tubulin

ZeroCostDL4Mic: exploiting Google Colab to develop a free and open-source toolbox for Deep-Learning in microscopy

ZeroCostDL4Mic is a collection of self-explanatory Jupyter Notebooks for Google Colab that features an easy-to-use graphical user interface. They are meant to quickly get you started on learning to use deep-learning for microscopy. 

Super-resolution optical fluctuation imaging (SOFI) achieves 3D super-resolution by computing temporal cumulants or spatio-temporal cross-cumulants of stochastically blinking fluorophores. In contrast to localization microscopy, SOFI is compatible with weakly emitting fluorophores and a wider range of blinking conditions. Balanced SOFI analyses several cumulant orders for extracting molecular parameter maps, such as the bright and dark state lifetimes, the concentration and the brightness distributions of fluorophores within biological samples. In combination with a deconvolution of the cumulant images, the estimated parameter maps proved useful to balance the image contrast and to linearize the brightness and blinking response. Thereby, the image quality and the resolution were improved significantly.

A collection for tracking microtubule dynamics, written in Python.