Cell segmentation

DeepImageJ is a user-friendly plugin that enables the use of a variety of pre-trained deep learning models in ImageJ and Fiji. The plugin bridges the gap between deep learning and standard life-science applications. DeepImageJ runs image-to-image operations on a standard CPU-based computer and does not require any deep learning expertise.

Training developper constructs and upload trained model, and made them available to users.

Models are available in a repository here.

It is macro recordable. It is advised to use DeepImageJ on a computer with GPU (CPU will likely be 20x slower)

VAST (Volume Annotation and Segmentation Tool) is a utility application for manual annotation of large EM stacks.

General labeling tool, used for a large variety of 3D data sets; electron-microscopic, multi-channel light-microscopic, and Micro-CT data sets as well as videos, and annotating arbitrary structures, regions and locations, depending on the user’s needs.

The macro will segment nuclei and separate clustered nuclei in a 3D image using a 2D Gaussian blur, followed by Thresholding, 2D hole filling and a 2D watershed. As a result an index-mask image is written for each input image.

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).