Автоматический анализ изображений микроскопии с применением облачного сервиса DLgram01

Translated title of the contribution: Automatic analysis of microscopy images using the DLgram01 cloud service

A. V. Matveev, M. Y. Mashukov, A. V. Nartova, N. N. Sankova, A. G. Okunev

Research output: Contribution to journalArticlepeer-review

Abstract

The study of materials by microscopy often includes counting the number of observed objects and determining their statistical parameters, for which it is necessary to measure hundreds of objects. The created DLgram01 cloud service allows specialists in the field of materials science who do not have programming skills to perform automated image processing - to determine the number and parameters (area, size) of the objects under study. The service is developed using the latest achievements in the field of deep machine learning. To train a neural network, the user needs to label only several objects. The neural network is trained automatically in a few minutes. Important features of the DLgram01 service are the ability to adjust the results of neural network prediction, as well as obtaining detailed information about all recognized objects. Using the service allows to significantly decrease the time for quantitative image analysis, reduce the influence of the subjective factor, increase the accuracy of the analysis and its ergo-intensity.
Translated title of the contributionAutomatic analysis of microscopy images using the DLgram01 cloud service
Original languageRussian
Article number32
Pages (from-to)300-311
Number of pages12
JournalPhysical and chemical aspects of the study of clusters nanostructures and nanomaterials
Issue number13
DOIs
Publication statusPublished - 2021

Keywords

  • microscopy
  • recognition
  • nanoparticles
  • deep neural networks
  • artificial intelligence
  • DEEP
  • SEGMENTATION

OECD FOS+WOS

  • 1.03.UH PHYSICS, ATOMIC, MOLECULAR & CHEMICAL
  • 2.1.NS NANOSCIENCE & NANOTECHNOLOGY
  • 1.03.UK PHYSICS, CONDENSED MATTER

State classification of scientific and technological information

  • 31 CHEMISTRY

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