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Deep Learning in Processing Medical Images and Calculating the Orbit Volume

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dc.contributor.author ASIPOVICH, V. S.
dc.contributor.author DUDICH, O. N.
dc.contributor.author KRASILNIKOVA, V. L.
dc.contributor.author KARAKULKO, A. A.
dc.contributor.author RADNIONOK, A. L.
dc.contributor.author MOROZ, P. A.
dc.contributor.author NIKOLAEV, A. Y.
dc.contributor.author KONOVALOVA, M. A.
dc.contributor.author YASHIN, K. D.
dc.date.accessioned 2020-05-23T19:08:26Z
dc.date.available 2020-05-23T19:08:26Z
dc.date.issued 2019
dc.identifier.citation ASIPOVICH, V. S., DUDICH, O. N., KRASILNIKOVA, V. L. et al. Deep Learning in Processing Medical Images and Calculating the Orbit Volume. In: ICNMBE: International conference on Nanotechnologies and Biomedical Engineering: proc. of the 4rd intern. conf., Sept. 18-21 : Program & Abstract Book , 2019. Chişinău, 2019, p. 130. ISBN 978-9975-72-392-3. en_US
dc.identifier.isbn 978-9975-72-392-3
dc.identifier.uri https://doi.org/10.1007/978-3-030-31866-6_93
dc.identifier.uri http://repository.utm.md/handle/5014/8367
dc.description Access full text - https://doi.org/10.1007/978-3-030-31866-6_93 en_US
dc.description.abstract A 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%. en_US
dc.language.iso en en_US
dc.publisher Tehnica UTM en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject orbit volume en_US
dc.subject deep learning en_US
dc.subject neural network en_US
dc.subject biomedical images en_US
dc.title Deep Learning in Processing Medical Images and Calculating the Orbit Volume en_US
dc.type Article en_US


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