journal-title":"Surg Radiol Anat"},{"issue":"2","key":"998_CR2","doi-asserted-by":"publisher","first-page":"318","DOI":"10.5603\/FM.a2019.0092","volume":"79","author":"FK Muhammed","year":"2020","unstructured":"Muhammed FK, Abdullah AO, Liu Y. A morphometric study of the sella turcica: race, age, and gender effect. Folia Morphol (Warsz). 2020;79(2):318\u201326.","journal-title":"Folia Morphol (Warsz)"},{"issue":"5","key":"998_CR3","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1093\/ejo\/cjm048","volume":"29","author":"M Andredaki","year":"2007","unstructured":"Andredaki M, Koumantanou A, Dorotheou D, Halazonetis DJ. A cephalometric morphometric study of the sella turcica. Eur J Orthod. 2007;29(5):449\u201356.","journal-title":"Eur J Orthod"},{"issue":"2","key":"998_CR4","first-page":"172","volume":"31","author":"E Afzal","year":"2019","unstructured":"Afzal E, Fida M. Association between variations in sella turcica dimensions and morphology and skeletal malocclusions. J Ayub Med Coll Abbottabad. 2019;31(2):172\u20137.","journal-title":"J Ayub Med Coll Abbottabad"},{"issue":"3","key":"998_CR5","doi-asserted-by":"publisher","first-page":"543","DOI":"10.5603\/FM.a2018.0022","volume":"77","author":"G Magat","year":"2018","unstructured":"Magat G, Ozcan Sener S. Morphometric analysis of the sella turcica in Turkish individuals with different dentofacial skeletal patterns. Folia Morphol (Warsz). 2018;77(3):543\u201350.","journal-title":"Folia Morphol (Warsz)"},{"issue":"5","key":"998_CR6","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1007\/s00276-019-02406-5","volume":"42","author":"BT Silveira","year":"2020","unstructured":"Silveira BT, Fernandes KS, Trivino T, Dos Santos LYF, de Freitas CF. Assessment of the relationship between size, shape and volume of the sella turcica in class II and III patients prior to orthognathic surgery. Surg Radiol Anat. 2020;42(5):577\u201382.","journal-title":"Surg Radiol Anat"},{"issue":"7","key":"998_CR7","doi-asserted-by":"publisher","first-page":"2076","DOI":"10.1097\/SCS.0000000000005964","volume":"30","author":"FK Muhammed","year":"2019","unstructured":"Muhammed FK, Abdullah AO, Liu Y. Morphology, incidence of bridging, dimensions of sella turcica, and cephalometric standards in three different racial groups. J Craniofac Surg. 2019;30(7):2076\u201381.","journal-title":"J Craniofac Surg"},{"key":"998_CR8","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph18094456","author":"T Jankowski","year":"2021","unstructured":"Jankowski T, Jedlinski M, Grocholewicz K, Janiszewska-Olszowska J. Sella turcica morphology on cephalometric radiographs and dental abnormalities\u2014is there any association?\u2014Systematic review. Int J Environ Res Public Health. 2021. https:\/\/doi.org\/10.3390\/ijerph18094456.","journal-title":"Int J Environ Res Public Health"},{"issue":"3","key":"998_CR9","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1007\/s10266-021-00593-5","volume":"109","author":"N Canigur Bavbek","year":"2021","unstructured":"Canigur Bavbek N, Arslan Avan B. Morphometric evaluation of cranial base and sella turcica in patients with bilateral agenesis of maxillary lateral incisors. Odontology. 2021;109(3):701\u20139.","journal-title":"Odontology"},{"issue":"2","key":"998_CR10","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1111\/ocr.12426","volume":"24","author":"IA Roomaney","year":"2021","unstructured":"Roomaney IA, Chetty M. Sella turcica morphology in patients with genetic syndromes: a systematic review. Orthod Craniofac Res. 2021;24(2):194\u2013205.","journal-title":"Orthod Craniofac Res"},{"issue":"3","key":"998_CR11","first-page":"451","volume":"78","author":"FN Silverman","year":"1957","unstructured":"Silverman FN. Roentgen standards fo-size of the pituitary fossa from infancy through adolescence. Am J Roentgenol Radium Ther Nucl Med. 1957;78(3):451\u201360.","journal-title":"Am J Roentgenol Radium Ther Nucl Med"},{"issue":"9","key":"998_CR12","doi-asserted-by":"publisher","first-page":"4974","DOI":"10.1007\/s00330-020-06856-z","volume":"30","author":"Q Ye","year":"2020","unstructured":"Ye Q, Shen Q, Yang W, Huang S, Jiang Z, He L, Gong X. Development of automatic measurement for patellar height based on deep learning and knee radiographs. Eur Radiol. 2020;30(9):4974\u201384.","journal-title":"Eur Radiol"},{"issue":"1","key":"998_CR13","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1186\/s40644-021-00413-7","volume":"21","author":"M Woo","year":"2021","unstructured":"Woo M, Devane AM, Lowe SC, Lowther EL, Gimbel RW. Deep learning for semi-automated unidirectional measurement of lung tumor size in CT. Cancer Imaging. 