Advanced Normalization Tools
ANTs computes high-dimensional mappings to capture the statistics of brain structure and function. See the FAQ page.
ANTs allows one to organize, visualize and statistically explore large biomedical image sets.
ANTs integrates imaging modalities and related information in space and time.
ANTs works across species or organ systems with minimal customization.
ANTs and related tools have won several international and unbiased competitions.
ANTsR is the underlying statistical workhorse.
Questions: Discussion Site or new ANTsDoc or try this version ... also read our guide to evaluation strategies and addressing new problems with ANTs or other software. New ANTs handout, part of forthcoming ANTs tutorial material.
ANTs Dashboard thanks to Arman Eshaghi and Hans J. Johnson
ANTs extracts information from complex datasets that include imaging (Word Cloud). Paired with ANTsR (answer), ANTs is useful for managing, interpreting and visualizing multidimensional data. ANTs is popularly considered a state-of-the-art medical image registration and segmentation toolkit. ANTsR is an emerging tool supporting standardized multimodality image analysis. ANTs depends on the Insight ToolKit (ITK), a widely used medical image processing library to which ANTs developers contribute. A summary of some ANTs findings and tutorial material (most of which is on this page) is here.
Role: Creator, Algorithm Design, Implementation, more
Role: Compeller, Algorithm Design, Implementation Guru, more
Role: Large-Scale Application, Testing, Software design
Core: Gang Song (Originator), Philip A. Cook, Jeffrey T. Duda (DTI), Ben M. Kandel (Perfusion, multivariate analysis)
morphology, GetLargestComponent, CCA, FillHoles ... much more!
Visualization: e.g. a gource of ANTs development
ANTs redesigned for generality, automation, multi-core computation with ITKv4
Dev'd ITKv4 with Kitware, GE, Natl. Lib of Medicine & Academia
ANTs finished in 1st rank in Klein 2009 intl. brain mapping competition
ANTs finished 1st overall in EMPIRE10 intl. lung mapping competition
ANTs is the standard registration for MICCAI-2013 segmentation competitions
Conducting ANTs-based R tutorial @ MICCAI-2013
ITK-focused Frontiers in Neuroinformatics research topic here
Won the best paper award at the STACOM 2014 challenge
antsRegistration bash example
ANTs and ITK paper
Pre-built ANTs templates with spatial priors download
The ANTs Cortical Thickness Pipeline example
"Cooking" tissue priors for templates example (after you build your template)
Basic Brain Mapping example
Large deformation example
Template construction example
Point-set mapping which includes the PSE metric and affine and deformable registration with (labeled) pointsets or iterative closest point
Feature matching example ... not up to date ...
Chimpanzee cortical thickness example
Global optimization example
fMRI or Motion Correction example
fMRI reproducibility example
fMRI prediction example ... WIP ...
Brain extraction example
N4 bias correction <-> segmentation example
Cortical thickness example
Lung and lobe estimation example
Bibliography bibtex of ANTs-related papers
ANTs google scholar page
Presentations: e.g. a Prezi about ANTs (WIP)
Reproducible science as a teaching tool: e.g. compilable ANTs tutorial (WIP)
Other examples slideshow
Landmark-based mapping for e.g. hippocampus discussed here
Brief ANTs segmentation video
Here is some boilerplate regarding ants image processing:
We will analyze multiple modality neuroimaging data with Advanced Normalization Tools (ANTs) version >= 2.1 1. ANTs has proven performance in lifespan analyses of brain morphology  and function  in both adult  and pediatric brain data [2,5,6] including infants . ANTs employs both probabilistic tissue segmentation (via Atropos ) and machine learning methods based on expert labeled data (via joint label fusion ) in order to maximize reliability and consistency of multiple modality image segmentation. These methods allow detailed extraction of critical image-based biomarkers such as volumes (e.g. hippocampus and amygdala), cortical thickness and area and connectivity metrics derived from structural white matter  or functional connectivity . Critically, all ANTs components are capable of leveraging multivariate image features as well as expert knowledge in order to learn the best segmentation strategy available for each individual image [3,4]. This flexibility in segmentation and the underlying high-performance normalization methods have been validated by winning several internationally recognized medical image processing challenges conducted within the premier conferences within the field and published in several accompanying articles .
ANTs was supported by: R01-EB006266-01 and by K01-ES025432-01