One-click image reconstruction in single-molecule localization microscopy via deep learning.

ABSTRACT: Deep neural networks have led to significant advancements in microscopy image generation and analysis. In single-molecule localization based super-resolution microscopy, neural networks are capable of predicting fluorophore positions from high-density emitter data, thus reducing acquisition time, and increasing imaging throughput. However, neural network-based solutions in localization microscopy require intensive human intervention and computation expertise to address the compromise between model performance and its generalization. For example, researchers manually tune parameters to generate training images that are similar to their experimental data; thus, for every change in the experimental conditions, a new training set should be manually tuned, and a new model should be trained. Her

SUBMITTER: Saguy A 

PROVIDER: S-EPMC12080944 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature