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The accurate segmentation of the maxillary sinus is required as a cornerstone for diagnosis and treatment planning. This study proposes a deep learning-based method for fully automatic segmentation of the maxillary sinus, including clear or hazy states, on cone-beam computed tomographic (CBCT) images. A model for segmentation of the maxillary sinuses was developed using U-Net, a convolutional neural network, and a total of 19,350 CBCT images were used from 90 maxillary sinuses (34 clear sinuses, 56 hazy sinuses). Post-processing to eliminate prediction errors of the U-Net segmentation results increased the accuracy. The average prediction results of U-Net were a dice similarity coefficient (DSC) of 0.9090\u2009\u00b1\u20090.1921 and a Hausdorff distance (HD) of 2.7013\u2009\u00b1\u20094.6154. After post-processing, the average results improved to a DSC of 0.9099\u2009\u00b1\u20090.1914 and an HD of 2.1470\u2009\u00b1\u20092.2790. The proposed deep learning model with post-processing showed good performance for clear and hazy maxillary sinus segmentation. This model has the potential to help dental clinicians with maxillary sinus segmentation, yielding equivalent accuracy in a variety of cases.<\/jats:p>","DOI":"10.1038\/s41598-022-18436-w","type":"journal-article","created":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T18:02:46Z","timestamp":1660759366000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["Deep learning-based fully automatic segmentation of the maxillary sinus on cone-beam computed tomographic images"],"prefix":"10.1038","volume":"12","author":[{"given":"Hanseung","family":"Choi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kug Jin","family":"Jeon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young Hyun","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eun-Gyu","family":"Ha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chena","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang-Sun","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,8,17]]},"reference":[{"key":"18436_CR1","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1046\/j.0143-2885.2001.00524.x","volume":"35","author":"C Hauman","year":"2002","unstructured":"Hauman, C., Chandler, N. & Tong, D. 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