  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.
