Workflow

Spine Analyzer allows to semi-automatically segment dendritic spines in 3D+t images and to measure their volumes and the intensities of the signal within in different channels over time.

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.

While a quickly retrained cellpose network (only on xy slices, no need to train on xz or yz slices) is giving good results in 2D, the anisotropy of the SIM image prevents its usage in 3D. Here the workflow consists in applying 2D cellpose segmentation and then using the CellStich libraries to optimize the 3D labelling of objects from the 2D independant labels.

Here the provided notebook is fully compatible with Google Collab and can be run by uploading your own images to your gdrive. A model is provided to be replaced by your own (create by CellPose 2.0)

This workflow describes a deep-learning based pipeline for reliable single-organoid segmentation and tracking in 2D+t high-resolution brightfield microscopy of mouse mammary epithelial organoids. The pipeline involves a four-layer U-Net to infer semantic segmentation predictions, adaptive morphological filtering to establish candidate organoid instances, and a shape-similarity-constrained, instance-segmentation-correcting tracking step to associate the corresponding organoid instances in time.

It is particularly focused on automatically detecting an organoid located approximately in the center of the first frame and track all its subsequent instances in the remaining frames, emphasizing on accurate organoid boundary delineation. Furthermore, segmentation network was trained using plausible pix2pixHD-generated bioimage data. Syntheric image simulator code and data are also available here.