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Focus prediction of medical microscopic images based on Lightweight Densely Connected with Squeeze-and-Excitation Network
Due to the demand for sample observation, optical microscopy has become an essential tool in the fields of biology and medicine. In addition, it is impossible to maintain the living sample in focus over long-time observation. Rapid focus prediction which involves moving a microscope stage along a ve...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10338878/ https://www.ncbi.nlm.nih.gov/pubmed/37457013 http://dx.doi.org/10.3389/fnins.2023.1213176 |
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author | Jiang, Hesong Ma, Li Wang, Xueyuan Zhang, Juan Liu, Yueyue Wang, Dan Wu, Peihong Han, Wanfen |
author_facet | Jiang, Hesong Ma, Li Wang, Xueyuan Zhang, Juan Liu, Yueyue Wang, Dan Wu, Peihong Han, Wanfen |
author_sort | Jiang, Hesong |
collection | PubMed |
description | Due to the demand for sample observation, optical microscopy has become an essential tool in the fields of biology and medicine. In addition, it is impossible to maintain the living sample in focus over long-time observation. Rapid focus prediction which involves moving a microscope stage along a vertical axis to find an optimal focus position, is a critical step for high-quality microscopic imaging of specimens. Current focus prediction algorithms, which are time-consuming, cannot support high frame rate imaging of dynamic living samples, and may introduce phototoxicity and photobleaching on the samples. In this paper, we propose Lightweight Densely Connected with Squeeze-and-Excitation Network (LDSE-NET). The results of the focusing algorithm are demonstrated on a public dataset and a self-built dataset. A complete evaluation system was constructed to compare and analyze the effectiveness of LDSE-NET, BotNet, and ResNet50 models in multi-region and multi-multiplier prediction. Experimental results show that LDSE-NET is reduced to 1E-05 of the root mean square error. The accuracy of the predicted focal length of the image is increased by 1 ~ 2 times. Training time is reduced by 33.3%. Moreover, the volume of the model only reaches the KB level, which has the characteristics of being lightweight. |
format | Online Article Text |
id | pubmed-10338878 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103388782023-07-14 Focus prediction of medical microscopic images based on Lightweight Densely Connected with Squeeze-and-Excitation Network Jiang, Hesong Ma, Li Wang, Xueyuan Zhang, Juan Liu, Yueyue Wang, Dan Wu, Peihong Han, Wanfen Front Neurosci Neuroscience Due to the demand for sample observation, optical microscopy has become an essential tool in the fields of biology and medicine. In addition, it is impossible to maintain the living sample in focus over long-time observation. Rapid focus prediction which involves moving a microscope stage along a vertical axis to find an optimal focus position, is a critical step for high-quality microscopic imaging of specimens. Current focus prediction algorithms, which are time-consuming, cannot support high frame rate imaging of dynamic living samples, and may introduce phototoxicity and photobleaching on the samples. In this paper, we propose Lightweight Densely Connected with Squeeze-and-Excitation Network (LDSE-NET). The results of the focusing algorithm are demonstrated on a public dataset and a self-built dataset. A complete evaluation system was constructed to compare and analyze the effectiveness of LDSE-NET, BotNet, and ResNet50 models in multi-region and multi-multiplier prediction. Experimental results show that LDSE-NET is reduced to 1E-05 of the root mean square error. The accuracy of the predicted focal length of the image is increased by 1 ~ 2 times. Training time is reduced by 33.3%. Moreover, the volume of the model only reaches the KB level, which has the characteristics of being lightweight. Frontiers Media S.A. 2023-06-29 /pmc/articles/PMC10338878/ /pubmed/37457013 http://dx.doi.org/10.3389/fnins.2023.1213176 Text en Copyright © 2023 Jiang, Ma, Wang, Zhang, Liu, Wang, Wu and Han. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Jiang, Hesong Ma, Li Wang, Xueyuan Zhang, Juan Liu, Yueyue Wang, Dan Wu, Peihong Han, Wanfen Focus prediction of medical microscopic images based on Lightweight Densely Connected with Squeeze-and-Excitation Network |
title | Focus prediction of medical microscopic images based on Lightweight Densely Connected with Squeeze-and-Excitation Network |
title_full | Focus prediction of medical microscopic images based on Lightweight Densely Connected with Squeeze-and-Excitation Network |
title_fullStr | Focus prediction of medical microscopic images based on Lightweight Densely Connected with Squeeze-and-Excitation Network |
title_full_unstemmed | Focus prediction of medical microscopic images based on Lightweight Densely Connected with Squeeze-and-Excitation Network |
title_short | Focus prediction of medical microscopic images based on Lightweight Densely Connected with Squeeze-and-Excitation Network |
title_sort | focus prediction of medical microscopic images based on lightweight densely connected with squeeze-and-excitation network |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10338878/ https://www.ncbi.nlm.nih.gov/pubmed/37457013 http://dx.doi.org/10.3389/fnins.2023.1213176 |
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