Image segmentation

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

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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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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Nuclei Segmentation 3D (ImageJ)

Description

The macro will segment nuclei and separate clustered nuclei in a 3D image using a binary watershed. As a result an index-mask image is written for each input image.

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3D ImageJ Suite

Description

This suite provides plugins to enhance 3D capabilities of ImageJ.

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Nuclei Segmentation 2D (ImageJ)

Description

The macro will segment nuclei and separate clustered nuclei using a binary watershed. As a result an index-mask image is written for each input image.

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