Section: New Results
Retracing and registration for Correlative light-electron microscopy (CLEM)
Participants : Bertha Mayela Toledo Acosta, Patrick Bouthemy, Charles Kervrann.
Correlative light-electron microscopy (CLEM) enables to relate cell dynamics visualized in light microscopy (LM) with cell structure provided by electron microscopy (EM) for a better understanding of cell mechanisms. Registration of LM and EM modalities is then a timely, important but difficult open problem, which still requires some manual assistance. LM and EM images are indeed of very different size, spatial resolution, field of view, and appearance. We have investigated an original automated approach for the retracing-and-registration stage of the overall CLEM workflow (see Fig. 12 ). Pairing between the LM region of interest (ROI) and the corresponding EM patch relies on a common representation for both images, based on the LoG (Laplacian of Gaussian) transform with an adaptive associated scale (or blurring). We exploit histograms of the LoG values or histograms supplied by the LDP (Local Directional Pattern) texture descriptor, with associated histogram distances, to solve the EM patch search issue. The search step supplies a pre-registration, which is refined by the estimation of an affine motion model to overlay the EM image onto the LM image around the ROI. Preliminary results on real CLEM images provided by UMR 144 CNRS-Institut Curie demonstrated the interest and efficiency of the proposed method.
Collaborators: Perrine Paul-Gilloteaux and Xavier Heiligenstein (UMR 144 CNRS-Institut Curie).
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