Advanced Normalization Tools in R

ANTsR is an R package which wraps the well-established C++ biomedical image processing library 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 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. However, you must first install the ANTsRCore package, whose pre-compiled binaries are available on its own Releases Page. 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 r-universe

ANTsR and its main dependencies (ANTsRCore, ITKR) are built on r-universe, which provides binaries for Windows, macOS and Linux:

install.packages('ANTsR', repos = c(ANTsX = 'https://antsx.r-universe.dev', CRAN = 'https://cloud.r-project.org'))

From source using remotes

To install the latest version of the code directly from GitHub, add r-universe to your repositories first, so remotes installs the ANTsRCore and ITKR binaries from there:

options(repos = c(ANTsX = 'https://antsx.r-universe.dev', CRAN = 'https://cloud.r-project.org'))
remotes::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:

$ 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:

$ R CMD INSTALL ITKR
$ R CMD INSTALL ANTsRCore
$ R CMD INSTALL ANTsR

Developer notes

Authors

Brian B. Avants (maintainer), Benjamin M. Kandel, Jeff T. Duda, Philip A. Cook, Nicholas J. Tustison

License

Apache License 2.0

Relevant links

ANTsR Instagram

DOI 10.17605/osf.io/bsq5v

Reference manual: ANTsR

Vignettes:

Wiki: Notes and work in progress examples

Package source: from github

Binaries: here

Windows installation option here

Suggested packages can be installed via install.packages or remotes::install_github.

Research using ANTsR

• Inter-modality inference yet to be added RIPMMARC

• Eigenanatomy for multiple modality population studies function sparseDecom

• Tumor segmentation function mrvnrfs (not exactly the same but close)

• Multiple modality pediatric template and population study employs several aspects of ANTsR

• Structural networks from subject-level data function makeGraph plus yet to be added RIPMMARC

• SCCAN relating neuroimaging and cognitive batteries function sparseDecom2

• Sparse regression with manifold smoothness constraints function sparseRegression

• Prior-based eigenanatomy function sparseDecom

• Corrective learning for segmentation functions segmentationRefinement.train and segmentationRefinement.predict.

• LINDA: automated segmentation of stroke lesions function mrvnrfs.

• LESYMAP: lesion to symptom mapping function sparseDecom2.

• 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.