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DLgram Cloud Service for Deep-Learning analysis of Microscopy Images Научная публикация

Журнал Microscopy Research and Technique
ISSN: 1059-910X
Вых. Данные Год: 2024, Том: 87, Номер: 5, Страницы: 991-998 Страниц : 8 DOI: 10.1002/jemt.24480
Ключевые слова automation; deep learning; image processing; microscopy; recognition
Авторы Matveev Andrey V. 1 , Nartova Anna V. 1,2 , Sankova Natalya N. 1,3 , Okunev Alexey G. 1
Организации
1 Institute of Intellectual Robototechnics,Novosibirsk State University, Novosibirsk, Russia
2 Department of Physico-Chemical Research Methods, Boreskov Institute of Catalysis SB RAS, Novosibirsk, Russia
3 Department of Non-Traditional Catalytic Processes, Boreskov Institute of Catalysis SB RAS, Novosibirsk, Russia

Информация о финансировании (1)

1 Российский научный фонд 22-23-00951

Реферат: 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.
Библиографическая ссылка: 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
Даты:
Поступила в редакцию: 23 окт. 2023 г.
Принята к публикации: 12 дек. 2023 г.
Опубликована online: 8 янв. 2024 г.
Опубликована в печати: 1 мая 2024 г.
Идентификаторы БД:
Web of science: WOS:001137473300001
Scopus: 2-s2.0-85181485920
РИНЦ: 65788202
PMID (PubMed): 38186233
OpenAlex: W4390664404
Цитирование в БД:
БД Цитирований
Web of science 5
Scopus 10
OpenAlex 10
РИНЦ 5
Альметрики: