Cell segmentation

Fiji plugin to segment oocyte and zona pellucida contours from transmitted light images and extract hundreds of morphological features to describe numerically the oocyte. Segmentation is based on trained neural networks (U-Net) that were trained on both mouse and human oocytes (in prophase and meiosis I) acquired in different conditions. They are freely avaialable on the github repository and can be retrained if necessary. Oocytor also have options to extract hundreds of morphological/intensity features to characterize manually the oocyte (eg perimeter, texture...). These features can also be used in machine learning pipeline for automatic phenotyping.

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)

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).

SuperDSM is a globally optimal segmentation method based on superadditivity and deformable shape models for cell nuclei in fluorescence microscopy images and beyond.

BiaPy is an open source Python library for building bioimage analysis pipelines. 

BiaPy contains ready-to-use solutions for the tasks of semantic segmentation, instance segmentation, object detection, image denoising, single image super-resolution, self-supervised learning and image classification.