Scalable and lightweight deep learning for efficient high accuracy single-molecule localization microscopy

Yue Fei, Shuang Fu, Wei Shi, Ke Fang, Ruixiong Wang, Tianlun Zhang and Yiming Li ()
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Yue Fei: Southern University of Science and Technology
Shuang Fu: Southern University of Science and Technology
Wei Shi: Southern University of Science and Technology
Ke Fang: Southern University of Science and Technology
Ruixiong Wang: Southern University of Science and Technology
Tianlun Zhang: Southern University of Science and Technology
Yiming Li: Southern University of Science and Technology

Nature Communications, 2025, vol. 16, issue 1, 1-9

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-62662-5

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