Enhancing the data processing speed of a deep-learning-based three-dimensional single molecule localization algorithm (FD-DeepLoc) with a combination of feature compression and pipeline programming
Three-dimensional (3D) single molecule localization microscopy (SMLM) plays an important role in biomedical applications, but its data processing is very complicated. Deep learning is a potential tool to solve this problem. As the state of art 3D super-resolution localization algorithm based on deep...
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| Language: | English |
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World Scientific Publishing
2025-03-01
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| Series: | Journal of Innovative Optical Health Sciences |
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| Online Access: | https://www.worldscientific.com/doi/10.1142/S1793545824500251 |
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| author | Shuhao Guo Jiaxun Lin Yingjun Zhang Zhen-Li Huang |
| author_facet | Shuhao Guo Jiaxun Lin Yingjun Zhang Zhen-Li Huang |
| author_sort | Shuhao Guo |
| collection | DOAJ |
| description | Three-dimensional (3D) single molecule localization microscopy (SMLM) plays an important role in biomedical applications, but its data processing is very complicated. Deep learning is a potential tool to solve this problem. As the state of art 3D super-resolution localization algorithm based on deep learning, FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing, even though it has greatly improved the data processing throughput. In this paper, a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM. This new algorithm uses the feature compression method to reduce the parameters of the model, and combines it with pipeline programming to accelerate the inference process of the deep learning model. The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy, which can realize real-time processing of [Formula: see text] pixels size images. The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering, and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm. |
| format | Article |
| id | doaj-art-c3ffca2196a24a4dac59e0c492b80403 |
| institution | Kabale University |
| issn | 1793-5458 1793-7205 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Journal of Innovative Optical Health Sciences |
| spelling | doaj-art-c3ffca2196a24a4dac59e0c492b804032025-08-20T03:42:02ZengWorld Scientific PublishingJournal of Innovative Optical Health Sciences1793-54581793-72052025-03-01180210.1142/S1793545824500251Enhancing the data processing speed of a deep-learning-based three-dimensional single molecule localization algorithm (FD-DeepLoc) with a combination of feature compression and pipeline programmingShuhao Guo0Jiaxun Lin1Yingjun Zhang2Zhen-Li Huang3Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Sanya 572025, P. R. ChinaKey Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Sanya 572025, P. R. ChinaKey Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Sanya 572025, P. R. ChinaKey Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Sanya 572025, P. R. ChinaThree-dimensional (3D) single molecule localization microscopy (SMLM) plays an important role in biomedical applications, but its data processing is very complicated. Deep learning is a potential tool to solve this problem. As the state of art 3D super-resolution localization algorithm based on deep learning, FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing, even though it has greatly improved the data processing throughput. In this paper, a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM. This new algorithm uses the feature compression method to reduce the parameters of the model, and combines it with pipeline programming to accelerate the inference process of the deep learning model. The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy, which can realize real-time processing of [Formula: see text] pixels size images. The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering, and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.https://www.worldscientific.com/doi/10.1142/S1793545824500251Real-time data processingfeature compressionpipeline programming |
| spellingShingle | Shuhao Guo Jiaxun Lin Yingjun Zhang Zhen-Li Huang Enhancing the data processing speed of a deep-learning-based three-dimensional single molecule localization algorithm (FD-DeepLoc) with a combination of feature compression and pipeline programming Journal of Innovative Optical Health Sciences Real-time data processing feature compression pipeline programming |
| title | Enhancing the data processing speed of a deep-learning-based three-dimensional single molecule localization algorithm (FD-DeepLoc) with a combination of feature compression and pipeline programming |
| title_full | Enhancing the data processing speed of a deep-learning-based three-dimensional single molecule localization algorithm (FD-DeepLoc) with a combination of feature compression and pipeline programming |
| title_fullStr | Enhancing the data processing speed of a deep-learning-based three-dimensional single molecule localization algorithm (FD-DeepLoc) with a combination of feature compression and pipeline programming |
| title_full_unstemmed | Enhancing the data processing speed of a deep-learning-based three-dimensional single molecule localization algorithm (FD-DeepLoc) with a combination of feature compression and pipeline programming |
| title_short | Enhancing the data processing speed of a deep-learning-based three-dimensional single molecule localization algorithm (FD-DeepLoc) with a combination of feature compression and pipeline programming |
| title_sort | enhancing the data processing speed of a deep learning based three dimensional single molecule localization algorithm fd deeploc with a combination of feature compression and pipeline programming |
| topic | Real-time data processing feature compression pipeline programming |
| url | https://www.worldscientific.com/doi/10.1142/S1793545824500251 |
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