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

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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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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