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Computer Science > Computer Vision and Pattern Recognition
Title: Guided-deconvolution for Correlative Light and Electron Microscopy
(Submitted on 19 Aug 2022)
Abstract: Correlative light and electron microscopy is a powerful tool to study the internal structure of cells. It combines the mutual benefit of correlating light (LM) and electron (EM) microscopy information. However, the classical approach of overlaying LM onto EM images to assign functional to structural information is hampered by the large discrepancy in structural detail visible in the LM images. This paper aims at investigating an optimized approach which we call EM-guided deconvolution. It attempts to automatically assign fluorescence-labelled structures to details visible in the EM image to bridge the gaps in both resolution and specificity between the two imaging modes.
Submission history
From: Rainer Heintzmann [view email][v1] Fri, 19 Aug 2022 17:12:15 GMT (22754kb,D)
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