Deep learning for segmentation in radiation therapy planning: a review.
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| Title: | Deep learning for segmentation in radiation therapy planning: a review. |
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| Authors: | Samarasinghe G; School of Computer Science and Engineering, University of New South Wales, Sydney, New South Wales, Australia.; Ingham Institute for Applied Medical Research and South Western Sydney Clinical School, UNSW, Liverpool, New South Wales, Australia., Jameson M; Genesiscare, Sydney, New South Wales, Australia.; St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia., Vinod S; Ingham Institute for Applied Medical Research and South Western Sydney Clinical School, UNSW, Liverpool, New South Wales, Australia.; Liverpool Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia., Field M; Ingham Institute for Applied Medical Research and South Western Sydney Clinical School, UNSW, Liverpool, New South Wales, Australia.; Liverpool Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia., Dowling J; Commonwealth Scientific and Industrial Research Organisation, Australian E-Health Research Centre, Herston, Queensland, Australia., Sowmya A; School of Computer Science and Engineering, University of New South Wales, Sydney, New South Wales, Australia., Holloway L; Ingham Institute for Applied Medical Research and South Western Sydney Clinical School, UNSW, Liverpool, New South Wales, Australia.; Liverpool Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia. |
| Source: | Journal of medical imaging and radiation oncology [J Med Imaging Radiat Oncol] 2021 Aug; Vol. 65 (5), pp. 578-595. Date of Electronic Publication: 2021 Jul 26. |
| Publication Type: | Journal Article; Review |
| Journal Info: | Publisher: Wiley-Blackwell Pub. Asia Country of Publication: Australia NLM ID: 101469340 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1754-9485 (Electronic) Linking ISSN: 17549477 NLM ISO Abbreviation: J Med Imaging Radiat Oncol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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