Component

Machine Learning made easy

APEER ML provides an easy way to train your own machine learning
models and segment your microscopy images. No expertise or coding required.

This Fiji plugin is a python script for CLEM registration using deep learning, but it could be applied in principle to other modalities. The pretrained model was learned on chromatin SEM images and fluorescent staining, but a script is also provided to train an new model, based on CSBDeep. The registration is then performed as a feature based registration, using register virtual stack plugin (which extract features and then perform RANSAc. Editing the script in python gives access to more option (such as the transformation model to be used, similarity by default. Images need to be prepared such that they contain only one channel, but channel of ineterst (to be transformed with the same transformation) can be given as input, and Transform Virtual Stack plugin can be used as well.

Local Z Projector is an ImageJ2 plugin, available in Fiji, that can perform local-Z projection of a 3D stack, possibly over time, possibly very large.

LZP performs projection of a surface of interest on a 2D plane from a 3D image. It is a simple tool that focuses on usability and is designed to be adaptable to many different use cases and image quality.

• It can work with 3D movies over time with multiple channels.
• It can work with images much larger than available RAM out of the box.
• It takes advantage of computers with multiple cores, and can be used in scripts.

Viv is a JavaScript library providing utilities for rendering primary imaging data. Viv supports WebGL-based multi-channel rendering of both pyramidal and non-pyramidal images. The rendering components of Viv are provided as Deck.gl layers, facilitating image composition with existing layers and updating rendering properties within a reactive paradigm.