Scalable and lightweight deep learning for efficient high accuracy single-molecule localization microscopy.
basic_science · Level V
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- Record sourced from PubMed, PMID 40764305.
- Also identified by DOI 10.1038/s41467-025-62662-5 and PMC identifier 12325636.
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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.