Pixel classification

Pixel labeling
Voxel classification
Semantic image segmentation

Nuclei Segmentation (ilastik)


NEUBIAS-WG5 workflow for nuclei segmentation using ilastik v1.3.2 and Python post-processing.

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Pixel classification for GlaS challenge with UNet


This workflow segments glands from H&E stained histopathological images
from the Gland Segmentation Challenge (GlaS2015) using deep learning (UNet).
UNet implementation largely inspired from PyTorch-UNet by Milesial. 

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3-D Density Kernel Estimation


3-D density kernel estimation (DKE-3-D) method, utilises an ensemble of random decision trees for counting objects in 3D images. DKE-3-D avoids the problem of discrete object identification and segmentation, common to many existing 3-D counting techniques, and outperforms other methods when quantification of densely packed and heterogeneous objects is desired. 



The MIPAV (Medical Image Processing, Analysis, and Visualization) application enables quantitative analysis and visualization of medical images of numerous modalities such as PET, MRI, CT, or microscopy. Using MIPAV's standard user-interface and analysis tools, researchers at remote sites (via the internet) can easily share research data and analyses, thereby enhancing their ability to research, diagnose, monitor, and treat medical disorders.

ANTs: Advanced Normalization Tools


ANTs computes high-dimensional mappings to capture the statistics of brain structure and function.

Image Registration

Diffeomorphisms: SyN, Independent Evaluation: Klein, Murphy, Template Construction (2004)(2010), Similarity Metrics, Multivariate registration, Multiple modality analysis and statistical bias

Image Segmentation

Atropos Multivar-EM Segmentation (link), Multi-atlas methods (link) and JLF, Bias Correction (link), DiReCT cortical thickness (link), DiReCT in chimpanzees


Advanced Normalization Tools



SuRVoS: Super-Region Volume Segmentation workbench

A volume is first partitioned into Super-Regions (superpixels or supervoxels) and then interactively segmented by the user providing training annotations. SuRVoS can then learn from and extend the annotations to the whole volume.

User interface of SuRVoS showing example annotation on soft x-ray tomography data



This plugin tags all pixel/voxels in a skeleton image and then counts all its junctions, triple and quadruple points and branches, and measures their average and maximum length.


he tags are shown in a new window displaying every tag in a different color. You can find it under [Plugins>Skeleton>Analyze Skeleton (2D/3D)]. See Skeletonize3D for an example of how to produce skeleton images.

The voxels are classified into three different categories depending on their 26 neighbors: - End-point voxels: if they have less than 2 neighbors. - Junction voxels: if they have more than 2 neighbors. - Slab voxels: if they have exactly 2 neighbors.

End-point voxels are displayed in blue, slab voxels in orange and junction voxels in purple.

Notice here that, following this notation, the number of junction voxels can be different from the number of actual junctions since some junction voxels can be neighbors of each other.


Output data type: table result, image of the skeleton

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Pixel Classification using ilastik


This workflow classifies, or segments, the pixels of an image given user annotations. It is especially suited if the objects of interests are visually (brightness, color, texture) distinct from their surrounding. Users can iteratively select pixel features and provide pixel annotations through a live visualization of selected feature values and current prediction responses. Upon users' satisfaction, the workflow then predicts the remaining unprocessed image(s) regions or new images (as batch processing). Users can export (as images of various formats): selected features, annotations, predicted classification probability, simple segmentation, etc. This workflow is often served as one of the first step options for other workflows offered by ilastik, such as object classification, automatic tracking.

Tissue analysis from histological sections


This macro batch processes all the 2D images (tif and jpg files) located in a user defined folder by calling Fiji Weka trainable segmentation to classify each pixel, and reports the areas of each class in a human readable results table. The classifier to be applied to each image should be previously trained on a representative image by an expert and exported to file (Save classifier) into the image folder to be processed.

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