Synonyms
Pixel labeling
Voxel classification
Semantic image segmentation

Yapic

Description

Yet another pixel classifier Yapic is a deep learning tool to :

train your own filter to enhance the structure of your choice 

train multiple filter at once 

it is based on the u-net convolutional filter . 

To train it : annotation can come from example from Ilastik software , tif labelled files can be transferred to yapic. 

Training takes about hours to days , prediction takes seconds once trained .

It can be ran from command line .

note that only 10 to 20 images with sparse labeling are required for efficient training 

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Nuclei Segmentation 3D (Ilastik)

Description

Execute Nuclei Segmentation in 3D images using pixel classification with ilastik.

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Nuclei Segmentation (ilastik)

Description

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

Description

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

Description

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. 

MIPAV

Description

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

Description

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

Description

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