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Development of automated computer vision methods for cell counting and endometrial gland detection for medical images processing

https://doi.org/10.15514/ISPRAS-2020-32(3)-11

Abstract

Current work is focused on the processing of medical images obtained by performing a pathomorphological analysis of preparation. The algorithms for processing images of nuclei of light and confocal microscopy and tissue of light microscopy were considered in particular. The application of the proposed algorithms and software for detecting pathologies was justified.

About the Authors

Daniel Igorevich SERGEEV
Peter the Great St.Petersburg Polytechnic University
Russian Federation
PhD student of the Institute of Computer Science and Technology


Alexander Evgenievich ANDREEV
The Research Institute of Obstetrics, Gynecology and Reproductology named after D.O. Ott
Russian Federation
Graduate of the magistracy of SPbPU, researcher


Anna Olegovna DROBINTSEVA
St.Petersburg State Pediatric Medical University
Russian Federation
Associate Professor, Candidate of Biological Sciences, Associate Professor of the Department of Medical Biology


Slobodanka CENEVSKA
Peter the Great St.Petersburg Polytechnic University
Russian Federation
Graduate student of the Institute of Computer Science and Technology


Nikola KUKAVITSA
Peter the Great St.Petersburg Polytechnic University
Russian Federation
Graduate student of the Institute of Computer Science and Technology


Pavel Dmitrievich DROBINTSEV
Peter the Great St.Petersburg Polytechnic University
Russian Federation
Associate Professor, Candidate of Technical Sciences, Director of the Higher School of Software Engineering at the Institute of Computer Science and Technology


References

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Review

For citations:


SERGEEV D.I., ANDREEV A.E., DROBINTSEVA A.O., CENEVSKA S., KUKAVITSA N., DROBINTSEV P.D. Development of automated computer vision methods for cell counting and endometrial gland detection for medical images processing. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2020;32(3):119-130. https://doi.org/10.15514/ISPRAS-2020-32(3)-11



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ISSN 2079-8156 (Print)
ISSN 2220-6426 (Online)