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

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
Article number121940C
JournalProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Publication statusPublished - 29 Apr 2022
EventSaratov Fall Meeting 2021: Computational Biophysics and Nanobiophotonics - Saratov, Russian Federation
Duration: 27 Sept 20211 Oct 2021

Bibliographical note

Copyright 2022 SPIE. One print or electronic copy may be made for personal use only. Systematic reproduction, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.

Funding Information:
The is work is funded by the Russian Science Foundation under the grant No 20-79-00332.


  • approximation
  • capillary
  • deep learning
  • feature extraction
  • red blood cells
  • semantic segmentation
  • transfer learning
  • videocapillaroscopy


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