2021;21(1):43.","journal-title":"Cancer Imaging"},{"key":"998_CR14","doi-asserted-by":"publisher","first-page":"7954333","DOI":"10.1155\/2022\/7954333","volume":"2022","author":"M Ahmad","year":"2022","unstructured":"Ahmad M, Qadri SF, Qadri S, Saeed IA, Zareen SS, Iqbal Z, Alabrah A, Alaghbari HM, Mizanur Rahman SM. A lightweight convolutional neural network model for liver segmentation in medical diagnosis. Comput Intell Neurosci. 2022;2022:7954333.","journal-title":"Comput Intell Neurosci"},{"issue":"4","key":"998_CR15","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1007\/s10278-021-00471-0","volume":"34","author":"DH Kim","year":"2021","unstructured":"Kim DH, Jeong JG, Kim YJ, Kim KG, Jeon JY. Automated vertebral segmentation and measurement of vertebral compression ratio based on deep learning in X-ray images. J Digit Imaging. 2021;34(4):853\u201361.","journal-title":"J Digit Imaging"},{"issue":"4","key":"998_CR16","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1016\/j.spinee.2019.11.010","volume":"20","author":"J Huang","year":"2020","unstructured":"Huang J, Shen H, Wu J, Hu X, Zhu Z, Lv X, Liu Y, Wang Y. Spine Explorer: a deep learning based fully automated program for efficient and reliable quantifications of the vertebrae and discs on sagittal lumbar spine MR images. Spine J. 2020;20(4):590\u20139.","journal-title":"Spine J"},{"key":"998_CR17","doi-asserted-by":"publisher","DOI":"10.1093\/ptj\/pzab041","author":"H Shen","year":"2021","unstructured":"Shen H, Huang J, Zheng Q, Zhu Z, Lv X, Liu Y, Wang Y. A deep-learning\u2013based, fully automated program to segment and quantify major spinal components on axial lumbar spine magnetic resonance images. Phys Ther. 2021. https:\/\/doi.org\/10.1093\/ptj\/pzab041.","journal-title":"Phys Ther"},{"issue":"10","key":"998_CR18","doi-asserted-by":"publisher","first-page":"1495","DOI":"10.1080\/02713683.2021.1908569","volume":"46","author":"J Cao","year":"2021","unstructured":"Cao J, Lou L, You K, Gao Z, Jin K, Shao J, Ye J. A novel automatic morphologic analysis of eyelids based on deep learning methods. Curr Eye Res. 2021;46(10):1495\u2013502.","journal-title":"Curr Eye Res"},{"issue":"1","key":"998_CR19","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1080\/07853890.2021.2009127","volume":"53","author":"L Lou","year":"2021","unstructured":"Lou L, Cao J, Wang Y, Gao Z, Jin K, Xu Z, Zhang Q, Huang X, Ye J. Deep learning-based image analysis for automated measurement of eyelid morphology before and after blepharoptosis surgery. Ann Med. 2021;53(1):2278\u201385.","journal-title":"Ann Med"},{"key":"998_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2020.109303","volume":"132","author":"W Yang","year":"2020","unstructured":"Yang W, Ye Q, Ming S, Hu X, Jiang Z, Shen Q, He L, Gong X. Feasibility of automatic measurements of hip joints based on pelvic radiography and a deep learning algorithm. Eur J Radiol. 2020;132: 109303.","journal-title":"Eur J Radiol"},{"key":"998_CR21","doi-asserted-by":"publisher","first-page":"20585","DOI":"10.1109\/ACCESS.2019.2896961","volume":"7","author":"M Ahmad","year":"2019","unstructured":"Ahmad M, Ai D, Xie G, Qadri SF, Song H, Huang Y, Wang Y, Yang J. Deep belief network modeling for automatic liver segmentation. IEEE Access. 2019;7:20585\u201395.","journal-title":"IEEE Access"},{"key":"998_CR22","doi-asserted-by":"publisher","first-page":"24273","DOI":"10.1109\/ACCESS.2021.3056516","volume":"9","author":"I Hirra","year":"2021","unstructured":"Hirra I, Ahmad M, Hussain A, Ashraf MU, Saeed IA, Qadri SF, Alghamdi AM, Alfakeeh AS. Breast cancer classification from histopathological images using patch-based deep learning modeling. IEEE Access. 