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https://github.com/ANTsX/ANTsR

R interface to the ANTs biomedical image processing library
https://github.com/ANTsX/ANTsR

brain decomposition mri r registration segmentation

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R interface to the ANTs biomedical image processing library

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README 

        

# Advanced Normalization Tools in R



[![R-CMD-check](https://github.com/ANTsX/ANTsR/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/ANTsX/ANTsR/actions/workflows/R-CMD-check.yaml)

![Downloads](https://img.shields.io/github/downloads/antsx/antsr/total)

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/stnava/ANTsRDocker/master)

[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg)](code_of_conduct.md)



ANTsR is an R package which wraps the well-established C++ biomedical image processing library [ANTs](https://github.com/ANTsX/ANTs). It includes efficient reading and writing of medical images, algorithms for registration, segmentation, and statistical learning, as well as functions to create publication-ready visualizations.



If you are looking to train deep learning models on your medical images in R, you might be interested in [ANTsRNet](https://github.com/ANTsX/ANTsPy) which provides pre-trained models and utilities for training deep learning models on medical images.







## Installation



The ANTsR package can be installed the pre-compiled binaries (fast) or from source. We are actively working on getting ANTsR onto a host like CRAN or BioConductor.



### Pre-compiled binaries



The fastest way to install ANTsR is from pre-compiled binaries made available on the [Releases Page](https://github.com/ANTsX/ANTsR/releases). However, you must first install the ANTsRCore package, whose pre-compiled binaries are available on its own [Releases Page](https://github.com/ANTsX/ANTsRCore/releases). Download the relevant versions for both pacakges and run this from the command-line:



```

R CMD INSTALL ANTsRCore_*.tgz

R CMD INSTALL ANTsR_*.tgz

```



### From source using devtools



To install ANTsR from source, you can use devtools to install the latest version of the code directly form GitHub. This should also automatically install the main dependencies like the ANTsRCore package.



```R

devtools::install_github('ANTsX/ANTsR')

```



### From source using R CMD INSTALL



Alternatively, you can clone and install ANTsR and its two main dependencies (ANTsRCore, ITKR) and then install them as you would traditional R source packages.



First, clone the repositories:



```sh

$ git clone https://github.com/stnava/ITKR.git

$ git clone https://github.com/ANTsX/ANTsRCore.git

$ git clone https://github.com/ANTsX/ANTsR.git

```



Install the package as follows:



```sh

$ R CMD INSTALL ITKR

$ R CMD INSTALL ANTsRCore

$ R CMD INSTALL ANTsR

```







## Developer notes



### Authors



[Brian B. Avants](http://stnava.github.io/) (maintainer), Benjamin M. Kandel, Jeff T. Duda, Philip A. Cook, Nicholas J. Tustison



### License



Apache License 2.0







## Relevant links



[ANTsR Instagram](https://www.instagram.com/antsrx/)



[DOI 10.17605/osf.io/bsq5v](https://osf.io/bsq5v/)



Reference manual: [ANTsR](https://antsx.github.io/ANTsR/reference/index.html)



[Vignettes](https://antsx.github.io/ANTsR/articles/):



Wiki: [Notes and work in progress examples](https://github.com/ANTsX/ANTsR/wiki)



Package source: [from github](https://github.com/ANTsX/ANTsR/zipball/master)



Binaries: [here](http://github.com/ANTsX/ANTsR/releases/)



_Windows_ installation option [here]()



**Suggested packages** [https://github.com/stnava/ANTsRDocker/blob/master/install.R](https://github.com/stnava/ANTsRDocker/blob/master/install.R)







## Research using ANTsR



- [Inter-modality inference](http://www.ncbi.nlm.nih.gov/pubmed/25449745) yet to be added RIPMMARC



- [Eigenanatomy for multiple modality population studies](http://www.ncbi.nlm.nih.gov/pubmed/25448483) function `sparseDecom`



- [Tumor segmentation](http://www.ncbi.nlm.nih.gov/pubmed/25433513) function `mrvnrfs` (not exactly the same but close)



- [Multiple modality pediatric template and population study](http://www.nature.com/articles/sdata20153) employs several aspects of ANTsR



- [Structural networks from subject-level data](http://www.ncbi.nlm.nih.gov/pubmed/25320792) function `makeGraph` plus yet to be added RIPMMARC



- [SCCAN relating neuroimaging and cognitive batteries](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3911786/) function `sparseDecom2`



- [Sparse regression with manifold smoothness constraints](http://www.ncbi.nlm.nih.gov/pubmed/24683960) function `sparseRegression`



- [Prior-based eigenanatomy](http://www.ncbi.nlm.nih.gov/pubmed/24852460) function `sparseDecom`



- [Corrective learning for segmentation](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3049832/) functions `segmentationRefinement.train` and `segmentationRefinement.predict`.



- [LINDA: automated segmentation of stroke lesions](https://www.ncbi.nlm.nih.gov/pubmed/26756101) function `mrvnrfs`.



- [LESYMAP: lesion to symptom mapping](https://www.ncbi.nlm.nih.gov/pubmed/28882479) function `sparseDecom2`.



- [ANTsRNet](https://github.com/ntustison/ANTsRNet) A growing collection of well-known deep learning

  architectures ported to the R language.

  _ Image segmentation

  _ U-Net (2-D)

  _ V-Net (3-D)

  _ Image classification

  _ AlexNet (2-D, 3-D)

  _ Vgg16/Vgg19 (2-D, 3-D)

  _ ResNet/ResNeXt (2-D, 3-D)

  _ GoogLeNet (2-D)

  _ DenseNet (2-D, 3-D)

  _ Object detection \* Single Shot MultiBox Detector (2-D, 3-D)







## Contributing



If you have a question or need help solving a problem, please create an issue on GitHub. If you want to contribute code or have a feature request, creating an issue on GitHub is also the best place to start.