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

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

pyImSegm

Description

Collection of several basic standard image segmentation methods focusing on medical imaging. In particular, the key block/applications are (un)supervised image segmentation using superpixels, object centre detection and region growing with a shape prior. Besides the open-source code, there is also a few sample images.

 

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Nuclei Segmentation (CellProfiler)

Description

This workflow processes a group of images containing cells with discernible nuclei and segments the nuclei and outputs a binary mask that show where nuclei were detected. It was developed as a test workflow for Neubias BIAFLOWS Benchmarking tool.

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Thresholder (ImageJ)

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