Spatial statistics in microscopy imagingMS66

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.

Fri 08 June at 09:30 Matemates (Matemates, floor 0)
GcoPS: a fast automatic colocalization method for 3D live cell imaging and super-resolution microscopy
Charles Kervrann (Inria)
Marked point processes for detecting objects in microscopy
Descombes Xavier (Inria Sophia-Antipolis)
Spatial statistics extends co-localization analysis to non-local interaction analysis
Ivo Sbalzarini (TU Dresden / Max Planck Insitute of Molecular Cell Biology and Genetics)
Spatial patterns in large-scale bioimaging data: applications in spatial transcriptomics and phenomics
Walter Thomas (CBIO, Mines ParisTech)
Charles Kervrann (Inria)
bayesian methods, deep learning, detection, image registration, image representation, image segmentation, inverse problems, machine learning, segmentation, spatial statistics, stochastic geometry, stochastic processes