Framework for Accurate Single-Molecule Spectroscopic Imaging Analyses Using Monte Carlo Simulation and Deep Learning.

ABSTRACT: Accurate single-molecule spectral imaging denoising and analysis are essential for advancing high-throughput single-molecule spectroscopy and spectrally resolved super-resolution microscopy. However, a standardized framework for guiding the accurate analysis of single-molecule spectral data remains unavailable. To address this, we developed an analysis framework that generates ground truth (GT) single-molecule spectral imaging data using Monte Carlo simulations, enabling the first supervised learning-based imaging denoising method (referred to as SpecUNet) for single-molecule spectral images. Within this framework, we established eight comprehensive evaluation metrics to systematically compare the performance of SpecUNet against existing imaging analytics using synthetic GT data. We furthe

SUBMITTER: Mao H 

PROVIDER: S-EPMC12352114 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature