morphological phenotype

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.

This toolbox is a unique collection of workflow templates for high-throughput screening. 4 different workflow templates are presented in the documentation linked below.

..A toolbox for high-throughput screening of image-based Caenorhabditis elegans phenotypes. The image analysis algorithms measure morphological phenotypes in individual worms and are effective for a variety of assays and imaging systems. This WormToolbox is available through the open-source CellProfiler project and enables objective scoring of whole-worm high-throughput image-based assays of C. elegans for the study of diverse biological pathways that are relevant to human disease.