{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:51:07Z","timestamp":1786978267284,"version":"build-2736575974"},"reference-count":22,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T00:00:00Z","timestamp":1719100800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aao-hnsfjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Otolaryngol.--head neck surg."],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Segmentation, the partitioning of patient imaging into multiple, labeled segments, has several potential clinical benefits but when performed manually is tedious and resource intensive. Automated deep learning (DL)\u2010based segmentation methods can streamline the process. The objective of this study was to evaluate a label\u2010efficient DL pipeline that requires only a small number of annotated scans for semantic segmentation of sinonasal structures in CT scans.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>Retrospective cohort study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>Academic institution.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Forty CT scans were used in this study including 16 scans in which the nasal septum (NS), inferior turbinate (IT), maxillary sinus (MS), and optic nerve (ON) were manually annotated using an open\u2010source software. A label\u2010efficient DL framework was used to train jointly on a few manually labeled scans and the remaining unlabeled scans. Quantitative analysis was then performed to obtain the number of annotated scans needed to achieve submillimeter average surface distances (ASDs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our findings reveal that merely four labeled scans are necessary to achieve median submillimeter ASDs for large sinonasal structures\u2014NS (0.96\u2009mm), IT (0.74\u2009mm), and MS (0.43\u2009mm), whereas eight scans are required for smaller structures\u2014ON (0.80\u2009mm).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We have evaluated a label\u2010efficient pipeline for segmentation of sinonasal structures. Empirical results demonstrate that automated DL methods can achieve submillimeter accuracy using a small number of labeled CT scans. Our pipeline has the potential to improve pre\u2010operative planning workflows, robotic\u2010 and image\u2010guidance navigation systems, computer\u2010assisted diagnosis, and the construction of statistical shape models to quantify population variations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of Evidence<\/jats:title>\n                    <jats:p>N\/A<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ohn.868","type":"journal-article","created":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T03:33:15Z","timestamp":1719113595000},"page":"1217-1225","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Label\u2010Efficient Framework for Automated Sinonasal CT Segmentation in Image\u2010Guided Surgery"],"prefix":"10.1002","volume":"171","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2158-823X","authenticated-orcid":false,"given":"Manish","family":"Sahu","sequence":"first","affiliation":[{"name":"Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuliang","family":"Xiao","sequence":"additional","affiliation":[{"name":"Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose L.","family":"Porras","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery Johns Hopkins University School of Medicine Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ameen","family":"Amanian","sequence":"additional","affiliation":[{"name":"Division of Otolaryngology, Department of Surgery University of British Columbia Vancouver British Columbia Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aseem","family":"Jain","sequence":"additional","affiliation":[{"name":"Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Thamboo","sequence":"additional","affiliation":[{"name":"Division of Otolaryngology, Department of Surgery University of British Columbia Vancouver British Columbia Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Russell H.","family":"Taylor","sequence":"additional","affiliation":[{"name":"Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francis X.","family":"Creighton","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology Johns Hopkins University School of Medicine Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masaru","family":"Ishii","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology Johns Hopkins University School of Medicine Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,6,23]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1177\/0194599815572097"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.otc.2015.05.008"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1055\/s-0029-1237352"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00106-006-1386-7"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/lio2.436"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2217279"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14320"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2856464"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-021-02390-5"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2009.2035616"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4927567"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01008-z"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/ohn.317"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2012.05.001"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.09.025"},{"key":"e_1_2_8_18_1","doi-asserted-by":"crossref","unstructured":"XuZ NiethammerM. DeepAtlas: joint semi\u2010supervised learning of image registration and segmentation. In Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference. Shenzhen China. October 13\u201317 2019 Proceedings Part II 22: Springer International Publishing; 2019:420\u2010429.","DOI":"10.1007\/978-3-030-32245-8_47"},{"key":"e_1_2_8_19_1","doi-asserted-by":"crossref","unstructured":"RonnebergerO FischerP BroxT. U\u2010net: convolutional networks for biomedical image segmentation. InMedical Image Computing and Computer\u2010Assisted Intervention\u2013MICCAI 2015: 18th International Conference. Munich Germany. October 5\u20139 2015 Proceedings Part III 18: Springer International Publishing; 2015:234\u2010241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_8_20_1","volume-title":"cc3d: Connected components on multilabel 3d images","author":"Silversmith W","year":"2021"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1076-6332(03)00671-8"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-015-0068-x"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1177\/01945998211044982"}],"container-title":["Otolaryngology\u2013Head and Neck Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aao-hnsfjournals.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ohn.868","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T09:00:17Z","timestamp":1760950817000},"score":1,"resource":{"primary":{"URL":"https:\/\/aao-hnsfjournals.onlinelibrary.wiley.com\/doi\/10.1002\/ohn.868"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,23]]},"references-count":22,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["10.1002\/ohn.868"],"URL":"https:\/\/doi.org\/10.1002\/ohn.868","archive":["Portico"],"relation":{},"ISSN":["0194-5998","1097-6817"],"issn-type":[{"value":"0194-5998","type":"print"},{"value":"1097-6817","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,23]]},"assertion":[{"value":"2023-12-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-03","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}