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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/38635
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dc.contributor.authorAsipovich, V. S.-
dc.contributor.authorDudich, O. N.-
dc.contributor.authorKrasilnikova, V. L.-
dc.contributor.authorKarakulko, A. A.-
dc.contributor.authorRadnionok, A. L.-
dc.contributor.authorMoroz, P. A.-
dc.contributor.authorNikolaev, A. Y.-
dc.contributor.authorKonovalova, M. A.-
dc.contributor.authorYashin, K. D.-
dc.date.accessioned2020-03-03T09:41:19Z-
dc.date.available2020-03-03T09:41:19Z-
dc.date.issued2019-
dc.identifier.citationDeep Learning in Processing Medical Images and Calculating the Orbit Volume / V. S. Asipovich [and other] // 4-th International Conference on Nanotechnologies and Biomedical Engineering : Proceedings of ICNBME-2019, Chisinau, Moldova, September 18-21, 2019 / editors : Ion Tiginyanu, Victor Sontea, Serghei Railean. – Switzerland : Springer Nature Switzerland, 2019. – Vol. 77. – P. 519-522. – (IFMBE Proceedings).ru_RU
dc.identifier.urihttps://libeldoc.bsuir.by/handle/123456789/38635-
dc.description.abstractA software tool for calculating the volume of a soft-tissue eye orbit using the deep learning of neural network Mask R-CNN has been developed and tested. The result of the development will be in demand when evaluating the results of surgical intervention for the reconstruction of the thin bones of the orbit. It was established that the inaccuracy in constructing the contour of a soft-tissue orbit is 4–8%.ru_RU
dc.language.isoenru_RU
dc.publisherSpringerru_RU
dc.subjectпубликации ученыхru_RU
dc.subjectOrbitru_RU
dc.subjectOrbit volumeru_RU
dc.subjectDeep learningru_RU
dc.subjectNeural networkru_RU
dc.subjectBiomedical imagesru_RU
dc.titleDeep Learning in Processing Medical Images and Calculating the Orbit Volumeru_RU
dc.typeСтатьяru_RU
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