Abstract
Single molecule localisation (SML) microscopy is a fundamental tool for biological discoveries; it provides sub-diffraction spatial resolution images by detecting and localizing "all" the fluorescent molecules labeling the structure of interest. For this reason, the effective resolution of SML microscopy strictly depends on the algorithm used to detect and localize the single molecules from the series of microscopy frames. To adapt to the different imaging conditions that can occur in a SML experiment, all current localisation algorithms request, from the microscopy users, the choice of different parameters. This choice is not always easy and their wrong selection can lead to poor performance. Here we overcome this weakness with the use of machine learning. We propose a parameter-free pipeline for SML learning based on support vector machine (SVM). This strategy requires a short supervised training that consists in selecting by the user few fluorescent molecules (∼ 10-20) from the frames under analysis. The algorithm has been extensively tested on both synthetic and real acquisitions. Results are qualitatively and quantitatively consistent with the state of the art in SML microscopy and demonstrate that the introduction of machine learning can lead to a new class of algorithms competitive and conceived from the user point of view.
Highlights
Single molecule localisation (SML) microscopy is an invaluable tool for biological discoveries [1, 2], its success in obtaining a super-resolved image depends on the optimization of several parameters, both from the experimental and image processing sides [3, 4]
With SML microscopy (e.g. PALM [5] and STORM [6]) fluorescent molecules are not active all together, they are activated at random space and time, with the aim to gather a stack of low resolution images made of few separated molecules each
The evaluation was purely quantitative and it was performed on the synthetic datasets of 2013 SML microscopy Challenge [22]
Summary
Single molecule localisation (SML) microscopy is an invaluable tool for biological discoveries [1, 2], its success in obtaining a super-resolved (or nanoscopy) image depends on the optimization of several parameters, both from the experimental and image processing sides [3, 4]. A SML algorithm achieved super-resolution in two main steps: a first phase of detection, or segmentation, where fluorescent molecules are localised at a pixel level, and a subsequent position refinement through fitting algorithms or faster and less accurate techniques, like centroids. These two phases are typically preceded by low-pass or band-pass filters in order to lower spurious signals contribution. Despite such consolidated pipeline, the detection step is often made of heuristics e.g. the user is asked to select a threshold to discriminate active molecules based on image intensity, signal-to-noise ratio or the number of collected photons. The first effect has been experimentally demonstrated by [8] and it is in agreement with the Nyquist sampling theorem [9]; the second is due to background mistaken for active molecule, whenever the threshold is locally too low
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