Scalable and lightweight deep learning for efficient high accuracy single-molecule localization microscopy
Abstract Deep learning has significantly improved the performance of single-molecule localization microscopy (SMLM), but many existing methods remain computationally intensive, limiting their applicability in high-throughput settings. To address these challenges, we present LiteLoc, a scalable analy...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-62662-5 |
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| _version_ | 1849234647073947648 |
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| author | Yue Fei Shuang Fu Wei Shi Ke Fang Ruixiong Wang Tianlun Zhang Yiming Li |
| author_facet | Yue Fei Shuang Fu Wei Shi Ke Fang Ruixiong Wang Tianlun Zhang Yiming Li |
| author_sort | Yue Fei |
| collection | DOAJ |
| description | Abstract Deep learning has significantly improved the performance of single-molecule localization microscopy (SMLM), but many existing methods remain computationally intensive, limiting their applicability in high-throughput settings. To address these challenges, we present LiteLoc, a scalable analysis framework for high-throughput SMLM data analysis. LiteLoc employs a lightweight neural network architecture and integrates parallel processing across central processing unit (CPU) and graphics processing unit (GPU) resources to reduce latency and energy consumption without sacrificing localization accuracy. LiteLoc demonstrates substantial gains in processing speed and resource efficiency, making it an effective and scalable tool for routine SMLM workflows in biological research. |
| format | Article |
| id | doaj-art-b9684305da824744a2d126cbc2a465f2 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-b9684305da824744a2d126cbc2a465f22025-08-20T04:03:06ZengNature PortfolioNature Communications2041-17232025-08-011611910.1038/s41467-025-62662-5Scalable and lightweight deep learning for efficient high accuracy single-molecule localization microscopyYue Fei0Shuang Fu1Wei Shi2Ke Fang3Ruixiong Wang4Tianlun Zhang5Yiming Li6Department of Biomedical Engineering, Southern University of Science and TechnologyDepartment of Biomedical Engineering, Southern University of Science and TechnologyDepartment of Biomedical Engineering, Southern University of Science and TechnologyDepartment of Biomedical Engineering, Southern University of Science and TechnologyDepartment of Biomedical Engineering, Southern University of Science and TechnologyDepartment of Biomedical Engineering, Southern University of Science and TechnologyDepartment of Biomedical Engineering, Southern University of Science and TechnologyAbstract Deep learning has significantly improved the performance of single-molecule localization microscopy (SMLM), but many existing methods remain computationally intensive, limiting their applicability in high-throughput settings. To address these challenges, we present LiteLoc, a scalable analysis framework for high-throughput SMLM data analysis. LiteLoc employs a lightweight neural network architecture and integrates parallel processing across central processing unit (CPU) and graphics processing unit (GPU) resources to reduce latency and energy consumption without sacrificing localization accuracy. LiteLoc demonstrates substantial gains in processing speed and resource efficiency, making it an effective and scalable tool for routine SMLM workflows in biological research.https://doi.org/10.1038/s41467-025-62662-5 |
| spellingShingle | Yue Fei Shuang Fu Wei Shi Ke Fang Ruixiong Wang Tianlun Zhang Yiming Li Scalable and lightweight deep learning for efficient high accuracy single-molecule localization microscopy Nature Communications |
| title | Scalable and lightweight deep learning for efficient high accuracy single-molecule localization microscopy |
| title_full | Scalable and lightweight deep learning for efficient high accuracy single-molecule localization microscopy |
| title_fullStr | Scalable and lightweight deep learning for efficient high accuracy single-molecule localization microscopy |
| title_full_unstemmed | Scalable and lightweight deep learning for efficient high accuracy single-molecule localization microscopy |
| title_short | Scalable and lightweight deep learning for efficient high accuracy single-molecule localization microscopy |
| title_sort | scalable and lightweight deep learning for efficient high accuracy single molecule localization microscopy |
| url | https://doi.org/10.1038/s41467-025-62662-5 |
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