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BSI is then measured using the segmented bones and extracted hot spots. To further improve the networks, deep supervision (DSV) and residual learning technologies were introduced.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>We evaluated the performance of the proposed system using 246 bone scintigrams of prostate cancer in terms of accuracy of skeleton segmentation, hot spot extraction, and BSI measurement, as well as computational cost. In a threefold cross-validation experiment, the best performance was achieved by BtrflyNet with DSV for skeleton segmentation and BtrflyNet with residual blocks. The cross-correlation between the measured and true BSI was 0.9337, and the computational time for a case was 112.0\u00a0s.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusion<\/jats:title>\n<jats:p>We proposed a deep learning-based BSI measurement system for a whole-body bone scintigram and proved its effectiveness by threefold cross-validation study using 246 whole-body bone scintigrams. The automatically measured BSI and computational time for a case are deemed clinically acceptable and reliable.<\/jats:p>\n<\/jats:sec>","DOI":"10.1007\/s11548-019-02105-x","type":"journal-article","created":{"date-parts":[[2019,12,13]],"date-time":"2019-12-13T18:03:34Z","timestamp":1576260214000},"page":"389-400","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Automated measurement of bone scan index from a whole-body bone scintigram"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2719-5923","authenticated-orcid":false,"given":"Akinobu","family":"Shimizu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hayato","family":"Wakabayashi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takumi","family":"Kanamori","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Atsushi","family":"Saito","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazuhiro","family":"Nishikawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiromitsu","family":"Daisaki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shigeaki","family":"Higashiyama","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joji","family":"Kawabe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,12,13]]},"reference":[{"issue":"6","key":"2105_CR1","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3322\/caac.21492","volume":"68","author":"F Bray","year":"2018","unstructured":"Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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All authors have approved the manuscript and agree with its submission to IJCARS.\nAuthors Shimizu A, Saito A, Higashiyama S, and Kawabe J have received research grants from Nihon\nMedi-Physics Co., Ltd. Authors Wakabayashi H and Kanamori T have no conflict of interest.\nAuthor Nishikawa K works for Nihon Medi-Physics Co., Ltd. Author Daisaki H worked for Nihon Medi-Physics Co., Ltd. from April 2012 to March 2017, and also received honorarium from Nihon Medi-Physics Co., Ltd. in his current position. All procedures in this study involving human participants were performed in accordance with the ethical standards of the institutional research committees and the 1975 Helsinki declaration (as revised in 2008(5)).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}