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