{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T21:51:10Z","timestamp":1790545870068,"version":"4.1.0"},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2020,2,11]],"date-time":"2020-02-11T00:00:00Z","timestamp":1581379200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,3,23]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n               <jats:p>This study aimed to examine the efficacy of semantic segmentation implemented by deep learning and to confirm whether this method is more effective than a commercially dominant auto-segmentation tool with regards to delineating normal lung excluding the trachea and main bronchi. A total of 232 non-small-cell lung cancer cases were examined. The computed tomography (CT) images of these cases were converted from Digital Imaging and Communications in Medicine (DICOM) Radiation Therapy (RT) formats to arrays of 32\u00a0\u00d7\u00a0128\u00a0\u00d7\u00a0128 voxels and input into both 2D and 3D U-Net, which are deep learning networks for semantic segmentation. The number of training, validation and test sets were 160, 40 and 32, respectively. Dice similarity coefficients (DSCs) of the test set were evaluated employing Smart Segmentation\u24c7 Knowledge Based Contouring (Smart segmentation is an atlas-based segmentation tool), as well as the 2D and 3D U-Net. The mean DSCs of the test set were 0.964 [95% confidence interval (CI), 0.960\u20130.968], 0.990 (95% CI, 0.989\u20130.992) and 0.990 (95% CI, 0.989\u20130.991) with Smart segmentation, 2D and 3D U-Net, respectively. Compared with Smart segmentation, both U-Nets presented significantly higher DSCs by the Wilcoxon signed-rank test (P\u00a0&amp;lt;\u00a00.01). There was no difference in mean DSC between the 2D and 3D U-Net systems. The newly-devised 2D and 3D U-Net approaches were found to be more effective than a commercial auto-segmentation tool. Even the relatively shallow 2D U-Net which does not require high-performance computational resources was effective enough for the lung segmentation. Semantic segmentation using deep learning was useful in radiation treatment planning for lung cancers.<\/jats:p>","DOI":"10.1093\/jrr\/rrz086","type":"journal-article","created":{"date-parts":[[2019,12,28]],"date-time":"2019-12-28T20:08:48Z","timestamp":1577563728000},"page":"257-264","source":"Crossref","is-referenced-by-count":67,"title":["Efficacy evaluation of 2D, 3D U-Net semantic segmentation and atlas-based segmentation of normal lungs excluding the trachea and main bronchi"],"prefix":"10.1093","volume":"61","author":[{"given":"Takafumi","family":"Nemoto","sequence":"first","affiliation":[{"name":"Division of Radiation Oncology, Saiseikai Yokohamashi Tobu-Hospital, Shimosueyoshi 3-6-1, Tsurumi-ku, Yokohama-shi, Kanagawa, 230-8765, Japan"},{"name":"Department of Radiology, Keio University School of Medicine, Shinanomachi 35, Shinjyuku-ku, Tokyo, 160-8582, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natsumi","family":"Futakami","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, Tokai University School of Medicine, Shimokasuya 143, Isehara-shi, Kanagawa, 259-1143, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masamichi","family":"Yagi","sequence":"first","affiliation":[{"name":"HPC&AI Business Dept., Platform Technical Engineer Div., System Platform Solution Unit, Fujitsu Limited, World Trade Center Building, 4-1, Hamamatsucho 2-chome, Minato-ku, Tokyo, 105-6125, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atsuhiro","family":"Kumabe","sequence":"first","affiliation":[{"name":"Department of Radiology, Keio University School of Medicine, Shinanomachi 35, Shinjyuku-ku, Tokyo, 160-8582, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atsuya","family":"Takeda","sequence":"first","affiliation":[{"name":"Radiation Oncology Center, Ofuna Chuo Hospital, Kamakura, 247-0056, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Etsuo","family":"Kunieda","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, Tokai University School of Medicine, Shimokasuya 143, Isehara-shi, Kanagawa, 259-1143, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naoyuki","family":"Shigematsu","sequence":"first","affiliation":[{"name":"Department of Radiology, Keio University School of Medicine, Shinanomachi 35, Shinjyuku-ku, Tokyo, 160-8582, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,2,11]]},"reference":[{"key":"2020052419000576100_ref1","article-title":"Globocan 2018","author":"International Agency for Research on Cancer"},{"key":"2020052419000576100_ref2","article-title":"Cancer statistics in Japan - 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