Alice Gros

We built our pipeline in Python to leverage its rich ecosystem of scientific computing libraries while keeping the code as accessible and user-friendly as possible. We provide detailed installation instructions and Jupyter notebooks with step-by-step instructions designed to be used by non-specialists, with a focus on ease of use and reproducibility.

Abstract We propose a two-stage supervised framework for characterizing cell divisions in 2D and 3D time-lapse microscopy. First, we recast division detection as a semantic segmentation task on image sequences. Second, a local regression model estimates the orientation and distance between daughter cells for each event. We validate this framework using image sequences of avian neuroepithelium and mouse gastruloids.

Abstract Whole-mount 3D imaging at the cellular scale is a powerful tool for exploring complex processes during morphogenesis. In organoids, it allows examining tissue architecture, cell types, and morphology simultaneously in 3D models. However, cell packing in multilayered organoid tissues hinders both deep imaging and quantification of cell-scale processes.