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Advanced Normalization Tools

Build Status ANTsX Contributor Covenant

ANTs computes high-dimensional mappings to capture the statistics of brain structure and function. See the collection of examples at this page.

ants template

ANTs allows one to organize, visualize and statistically explore large biomedical image sets.

ants render

ANTs integrates imaging modalities and related information in space and time.

ants render

ANTs works across species or organ systems with minimal customization.

ants primate

ANTs and related tools have won several international and unbiased competitions.

ants competes

ANTsR is the underlying statistical workhorse. ANTsR examples here.

ANTsPy is pythonic ANTs/ANTsR. See this content too.

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.

The ANTs handout, part of forthcoming ANTs tutorial material here and here.

ANTsTalk - subject to change at any moment

ANTsRegistrationTalk - subject to change at any moment

Install ANTs via pre-built: Packages @ github older versions @ sourceforge ... also, Github Releases are here thanks to Arman Eshaghi. You can also run ANTs Cortical Thickness pipeline in the cloud using the free http://OpenNeuro.org platform (no installation required).

Build ANTs from: Source-Code (recommended) on Linux / Mac OS or Windows.

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.

Authors

Brian B. Avants - UPENN

Role: Creator, Algorithm Design, Implementation, more

Nicholas J. Tustison - UVA

Role: Compeller, Algorithm Design, Implementation Guru, more

Hans J. Johnson - UIowa

Role: Large-Scale Application, Testing, Software design

Team Members

Core: Gang Song (Originator), Philip A. Cook, Jeffrey T. Duda (DTI), Ben M. Kandel (Perfusion, multivariate analysis)

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

Multivariate Analysis Eigenanatomy (1) (2)

Prior-Based Eigenanatomy (in prep), Sparse CCA (1), (2), Sparse Regression (link)

ImageMath Useful!

morphology, GetLargestComponent, CCA, FillHoles ... much more!

Application Domains

Frontotemporal degeneration PENN FTD center

Multimodality Neuroimaging

Lung Imaging

  • Structure
  • Perfusion MRI
  • Branching

Multiple sclerosis (lesion filling) example

Background & Theory

ANTs has won several unbiased & international competitions

Learning about ANTs (examples, etc.)

General

Neuro

Lung

Cardiac

Misc.

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

Benchmarks for expected memory and computation time: results. These results are, of course, system and data dependent.

References

Google Scholar

Pubmed

Boilerplate ANTs

Here is some boilerplate regarding ants image processing:

We will analyze multiple modality neuroimaging data with Advanced Normalization Tools (ANTs) version >= 2.1 [1] (http://stnava.github.io/ANTs/). ANTs has proven performance in lifespan analyses of brain morphology [1] and function [2] in both adult [1] and pediatric brain data [2,5,6] including infants [7]. ANTs employs both probabilistic tissue segmentation (via Atropos [3]) and machine learning methods based on expert labeled data (via joint label fusion [4]) 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 [13] or functional connectivity [12]. 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 [8][9][10][11].

References

[1] http://www.ncbi.nlm.nih.gov/pubmed/24879923

[2] http://www.ncbi.nlm.nih.gov/pubmed/24817849

[3] http://www.ncbi.nlm.nih.gov/pubmed/21373993

[4] http://www.ncbi.nlm.nih.gov/pubmed/21237273

[5] http://www.ncbi.nlm.nih.gov/pubmed/22517961

[6] http://www.ncbi.nlm.nih.gov/pubmed/24033570

[7] http://www.ncbi.nlm.nih.gov/pubmed/24139564

[8] http://www.ncbi.nlm.nih.gov/pubmed/21632295

[9] http://www.ncbi.nlm.nih.gov/pubmed/19195496

[10] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3837555/

[11] http://nmr.mgh.harvard.edu/~koen/MenzeTMI2014.pdf

[12] http://www.ncbi.nlm.nih.gov/pubmed/23813017

[13] http://www.ncbi.nlm.nih.gov/pubmed/24830834

ANTs was supported by: R01-EB006266-01 and by K01-ES025432-01

ants chimp