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Phase-contrast microscopy for automated morphology analysis of astrocytes in vitro using deep learning-based method
Russian version
Simkin I. V. 1, Shcheveleva T. E.1, Kolotyeva N. A. 1,2, Novikova S. V. 1,2, Simkina A. A.1, Yakovlev E. V.1, Yurchenko S. O.1, Salmina A. B. 1,2, Kryuchkov N. P.1
1Bauman Moscow State Technical University, Moscow, Russia
2Russian Сenter of Neurology and Neurosciences, Moscow, Russia
Email: vanyasimkin@gmail.com, tshheveleva03@mail.ru, kolotyeva.n.a@neurology.ru, shirokova2001@yandex.ru, yakov.egor@gmail.com, st.yurchenko@mail.ru, allasalmina@mail.ru, kruchkov_nkt@mail.ru
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The study of astrocyte morphology in vitro is of fundamental importance for neuroscience. However, traditional analysis methods, which require cell fixation and staining, preclude the observation of dynamic structural changes in living cells. Phase-contrast microscopy is a non-invasive optical technique that can overcome this limitation, but the automatic segmentation and quantitative analysis of the resulting images remain challenging due to the variable and complex structure of astrocytes. This paper presents an advanced method for the automatic segmentation and morphometric analysis of phase-contrast images of astrocytes, based on a convolutional neural network architecture. The method was optimized through automatic hyperparameter tuning and achieved a Dice coefficient of 0.73 on the test dataset. The developed approach enables the automatic delineation of the astrocyte cell body and its processes, as well as precise measurements of key morphological parameters (area, length, and branching complexity) over time. Phase-contrast microscopy, as a non-invasive optical method, forms the basis of the developed approach, enabling real-time analysis of astrocyte morphology. Keywords: phase-contrast microscopy, machine learning, image segmentation, YOLOv11, astrocytes, morphometric analysis, optical biophotonics.
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