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DLgram Cloud Service for Deep-Learning Analysis of Microscopy Images
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  • Andrey V. Matveev,
  • Anna V. Nartova,
  • Natalya N. Sankova,
  • Alexey G. Okunev
Andrey V. Matveev
Novosibirsk State University

Corresponding Author:[email protected]

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Anna V. Nartova
Novosibirsk State University
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Natalya N. Sankova
Novosibirsk State University
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Alexey G. Okunev
Novosibirsk State University
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Abstract

To analyze images in various fields of science and technology, it is often necessary to count observed objects and determine their parameters. This can be quite labor-intensive and time-consuming. This article presents DLgram, a universal, user-friendly cloud service that is developed for this purpose. It is based on deep learning technologies and does not require programming skills. The user labels several objects in the image and uploads it to the cloud where the neural network is trained to recognize the objects being studied. The user receives recognition results which, if necessary, can be corrected, errors removed, or missing objects added. In addition, it is possible to carry out mathematical processing of the data obtained to get information about the sizes, areas, and coordinates of the observed objects. The article describes the service features and discusses examples of its application. The DLgram service allows to reduce significantly the time spent on quantitative image analysis, reduce subjective factor influence, and increase the accuracy of analysis.
19 Oct 2023Submitted to Microscopy Research and Technique
24 Oct 2023Submission Checks Completed
24 Oct 2023Assigned to Editor
19 Nov 2023Review(s) Completed, Editorial Evaluation Pending
19 Nov 2023Reviewer(s) Assigned