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However, gland segmentation and quantitation of gland uptake is challenging. We develop a salivary gland SPECT\/CT with automated segmentation using a deep convolutional neural network (CNN). The protocol comprises SPECT\/CT at 20\u00a0min, sialagogue stimulation, and SPECT at 40\u00a0min post-injection of Tc-99m pertechnetate (555\u00a0MBq). The 40-min SPECT was reconstructed using the 20-min CT after misregistration correction. Manual salivary gland segmentation for %injected dose (%ID) by human experts proved highly reproducible, but took 15\u00a0min per scan. An automatic salivary segmentation method was developed using a modified 3D U-Net for end-to-end learning from the human experts (<jats:italic>n<\/jats:italic>\u2009=\u2009333). The automatic segmentation performed comparably with human experts in voxel-wise comparison (mean Dice similarity coefficient of 0.81 for parotid and 0.79 for submandibular, respectively) and gland %ID correlation (<jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>\u2009=\u20090.93 parotid, <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>\u2009=\u20090.95 submandibular) with an operating time less than 1\u00a0min. The algorithm generated results that were comparable to the reference data. In conclusion, with the aid of a CNN, we developed a quantitative salivary gland SPECT\/CT protocol feasible for clinical applications. The method saves analysis time and manual effort while reducing patients\u2019 radiation exposure.<\/jats:p>","DOI":"10.1038\/s41598-021-87497-0","type":"journal-article","created":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T10:07:47Z","timestamp":1617962867000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Quantitative salivary gland SPECT\/CT using deep convolutional neural networks"],"prefix":"10.1038","volume":"11","author":[{"given":"Junyoung","family":"Park","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jae Sung","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongkyu","family":"Oh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyun Gee","family":"Ryoo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeong Hee","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Won Woo","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,4,9]]},"reference":[{"key":"87497_CR1","first-page":"1551","volume":"39","author":"C Alexander","year":"1998","unstructured":"Alexander, C., Bader, J. 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