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.

Cite: Matveev A.V. , Nartova A.V. , Sankova N.N. , Okunev A.G.
DLgram Cloud Service for Deep-Learning analysis of Microscopy Images
Microscopy Research and Technique. 2024. V.87. N5. P.991-998. DOI: 10.1002/jemt.24480 WOS Scopus РИНЦ PMID OpenAlex

Dates:

Submitted:	Oct 23, 2023
Accepted:	Dec 12, 2023
Published online:	Jan 8, 2024
Published print:	May 1, 2024

Identifiers:

Web of science:	WOS:001137473300001
Scopus:	2-s2.0-85181485920
Elibrary:	65788202
PMID:	38186233
OpenAlex:	W4390664404

Citing:

DB	Citing
Web of science	1
Scopus	1
Elibrary	1

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