title: ÐÐµÑÐ¾Ð´Ñ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ Ð¤Ð Ð¢ ÐºÐ¾Ð½ÑÐ¾ÐºÐ°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð¼Ð¸ÐºÑÐ¾ÑÐºÐ¾Ð¿Ð°
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: Deep Learning Methods for Constructing a Confocal Microscope PSF
Creators: name: Ð¡Ð°ÑÑÐº, ÐÐ»ÐµÐºÑÐ°Ð½Ð´Ñ
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: 2023
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
Ð´ÐµÐºÐ¾Ð½Ð²Ð¾Ð»ÑÑÐ¸Ñ
Ð¿ÑÐ°ÐºÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ
ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
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Ð¿Ð¸ÑÐ°Ð¼Ð¸Ð´Ð°Ð»ÑÐ½Ð°Ñ ÑÐµÑÑ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð²
fluorescence microscopy
confocal microscopy
deconvolution
practical applications
convolutional neural networks
network architecture
feature pyramidal network

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´ÑÑÐµÐ³Ð¾ ÑÐ»ÑÑÑÐµÐ½Ð¸Ðµ ÐºÐ°ÑÐµÑÑÐ²Ð° ÑÑÑÑÐ¼ÐµÑÐ½ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ ÑÐ»ÑÐ¾ÑÐµÑÑÐµÐ½ÑÐ½ÑÑ Ð¾Ð±ÑÐµÐºÑÐ¾Ð², Ð¿Ð¾Ð»ÑÑÐ°ÐµÐ¼ÑÑ Ð² ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ ÐºÐ¾Ð½ÑÐ¾ÐºÐ°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð¼Ð¸ÐºÑÐ¾ÑÐºÐ¾Ð¿Ð°. ÐÐ°Ð´Ð°ÑÐ¸, ÑÐµÑÐ°ÐµÐ¼ÑÐµ Ð² Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ: A. ÐÐ·ÑÑÐµÐ½Ð¸Ðµ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð², ÑÐµÑÐ°ÑÑÐ¸Ñ ÑÑÐ¾Ð¶Ð¸Ðµ Ð·Ð°Ð´Ð°ÑÐ¸. B. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, Ð°Ð¿Ð¿ÑÐ¾ÐºÑÐ¸Ð¼Ð¸ÑÑÑÑÐµÐ³Ð¾ Ð¿ÑÐ¾ÑÐµÑÑ Ð´ÐµÐºÐ¾Ð½Ð²Ð¾Ð»ÑÑÐ¸Ð¸ ÑÑÑÑÐ¼ÐµÑÐ½ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹. C. Ð ÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð° Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð¸ Ð´Ð°Ð½Ð½ÑÑ Ð´Ð»Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°, ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¸ Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ°ÑÐ¸Ñ ÑÐ°Ð±Ð¾ÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð±ÑÐ» ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, ÑÑÑÑÐºÑÑÑÐ° Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐ¸ ÐºÐ¾ÑÐ¾ÑÐ¾Ð³Ð¾ Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð° Ð½Ð° Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐµ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¾ÑÐ° DeblurGAN-v2. Ð¡ÐµÑÑ Ð±ÑÐ»Ð° Ð¾Ð±ÑÑÐµÐ½Ð° Ð½Ð° Ð´Ð°Ð½Ð½ÑÑ, Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð¼ ÑÐ²ÑÑÑÐºÐ¸ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ ÑÑÑÑÐ¼ÐµÑÐ½ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ñ ÑÐ´ÑÐ°Ð¼Ð¸ ÑÐ²ÑÑÑÐºÐ¸, Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ Ð¸Ð· Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ ÑÐ»ÑÐ¾ÑÐµÑÑÐµÐ½ÑÐ½ÑÑ ÑÑÐµÑ Ð¸ Ð¸Ñ ÑÐ¾ÑÐ½ÑÑ Ð¼Ð°ÑÐµÐ¼Ð°ÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ð¹. Ð Ð°Ð±Ð¾ÑÐ° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð³Ð¾ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð° Ð´ÐµÐºÐ¾Ð½Ð²Ð¾Ð»ÑÑÐ¸Ð¸ Ð¿ÑÐ¾ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð½Ð° Ð¸ Ð¿ÑÐ¾Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð° Ð½Ð° ÑÐµÐ°Ð»ÑÐ½ÑÑ ÑÐ½Ð¸Ð¼ÐºÐ°Ñ ÑÑÐµÑ, ÑÑÑÐ±Ð¾ÑÐµÐº Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð¾Ð². Ð¢Ð°ÐºÐ¶Ðµ Ð±ÑÐ» ÑÐ´ÐµÐ»Ð°Ð½ Ð²ÑÐ²Ð¾Ð´, ÑÑÐ¾ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½ÑÐ¹ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¼Ð¾Ð¶ÐµÑ Ð±ÑÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½Ð¸Ð¼ Ð½Ð° Ð¿ÑÐ°ÐºÑÐ¸ÐºÐµ ÐºÐ°Ðº Ð°Ð»ÑÑÐµÑÐ½Ð°ÑÐ¸Ð²Ð° ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ð¼ Ð¼ÐµÑÐ¾Ð´Ð°Ð¼ Ð´ÐµÐºÐ¾Ð½Ð²Ð¾Ð»ÑÑÐ¸Ð¸: Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÑ Ð¾ÑÐ»Ð¸ÑÐ°ÐµÑÑÑ Ð²ÑÑÐ¾ÐºÐ¾Ð¹ ÑÐºÐ¾ÑÐ¾ÑÑÑÑ ÑÐ°Ð±Ð¾ÑÑ Ð¸ Ð¾Ð±Ð»Ð°Ð´Ð°ÐµÑ ÑÑÐ¾Ð¶Ð¸Ð¼Ð¸ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð½Ð¸ÑÐ¼Ð¸ Ð¼ÐµÑÑÐ¸Ðº ÐºÐ°ÑÐµÑÑÐ²Ð°.
description: This paper is devoted to the implementation of a machine learning algorithm that improves the quality of three-dimensional images of fluorescent objects obtained during the operation of a confocal microscope. The main research tasks are: A. Researching existing algorithms that solve similar problems. B. Development of a machine learning algorithm that implements the deconvolution process. C. Implementation of data generation method to train the algorithm, testing and demonstration of the algorithm. As a result, a deep learning algorithm was developed, whose neural network structure is based on DeblurGAN-v2 generatorâs architecture. The network has been trained on the data obtained by convolution of different three-dimensional images by convolution kernels of fluorescent sphere image and their exact mathematical representations. The performance of the deconvolution algorithm has been tested and demonstrated on real images of spheres, tubes, and neurons. It was also concluded that the developed algorithm can be applied in practice as an alternative to existing deconvolution methods: the neural network has a high speed of operation and has similar quality metrics readings.