Python

Track non-dividing particles in 2D time-lapse image.

Execute Nuclei Segmentation in 3D images using pixel classification with ilastik.

U-Net segmentation as presented in Reference Publication. The model predicts three classes: background, edge and foreground. The model was trained with Kaggle Data Science Bowl (DSB) 2018 training set.

Nuclei Segmentation using Deep Learning for individual cell analysis (DeepCell).

Summary

napari is a fast, interactive, multi-dimensional image viewer for Python. It’s designed for browsing, annotating, and analyzing large multi-dimensional images. It’s built on top of Qt (for the GUI), vispy (for performant GPU-based rendering), and the scientific Python stack (e.g. numpy, scipy). It includes critical viewer features out-of-the-box, such as support for large multi-dimensional data, and layering and annotation. By integrating closely with the Python ecosystem, napari can be easily coupled to leading machine learning and image analysis tools (e.g. scikit-image, scikit-learn, TensorFlow, PyTorch), enabling more user-friendly automated analysis.

Installation

• The installation procedure for Silicon Mac (M1 Processor, arm64 ) requires some tricks. As of Oct 2021, this procedure by Peter Sobolewski works but:
  • For installing pyqt5, use a slightly different command `brew install PyQt@5` to install PyQt5.