2021;9:24273\u201387.","journal-title":"IEEE Access"},{"key":"998_CR23","doi-asserted-by":"publisher","DOI":"10.3390\/app9010069","author":"S Furqan Qadri","year":"2018","unstructured":"Furqan Qadri S, Ai D, Hu G, Ahmad M, Huang Y, Wang Y, Yang J. Automatic deep feature learning via patch-based deep belief network for vertebrae segmentation in CT images. Appl Sci. 2018. https:\/\/doi.org\/10.3390\/app9010069.","journal-title":"Appl Sci"},{"key":"998_CR24","doi-asserted-by":"publisher","first-page":"4189781","DOI":"10.1155\/2022\/4189781","volume":"2022","author":"XX Yin","year":"2022","unstructured":"Yin XX, Sun L, Fu Y, Lu R, Zhang Y. U-Net-Based medical image segmentation. J Healthc Eng. 2022;2022:4189781.","journal-title":"J Healthc Eng"},{"key":"998_CR25","doi-asserted-by":"crossref","unstructured":"Shamir RR, Duchin Y, Kim J, Sapiro G, Harel N. Continuous dice coefficient: a method for evaluating probabilistic segmentations. bioRxiv 2018:306977.","DOI":"10.1101\/306977"},{"key":"998_CR26","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.csda.2012.11.004","volume":"60","author":"C-C Chen","year":"2013","unstructured":"Chen C-C, Barnhart HX. Assessing agreement with intraclass correlation coefficient and concordance correlation coefficient for data with repeated measures. Comput Stat Data Anal. 2013;60:132\u201345.","journal-title":"Comput Stat Data Anal"},{"issue":"4","key":"998_CR27","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.tjem.2018.09.001","volume":"18","author":"N\u00d6 Do\u011fan","year":"2018","unstructured":"Do\u011fan N\u00d6. Bland-Altman analysis: a paradigm to understand correlation and agreement. Turk J Emerg Med. 2018;18(4):139\u201341.","journal-title":"Turk J Emerg Med"},{"key":"998_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/s11282-022-00629-8","author":"KS Shakya","year":"2022","unstructured":"Shakya KS, Laddi A, Jaiswal M. Automated methods for sella turcica segmentation on cephalometric radiographic data using deep learning (CNN) techniques. Oral Radiol. 2022. https:\/\/doi.org\/10.1007\/s11282-022-00629-8.","journal-title":"Oral Radiol"},{"issue":"5","key":"998_CR29","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1093\/ejo\/cjm049","volume":"29","author":"EA Alkofide","year":"2007","unstructured":"Alkofide EA. The shape and size of the sella turcica in skeletal Class I, Class II, and Class III Saudi subjects. Eur J Orthod. 2007;29(5):457\u201363.","journal-title":"Eur J Orthod"},{"issue":"6","key":"998_CR30","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1375\/twin.12.6.598","volume":"12","author":"MT Brock-Jacobsen","year":"2009","unstructured":"Brock-Jacobsen MT, Pallisgaard C, Kjaer I. The morphology of the sella turcica in monozygotic twins. Twin Res Hum Genet. 2009;12(6):598\u2013604.","journal-title":"Twin Res Hum Genet"},{"issue":"6","key":"998_CR31","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1016\/j.jormas.2018.05.005","volume":"119","author":"P Dasgupta","year":"2018","unstructured":"Dasgupta P, Sen S, Srikanth HS, Kamath G. Sella turcica bridging as a predictor of Class ii malocclusion\u2013an investigative study. J Stomatol Oral Maxillofac Surg. 2018;119(6):482\u20135.","journal-title":"J Stomatol Oral Maxillofac Surg"},{"key":"998_CR32","doi-asserted-by":"publisher","first-page":"6646406","DOI":"10.1155\/2021\/6646406","volume":"2021","author":"ST Chou","year":"2021","unstructured":"Chou ST, Chen CM, Chen PH, Chen YK, Chen SC, Tseng YC. Morphology of sella turcica and bridging prevalence correlated with sex and craniofacial skeletal pattern in Eastern Asia population: CBCT study. Biomed Res Int. 2021;2021:6646406.","journal-title":"Biomed Res Int"},{"issue":"5","key":"998_CR33","first-page":"571","volume":"10","author":"HP Sathyanarayana","year":"2013","unstructured":"Sathyanarayana HP, Kailasam V, Chitharanjan AB. Sella turcica-Its importance in orthodontics and craniofacial morphology. Dent Res J (Isfahan). 2013;10(5):571\u20135.","journal-title":"Dent Res J (Isfahan)"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-023-00998-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-023-00998-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-023-00998-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,25]],"date-time":"2023-03-25T03:03:06Z","timestamp":1679713386000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-023-00998-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,25]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["998"],"URL":"https:\/\/doi.org\/10.1186\/s12880-023-00998-4","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,25]]},"assertion":[{"value":"4 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 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 study was conducted in accordance with the Declaration of Helsinki. The Ethics Committee of Guiyang Hospital of Stomatology approved this retrospective study and waived the need for informed consent from patients (Approval No. GYSKLL-KY-20220317\u201301).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"41"}}