title: ÐÐ½Ð»Ð°Ð¹Ð½-ÑÐµÑÐ²Ð¸Ñ ÐÐ-Ð´ÐµÐºÐ¾Ð½Ð²Ð¾Ð»ÑÑÐ¸Ð¸ Ð¸ Ð´ÐµÐ½Ð¾Ð¹Ð·Ð¸Ð½Ð³Ð° Ð´Ð»Ñ ÐºÐ¾Ð½ÑÐ¾ÐºÐ°Ð»ÑÐ½ÑÑ Ð¼Ð¸ÐºÑÐ¾ÑÐºÐ¾Ð¿Ð¾Ð²
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Online AI deconvolution and denoising service for confocal microscopes
Creators: name: ÐÐ¾Ð»Ð¸Ð½, ÐÐ²Ð°Ð½
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÑÐ»ÑÐ¾ÑÐµÑÑÐµÐ½ÑÐ½Ð°Ñ Ð¼Ð¸ÐºÑÐ¾ÑÐºÐ¾Ð¿Ð¸Ñ
ÐºÐ¾Ð½ÑÐ¾ÐºÐ°Ð»ÑÐ½Ð°Ñ Ð¼Ð¸ÐºÑÐ¾ÑÐºÐ¾Ð¿Ð¸Ñ
Ð¾Ð½Ð»Ð°Ð¹Ð½-ÑÐµÑÐ²Ð¸Ñ
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
Ð´ÐµÐ½Ð¾Ð¹Ð·Ð¸Ð½Ð³
ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ
ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð°ÑÑ Ð¸ÑÐµÐºÑÑÑÐ° ÑÐµÑÐ¸
fluorescence microscopy
confocal microscopy
online service
computer vision
denoising
segmentation
convolutional neural networks
network architecture

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description: This work is devoted to the development of an online service that produces various processes to improve the quality of three-dimensional images of small-sized luminescent objects recorded by confocal microscopes. The following objectives were formulated as part of this research: 1. Study of existing algorithms that solve similar problems. 2. Development of a machine learning algorithm for automatic segmentation of fluorescent spheres. 3. Development of a deep learning denoising (noise reduction) algorithm for three-dimensional images. 4. Creation of an online service that provides interaction with the implemented algorithms. As a result of this work, an online service was developed that includes the following functionality: 1. Manual and automatic approaches to fluorescent sphere segmentation. The automatic approach was based on computer vision methods. 2. A deep learning algorithm for denoising 3D images based on Noise2Noise (N2N) network and Non-local means (NLM) filter is trained and tested on data from different microscopes. 3. Implemented Richardsdon-Lucy deconvolution method to generate expert point scattering function (ePRF) and improve 3D images. It is concluded that the online service is effective in obtaining more accurate images of cellular structures and materials, which allows detailed analysis of observed objects in biomedical research.