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.
翻译:测量通过荧光显微镜测量的分子数量是细胞生物学和医学研究的一个重要主题。在这项工作中,我们提出一个连续的超分辨率扫描显微镜算法,该算法在自动生成的图像部分中提供分子计数,并以无光度信任间隔的形式提供统计保证。为此,我们首先对样本的STED显微镜测量采用多级扫描程序,以获得一个重要区域的系统,其中每个区域至少包含一个有规定统一概率的分子。这个区域系统通常非常冗余,由复方形建筑块组成。要选择一个由更自然形状区域组成的信息丰富但非冗余的子集,我们通过通用分解算法将我们的系统混合起来。这些部分的直径可以按显微镜的分辨率排序。利用对同样的相光度巧合测量,然后我们能够估计分子的亮度和数量,并对先前构建的分子数进行统一的信任度间隔。换句话说,我们用一个通用的分子运算算法来将我们的系统混合起来。