Component

Crop 2D/3D images with arbitrary box size and orientation(s).

The RotCrop Plugins for ImageJ allow to perform rotated crop of 2D/3D images. In fully manual versions, the crop can be defined based on crop dimensions, crop center, and rotation angle(s) of the crop frame (in 3D, three angles are necessary).

Additional plugins provides an estimate of the rotation based on the gradient vector computed around the crop frame center. The gradient direction is used as vertical direction in the result, making it easier to generate 2D/3D images that are tangent to a surface such as the epidermis of an organ or organism.

A collection of Image Processing and Analysis (IPA) functions used at the Facility for Advanced Imaging and Microscopy (FAIM).

DeXtrusion is a machine learning based python pipeline to detect cell extrusions in epithelial tissues movies. It can also detect cell divisions and SOPs, and can easily be trained to detect other dynamic events.

DeXtrusion takes as input a movie of an epithelium and outputs the spatio-temporal location of cell extrusion events or other event as cell divisions. The movie is discretized into small overlapping rolling windows which are individually classified for event detection by a trained neural network. Results are then put together in event probability map for the whole movie or as spatio-temporal points indicating each event.

CellStich proposes a set of tools for 3D segmentation from 2D segmentation: it reassembles 2D labels obtained from cell in slices in unique 3D labels across slices. It isparticularly robust to anisotropy, and is the ideal companion to cellpose 2D models or other 2D deep learning based models. One could also think about using it for cell tracking by overlap (using time as a third dimension).