Towards quantitative super-resolution microscopy: Molecular maps with statistical guarantees

TL;DR

This paper introduces a new algorithm for super-resolution microscopy that quantifies molecule counts with statistical guarantees, producing molecular maps with confidence intervals from STED microscopy data.

Contribution

The work presents a novel consecutive algorithm combining multiscale scanning and segmentation to provide molecule counts with asymptotic confidence intervals in super-resolution microscopy.

Findings

Algorithm performs well on simulated data.

Provides statistically guaranteed molecule counts.

Produces detailed molecular maps with error control.

Abstract

Quantifying the number of molecules from fluorescence microscopy measurements is an important topic in cell biology and medical research. In this work, we present a consecutive algorithm for super-resolution (STED) scanning microscopy that provides molecule counts in automatically generated image segments and offers statistical guarantees in form of asymptotic confidence intervals. To this end, we first apply a multiscale scanning procedure on STED microscopy measurements of the sample to obtain a system of significant regions, each of which contains at least one molecule with prescribed uniform probability. This system of regions will typically be highly redundant and consists of rectangular building blocks. To choose an informative but non-redundant subset of more naturally shaped regions, we hybridize our system with the result of a generic segmentation algorithm. The diameter of the segments can be of the order of the resolution of the microscope. Using multiple photon coincidence measurements of the same sample in confocal mode, we are then able to estimate the brightness and number of the molecules and give uniform confidence intervals on the molecule counts for each previously constructed segment. In other words, we establish a so-called molecular map with uniform error control. The performance of the algorithm is investigated on simulated and real data.