Deep learning detection of exocytosis events in TIRF microscopy
Year: 2025
Type: conference-paper
Abstract: Segmentation and detection of biological objects in fluorescence microscopy is of paramount importance in cell imaging. Deep learning approaches have recently shown promise to advance, automatize and accelerate analysis. However, most of the interest has been given to the segmentation of static objects of 2D/3D images whereas the segmentation of dynamic processes obtained from time-lapse acquisitions has been less explored. In [1], we adapted DeepFinder [2], a U-net originally designed for 3D noisy cryo-electron tomography (cryo-ET) data, for the detection of rare dynamic exocytosis events (termed ExoDeepFinder) observed in temporal series of 2D Total Internal Reflection Fluorescent Microscopy (TIRFM) images. ExoDeepFinder achieved good absolute performances with a relatively small training dataset of 60 cells/~12000 events. We rigorously compared deep learning performances with unsupervised conventional methods from the literature [1]. ExoDeepFinder outcompeted the tested methods, but also exhibited a greater plasticity to the experimental conditions when tested under drug treatments and after changes in cell line or imaged reporter. This robustness to unseen experimental conditions did not require re-training demonstrating generalization capability of ExoDeepFinder. ExoDeepFinder, as well as the annotated training datasets, were made transparent and available through an open-source software as well as a Napari plugin and can directly be applied to custom user data. The apparent plasticity and performances of ExoDeepFinder to detect dynamic events open new opportunities for future deep-learning guided analysis of dynamic processes in live-cell imaging.
Source: HAL
Institutions: Université Paris Cité, Institut Jacques Monod, Institut national de recherche en sciences et technologies du numérique, SAIRPICO: Imagerie Spatio-Temporelle, Intelligence Artificielle et Calcul Numérique pour la Biologie Cellulaire et Chemobiologie, Université de Rennes, Université Paris-Saclay
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7205491647