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Spatial statistics and point processes, have been a fundamental field of research for decades. Leading methods in image processing rely on powerful image models/priors, e.g., marked point processes and random sets. They are now especially appropriate to analyze the spatial distribution of proteins or single molecules observed in fluorescence microscopy and super-resolution imaging. Recently, they have been utilized for solving inverse problems in bioimaging (e.g. deconvolution), tracking moving particles or co-localisation in fluorescence microsopy. Machine learning techniques and convolutional neural networks are now investigated to address similar issues. The proposed minisymposium consists of two sessions, covering a series of problems in this field.