Change detection methods in fluorescence live cell imaging for sub-cellular trafficking analysis

International audience; The study of membrane plasticity and the role of molecular "machines" in the control of biogenesis of the endo-cellular membranes have highlighted the crucial role of the "Rab" GTPases family as organizing centers of functional molecular platforms. Yet, to understand the regulation and coordination of these molecular assemblies, which are responsible for intracellular dynamic architectures, a more global vision, the development and the correlation of approaches at different spatial and temporal scales are needed. Considering the "fickle" nature of such dynamic architectures, the current performance of image acquisition systems and the analytical tools at our disposal, many technological challenges must be overcome. Dynamic aspects of perspectives described above require conceptual developments, particularly in the field of microscopy imaging. Moreover, to extract maximum information on the same sample, the development of an adapted microscopy, correlating different modalities, is needed. Last but not least, accurate image descriptors, allowing automatic detection and classification of molecular behavior in space and time, are indispensable. In this talk, we will focus on unsupervised change detection algorithms and new image modeling able to capture spatio-temporal regularities and geometries present in an image pair. In contrast to the usual pixel-wise methods and Markov Random Fields methods, we propose a patch-based formulation for modeling semi-local interactions and detecting local or regional changes in a microscopy image pair. By introducing dissimilarity measures to compare patches and binary local decisions, we design collaborative decision rules that use the total number of detections made by individual neighboring pixels, for different patch sizes. First, we will describe the patch-based representation for image pair analysis and present collaborative decision rules in neighborhoods. In addition, we will present the algorithm used to fuse binary decisions with statistical tests, at different spatial scales. Experimental results in video-microscopy (TIRF and wide-field imaging) demonstrate that the detection algorithm (with no optical flow computation) performs well at detecting meaningful changes and appearing/disappearing spots at the cell membrane. We also illustrate the approach for probabilistic local and global colocalization analysis of molecules in dual-color confocal images.