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Transfer learning methods for glioma C6 cell segmentation
D.A. Ilyukhin 1, V.O. Yachnaya 1,2, R.O. Malashin 1,2, M.K. Ermachenkova 1,2, A.V. Volkov 3,4, S.G. Pashkevich 4, A.A. Denisov 3,4

1 Pavlov Institute of Physiology, Russian Academy of Sciences,
Naberezhnaya Makarova 6, Saint-Petersburg, 199034, Russia;
2 Saint Petersburg State University of Aerospace Instrumentation,
Bolshaya Morskaya 67, Saint Petersburg, 190000, Russia;
3 Institute of Physiology, National Academy of Sciences of Belarus,
Academicheskaya 28, Minsk, 220072, Belarus;
4 Belarusian State University,
Prospekt Nezavisimosti 4, Minsk, 220030, Belarus

 PDF, 2504 kB

DOI: 10.18287/2412-6179-CO-1609

Pages: 794-804.

Full text of article: Russian language.

Abstract:
In this paper, we present an algorithm for binary segmentation of glioma C6 cells using deep learning methods to simplify and speed up the analysis of this culture growth. The first of its kind dataset containing 30 microscopic phase-contrast images of glioma C6 cells is collected to design and test the algorithm. We explore the influence of the encoder architecture in the neural network segmenter on the accuracy of glioma cell segmentation. Transfer learning approaches using the LIVECell dataset of microscopic images and the large ImageNet dataset of non-specialized images are used since the collected dataset contains a relatively small number of images. Experiments show that pre-training the neural network on LIVECell provides a significant advantage in low-resolution glioma cell recognition, with encoders trained on ImageNet providing better results at higher resolution. The paper proposes ways to improve the generalizing abilities of LIVECell weights to work at high resolution by applying augmentation. We demonstrate that using different starting weights allows us to obtain different generalization properties beyond the training set, which can be useful when detecting, or excluding from consideration, other cells in an image.

Keywords:
binary segmentation, microscopic images, computer vision, U-Net, neural networks, glioma C6.

Citation:
Ilyukhin DA, Yachnaya VO, Malashin RO, Ermachenkova MK, Volkov AV, Pashkevich SG, Denisov AA. Transfer learning methods for glioma C6 cell segmentation. Computer Optics 2025; 49(5): 794-804. DOI: 10.18287/2412-6179-CO-1609.

Acknowledgements:
The work was partly funded under a grant from the St. Petersburg Science Foundation and a government project of Pavlov Institute of Physiology, Russian Academy of Sciences (№1021062411653-4-3.1.8), and financially supported by BRFFR (SCST) under grant “Detection of tumor cells in nervous tissue using deep learning methods” (contract No. M24SPbG-010).

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