This paper introduces a novel system for the analysis of superresolution microscopy images using a learning based approach boosting performance and simplicity of use. Key component of single-molecule-localisation (SML) microscopy techniques is the ability to localise single emitting molecules in a stack of noisy images with a high degree of accuracy. To this end, we propose a SVM-based detector coupled with a weighted Least Squares fitting approach that favourably compares with the state of the art on established evaluation protocols.
['Silvia Colabrese', 'Marco Castello', 'Giuseppe Vicidomini', 'Alessio Del Bue']
Learning-based approach to boost detection rate and localisation accuracy in single molecule localisation microscopy