License: Apache License 2.0
Maintainer: Brian B. Avants
BugReports: github issues
Travis checks: ANTsR results
Preview ANTsR on the cloud
.libPaths('/cloud/project/RL/') library(ANTsR) ?plot.antsImage ?antsRegistration
To learn more, open and run the Rmd files in the directory "/cloud/project/RL/ANTsR/doc"
Note: as of this writing, memory is very limited on this cloud preview so some examples may not run successfully.
Reference manual: ANTsR
Package source: from github
Windows installation option here
Install the binary, after downloading, via command line:
R CMD INSTALL ANTsR_*.tgz
Research using ANTsR
Inter-modality inference yet to be added RIPMMARC
Eigenanatomy for multiple modality population studies function
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
makeGraphplus yet to be added RIPMMARC
SCCAN relating neuroimaging and cognitive batteries function
Sparse regression with manifold smoothness constraints function
Prior-based eigenanatomy function
Corrective learning for segmentation functions
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)
- Image segmentation
Installation from source
Please read this entire section before choosing which method you prefer.
Windows users should see Rtools and maybe, also, installr for assistance in setting up their environment for building (must have a compiler too). To my knowledge, there are no recorded instances of ANTsR being installed on Windows. If someone does so, we would like to know.
mydeps <- c( "Rcpp", "RcppEigen", "magrittr", "rsvd", "magic", "psych" ) install.packages( pkgs = mydeps, dependencies = TRUE )
Method 1: with devtools in R
library( devtools ) # install_github("stnava/cmaker") # if you do not have cmake install_github("stnava/ANTsR")
Method 2: from command line (most traditional method)
Assumes git, cmake and compilers are available in your environment (as above).
First, clone the repository:
$ 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
travis.yml file also shows a way to install from Linux command line.
Method 3: from binaries
Note that version numbers will change over time.
wget https://github.com/stnava/ITKR/releases/download/latest/ITKR_0.4.12_R_x86_64-pc-linux-gnu.tar.gz R CMD INSTALL ITKR_0.4.12_R_x86_64-pc-linux-gnu.tar.gz wget https://github.com/ANTsX/ANTsRCore/releases/download/v0.4.2.1/ANTsRCore_0.4.2.1_R_x86_64-pc-linux-gnu.tar.gz R CMD INSTALL ANTsRCore_0.4.2.1_R_x86_64-pc-linux-gnu.tar.gz wget https://github.com/ANTsX/ANTsR/releases/download/latest/ANTsR_0.6_R_x86_64-pc-linux-gnu.tar.gz R CMD INSTALL ANTsR_0.6_R_x86_64-pc-linux-gnu.tar.gz
Method 4: platform independent via docker and kitematic
This is a beta operation that is in flux but may be convenient for some users.
based on this approach
create a docker username
download and install kitematic
login to kitematic with your docker username
antsrin the kitematic repository search bar
create a new container from the
start the container
type the "access url" address into a browser to run rstudio with antsr. it should be something like
you can also add your home folders to the container instance by adjusting the "volumes" option under the settings tab. then you can access local data.
Load the package:
List the available functions in the namespace ANTsR:
Call help on a function via ?functionName or see function arguments
Tagging a beta release
git tag -d beta git push origin :refs/tags/beta git tag beta git push --tags origin
More like development highlights, as opposed to release notes. See
git log for the complete history. We try to follow these versioning recommendations for R packages. Under these guidelines, only
major.minor is an official release.
- ENH: better compilation and release style.
- ENH: return boolean same size as image
- ENH: improved decomposition methods
- ENH: easier to use antsrSurf and antsrVol
- ENH: spare distance matrix, multi scale svd
ENH: added domainImg option to plot.antsImage which can standardize plot.
COMP: test for DVCL define requirement to improve clang and gcc compilations
WIP: transform objects can be created on the fly and manipulated thanks to jeff duda
ENH: automation for eigenanatomy
ENH: reworked SCCAN and eanat
ENH: resting state Vignette
DOC: clarify/extend antsApplyTransforms
ENH: multidimensional images
STYLE: iMath not ImageMath in ANTsR
WIP: iMath improvements
WIP: ASL pipeline fuctionality
BUG: Fixed image indexing bug
BUG: plot.antsImage improvements
ENH: more antsRegistration options
ENH: JointLabelFusion and JointIntensityFusion
ENH: Enable negating images
ENH: weingarten curvature
ENH: antsApplyTransformsToPoints with example
ENH: Suppress output from imageWrite.
First official release.