Abstract
The aim of this study is the comparison of the various deep convolutional neural networks for segmentation of fibroblast in brightfield microscopic images. This investigation compares two main architectures: Unet and Linknet. Every main architecture is equipped with various ‘backbone’ network creating specific bundle. The experimental dataset consisting of 16 sequences of images of monitored cells’ culture have been split into training and validation set. Then it was analysed and used for validation of the networks to establish the best bundle (net architecture and ‘backbone’). This study proved that trained deep convolutional neural networks could be used as a segmentation tool in this task.
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References
Korzynska, A., Roszkowiak, L., Siemion, K., Zak, J., Zakrzewska, K., Samluk, A., Wencel, A., Pluta, K., Pijanowska, D.: The analysis of the shape of the genetically modified human skin fibroblasts in culture. In: Recent Developments and Achievements in Biocybernetics and Biomedical Engineering, pp. 98–109. Springer, Cham (2017)
Pang, F., Liu, Z.: Analyzing temporal dynamics of cell deformation and intracellular movement with video feature aggregation. Biomed. Eng. OnLine 18(1), 20 (2019)
Samluk, A., Zakrzewska, K.E., Pluta, K.D.: Generation of fluorescently labeled cell lines, C3A hepatoma cells, and human adult skin fibroblasts to study coculture models. Artif. Organs 37(7), E123–E130 (2013)
Korzynska, A., Siemion, K., Zak, J., Roszkowiak, L., Wencel, A., Pluta, K., Pijanowska, D.: The analysis of the movement of the genetically modified human skin fibroblasts in culture. In: 2018 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE, October 2018
Li, C., Xu, C., Gui, C., Fox, M.: Level set evolution without re-initialization: a new variational formulation. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005). IEEE (2005)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M, Frangi, A.F. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pp. 234– 241. Springer, Cham (2015). ISBN 978-3-319-24574-4
Chaurasia, A., Culurciello, E.: LinkNet: exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE Visual Communications and Image Processing (VCIP), pp. 1–4, December 2017. https://doi.org/10.1109/VCIP.2017.8305148
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6230–6239, July 2017. https://doi.org/10.1109/CVPR.2017.660. ISSN 1063-6919
Yakubovskiy, P.: Segmentation models repository. https://github.com/qubvel/segmentation_models/. Accessed 19 Mar 2019
Acknowledgments
We would like to thank Comtegra S.A. for renting us the two Tesla v100 cards that we used for computing. They had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
This study was supported by statutory funds of Nalecz Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences.
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Roszkowiak, L., Zak, J., Siemion, K., Kinasiewicz, J., Korzynska, A. (2020). Fibroblast Segmentation in Microscopic Brightfield Images with Convolutional Neural Network. In: Korbicz, J., Maniewski, R., Patan, K., Kowal, M. (eds) Current Trends in Biomedical Engineering and Bioimages Analysis. PCBEE 2019. Advances in Intelligent Systems and Computing, vol 1033. Springer, Cham. https://doi.org/10.1007/978-3-030-29885-2_13
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