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

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Publicado en:Nature Communications vol. 16, no. 1 (2025), p. 7217-7226
Autor principal: Fei, Yue
Otros Autores: Fu, Shuang, Shi, Wei, Fang, Ke, Wang, Ruixiong, Zhang, Tianlun, Li, Yiming
Publicado:
Nature Publishing Group
Materias:
Microscopy
Parallel processing
Central processing units > CPUs
Accuracy
Deep learning
Data analysis
Biological research
Localization
Machine learning
Performance evaluation
Efficiency
Energy consumption
Neural networks
Graphics processing units
Network latency
Algorithms
Latency
Real time
Economic
Acceso en línea:Citation/Abstract
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Resumen: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.This study presents LiteLoc, a lightweight and scalable AI model for efficient and accurate single molecule localization microscopy data analysis, bringing real-time deep-learning-based analysis to the era of high throughput super resolution imaging.
ISSN:2041-1723
DOI:10.1038/s41467-025-62662-5
Fuente:Health & Medical Collection