Application of deep convolutional and long short-term memory neural networks to red blood cells motion detection and velocity approximation

Alexey V. Kornaev; Viktor V. Dremin; Elena P. Kornaeva; Mikhail V. Volkov

doi:10.1117/12.2626040

29 April 2022 Application of deep convolutional and long short-term memory neural networks to red blood cells motion detection and velocity approximation

Alexey V. Kornaev, Viktor V. Dremin, Elena P. Kornaeva, Mikhail V. Volkov

Author Affiliations +

Proceedings Volume 12194, Computational Biophysics and Nanobiophotonics; 121940C (2022) https://doi.org/10.1117/12.2626040
Event: XXV Annual Conference Saratov Fall Meeting 2021; and IX Symposium on Optics and Biophotonics, 2021, Saratov, Russian Federation

Abstract

The paper deals with processing data obtained using nailfold high-speed videocapillaroscopy. To detect the red blood cells velocity two approaches are used. The deterministic approach is based on pixel intensities analysis for object detection and calculation of the displacement and velocity of red blood cells in a capillary. The obtained data formulate targets for the second approach. The stochastic approach is based on a sequence of artificial neural networks. The semantic segmentation network UNet is used for capillary detection. Then, the classification network GoogLeNet or ResNet is used as a feature extractor to convert masked video frames to a sequence of feature vectors. And finally, the long short-term memory network is used to approximate the red blood cells velocity. The results demonstrated that the accuracy of the mean velocity approximation in the time range of several seconds is up to 0.96. But the accuracy at each specific time moment is less accurate. So, the proposed algorithm allows the determination of the RBCs mean velocity but it doesn't allow determination of the RBCs pulsations accurate enough.

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Alexey V. Kornaev, Viktor V. Dremin, Elena P. Kornaeva, and Mikhail V. Volkov "Application of deep convolutional and long short-term memory neural networks to red blood cells motion detection and velocity approximation", Proc. SPIE 12194, Computational Biophysics and Nanobiophotonics, 121940C (29 April 2022); https://doi.org/10.1117/12.2626040

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KEYWORDS

Blood

Capillaries

Video

Feature extraction

Motion detection

Neural networks

Biomedical optics

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