MICROTUBULE TRACKING FROM STOCHASTIC OPTICAL RECONSTRUCTION MICROSCOPY IMAGES

Our work aims at using quantitative imaging tools to com-plement the limitation of noise encountered by high reso-lution fluorescence microscopy methods. Several cycles of fluorophore activation, imaging and deactivation pro-duce a sequence of images in which the signals of indi-vidual fluorophores do not overlap, due to the low light intensity during their activation. The centroid position of each fluorophore is then determined by Gaussian fitting of each signal, where the final resolution depends on the precision with which each fluorphore is localized. Super-imposing the images will result in having the same fluo-rophore mapped onto a ‘cloud ’ of locations. The most sig-nificant information of the superimposed images is con-tained in the macro-structures identifying microtubules, mitochondria or other organelles. Cascades of binary im-age processing algorithms are applied in order to isolate the larger organelles. A Markovian algorithm selecting the nearest neighbour is finally applied to the de-noised images, to automatically extract relevant information on microtubules. Our work supplements advancements in experimental technologies with computational methods, helping quantifying sub-cellular properties with high ac-curacy. 1