Image segmentation is (one of) the (few) concept(s) on the border between Image (pre)processing (Image->Image) and Image analysis (Image->Data).

ImageM

Description

ImageM integrates into a GUI several algorithms for interactive image processing and analysis. Interface is largely inspired from the open source software "ImageJ".

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StarDist - ImageJ

Description

This is the ImageJ/Fiji plugin for StarDist, a cell/nuclei detection method for microscopy images with star-convex shape priors ( typically for Dapi like staining of nuclei). The plugin can be used to apply already trained models to new images.

Stardist

VAST Lite

Description

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.

NucleusJ

Description

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Starting from image stacks, the nuclear boundary as well as nuclear bodies are segmented. As output, NucleusJ automatically measures 15 parameters quantifying shape and size of nuclei as well as intra-nuclear objects and the positioning of the objects within the nuclear volume.

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LBADSA

Description

LBADSA is based on the fitting of the Young-Laplace equation to the image data to measure drops.

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DragonFly

Description

Dragonfly is a software platform for the intuitive inspection of multi-scale multi-modality image data. Its user-friendly experience translates into powerful quantitative findings with high-impact visuals, driven by nuanced easy-to-learn controls.

For segmentation: It provides an engine fior machine Learning, Watershed and superpixel methods, support histological data .

It offers a 3D viewer, and python scripting capacities .

It is free for reserach use, but not for commercial usage.

DragonFly

Mask-RCNN

Description

This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone.

Nuclei Segmentation (Mask-RCNN)

Description

NEUBIAS-WG5 workflow for nuclei segmentation using Mask-RCNN. The workflow uses Matterport Mask-RCNN. Keras implementation. The model was trained with Kaggle 2018 Data Science Bowl images.

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jSLIC

Description

jSLIC superpixels - is a segmentation method for clustering similar regions - superpixels - in the given image which are usually used for other segmentation techniques. The only two parameters are average (initial) size of each superpixel and rigidity parameter in range (0,1)

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superpixels - ROI