2D

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

MATLAB app to characterize nanoparticles imaged with super-resolution microscopy. nanoFeatures will read text and csv files from the NIKON and ONI microscopes and from the ThunderSTORM Fiji plugin, then cluster the localizations and filter by size and sphericity and finally output nanoparticle features like size, aspect ratio, and number of localizations per cluster (total and for each channel).

These are commands that create or process binary (black and white) images. Typical morphological operations/functions can be found here.

ELEPHANT is a platform for 3D cell tracking, based on incremental and interactive deep learning.
It implements a client-server architecture. The server is built as a web application that serves deep learning-based algorithms. The client application is implemented by extending Mastodon, providing a user interface for annotation, proofreading and visualization.

ZeroCostDL4Mic: exploiting Google Colab to develop a free and open-source toolbox for Deep-Learning in microscopy

ZeroCostDL4Mic is a collection of self-explanatory Jupyter Notebooks for Google Colab that features an easy-to-use graphical user interface. They are meant to quickly get you started on learning to use deep-learning for microscopy.