CeRVIM-IID Seminar: Multiplexing fluorescence microscopy images with multi-dimensional deep networks
Catherine Bouchard
Laboratoire de Vision et Systèmes Numériques, LVSN, U. Laval
Laboratoire de Flavie Lavoie-Cardinal, FLC-Lab, U. Laval
Friday, March 15, 2024, 1:30 p.m., PLT-3904
Abstract
Studying the complex interactions between all proteins involved in biological processes requires imaging simultaneously as many targets as possible. Fluorescence lifetime imaging (FLIM) measures the delay between the excitation and the emission of each photon to help discern its emitter, resulting in a multi-color image from a single acquisition. The algorithms developed for this assignment are generally applied pixel-by-pixel, using one dimension (the distribution of the measured time delay), and therefore do not exploit a valuable source of information that spreads across multiple pixels: the spatial organization of the proteins. We developed a method that exploits a multi-dimensional deep neural network that processes simultaneously all dimensions of the image (temporal and spatial) to better assign an emitter to each photon for FLIM images. This method proves to be more accurate than pixel-by-pixel methods for cases where photons are limited, like in super-resolution imaging of living cells, because it simultaneously uses the spatial features of the image with the time information. It can additionally serve as an unsupervised denoising method, further enhancing its performance for low-noise images. The method can be trained on partially simulated images and applied to real acquisitions, enabling its application for the many experimental cases where training datasets can not be acquired.
The presentation will be given in English and the slides will be in English.