TY - GEN
T1 - Using Computer Vision and Deep Learning for Cells Recognition
AU - Kudinov, Vitalii Yu
AU - Mashukov, Mikhail Yu
AU - Maslova, Ekaterina A.
AU - Orishchenko, Konstantin E.
AU - Okunev, Aleksey G.
AU - Matveev, Andrey V.
N1 - Funding Information:
This work was performed as part of a State Task for the BIC SB RAS, supported by the Ministry of Education and Science of Russian Federation, grant #2019-0546 (FSUS-2020-0040) and 5-100 Excellence Program.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/11/14
Y1 - 2020/11/14
N2 - The task of the objects identification, counting, and measurement is a huge part of scientific investigations and technological applications. Automated methods using traditional processing such as segmentation, edge detection, and so on represented by available software (e.g. CellProfiler) are not flexible, can be used only with images of high-quality, and in addition require setting a part of parameters by hand. This contribution presents the applying the deep learning method for recognition of HeLa cells expressing green fluorescent protein (EGFP) automatically. We used Cascade Mask R-CNN neural networks which has a ResNeXt backbone and deformable convolutional networks layers. Training dataset contained seven pictures with 5754 labeled cells. Three images with 2469 labeled cells were used as test-dataset. The trained neural network showed mAP=0.4.
AB - The task of the objects identification, counting, and measurement is a huge part of scientific investigations and technological applications. Automated methods using traditional processing such as segmentation, edge detection, and so on represented by available software (e.g. CellProfiler) are not flexible, can be used only with images of high-quality, and in addition require setting a part of parameters by hand. This contribution presents the applying the deep learning method for recognition of HeLa cells expressing green fluorescent protein (EGFP) automatically. We used Cascade Mask R-CNN neural networks which has a ResNeXt backbone and deformable convolutional networks layers. Training dataset contained seven pictures with 5754 labeled cells. Three images with 2469 labeled cells were used as test-dataset. The trained neural network showed mAP=0.4.
KW - cell recognition
KW - deep neural networks
KW - HeLa cells
KW - microscopy
UR - http://www.scopus.com/inward/record.url?scp=85099530689&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/ed01532e-78ea-36f7-aa09-32dd52c0ecc1/
U2 - 10.1109/S.A.I.ence50533.2020.9303201
DO - 10.1109/S.A.I.ence50533.2020.9303201
M3 - Conference contribution
AN - SCOPUS:85099530689
SN - 9780738131115
T3 - Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020
SP - 17
EP - 20
BT - Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020
Y2 - 14 November 2020 through 15 November 2020
ER -