Component

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.

HistoMetrix is an advanced histology analysis software designed to simplify image processing and analysis for biologists and pathologists. Powered by the most advanced deep learning technology, HistoMetrix enables you to effortlessly uncover valuable insights and visualize results without the need for extensive technical expertise. Let’s explore the key features that make HistoMetrix the ultimate solution for histology analysis.

HistoMetrix leverages cutting-edge deep learning technology to streamline and simplify image processing for histology analysis. You can easily navigate through complex datasets, detect and analyze tissue structures, and extract meaningful information with just a few clicks. Research use only.

HistoMetrix combines advanced deep learning technology with cost-effectiveness, making it the ideal histology analysis software for biologists and pathologists. With HistoMetrix, effortlessly uncover valuable insights, visualize results, and simplify image processing, all while enjoying significant cost savings compared to other solutions. Benefit from affordable pricing plans, no expensive hardware requirements, and no need for costly training programs. HistoMetrix streamlines workflows, automates tasks, and provides efficient analysis tools, allowing you to save valuable time and resources. Experience the cost-effective solution for histology analysis and accelerate your research with HistoMetrix.

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).

Image segmentation and object detection performance measures

The goal of this package is to provide easy-to-use tools for evaluation of the performance of segmentation methods in biomedical image analysis and beyond, and to fasciliate the comparison of different methods by providing standardized implementations. This package currently only supports 2-D image data.