{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T18:56:01Z","timestamp":1788548161382,"version":"build-2803163510"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T00:00:00Z","timestamp":1702425600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Korean Government"},{"name":"Ministry of Science and Information Communication Technology","award":["2023R1A2C200532611"],"award-info":[{"award-number":["2023R1A2C200532611"]}]},{"DOI":"10.13039\/100019266","name":"Korea Medical Device Development Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100019266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry, and Energy","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Ministry of Health & Welfare"},{"DOI":"10.13039\/501100003569","name":"Ministry of Food and Drug Safety","doi-asserted-by":"publisher","award":["1711194231"],"award-info":[{"award-number":["1711194231"]}],"id":[{"id":"10.13039\/501100003569","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003569","name":"Ministry of Food and Drug Safety","doi-asserted-by":"publisher","award":["KMDF_PR_20200901_0011"],"award-info":[{"award-number":["KMDF_PR_20200901_0011"]}],"id":[{"id":"10.13039\/501100003569","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>This study aimed to develop a robust and accurate deep learning network for detecting the posterior superior alveolar artery (PSAA) in dental cone-beam CT (CBCT) images, focusing on the precise localization of the centre pixel as a critical centreline pixel.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>PSAA locations were manually labelled on dental CBCT data from 150 subjects. The left maxillary sinus images were horizontally flipped. In total, 300 datasets were created. Six different deep learning networks were trained, including 3D U-Net, deeply supervised 3D U-Net (3D U-Net DS), multi-scale deeply supervised 3D U-Net (3D U-Net MSDS), 3D Attention U-Net, 3D V-Net, and 3D Dense U-Net. The performance evaluation involved predicting the centre pixel of the PSAA. This was assessed using mean absolute error (MAE), mean radial error (MRE), and successful detection rate (SDR).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The 3D U-Net MSDS achieved the best prediction performance among the tested networks, with an MAE measurement of 0.696\u2009\u00b1\u20091.552\u2009mm and MRE of 1.101\u2009\u00b1\u20092.270\u2009mm. In comparison, the 3D U-Net showed the lowest performance. The 3D U-Net MSDS demonstrated a SDR of 95% within a 2\u2009mm MAE. This was a significantly higher result than other networks that achieved a detection rate of over 80%.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>This study presents a robust deep learning network for accurate PSAA detection in dental CBCT images, emphasizing precise centre pixel localization. The method achieves high accuracy in locating small vessels, such as the PSAA, and has the potential to enhance detection accuracy and efficiency, thus impacting oral and maxillofacial surgery planning and decision-making.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/dmfr\/twad002","type":"journal-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:13:59Z","timestamp":1704845639000},"page":"22-31","source":"Crossref","is-referenced-by-count":4,"title":["Automatic detection of posterior superior alveolar artery in dental cone-beam CT images using a deeply supervised multi-scale 3D network"],"prefix":"10.1093","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7521-2567","authenticated-orcid":false,"given":"Jae-An","family":"Park","sequence":"first","affiliation":[{"name":"Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University , 101 Daehak-ro, Jongno-gu , Seoul, 03080, 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