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Reading DICOM-RT in R using RadOnc

radonc1

Previously I’ve written my own R code to access DICOM-RT structure sets in group 3006 of the meta-data. Shortly after I wrote that original post, Reid F. Thompson made his R package RadOnc available on CRAN. Unfortunately, my old code no longer works with the current version of the oro.dicom R package, therefore I would recommend using RadOnc instead. The code below is focused on importing the 3D geometry, but the R package has a lot of other features that you might find useful: for example, calculation of Dice similarity coefficient and Hausdorff distance; as well as import of dose-volume histograms (DVH).

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bayesImageS::mcmcPottsNoData(..)

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This is a follow up to my previous post about the Swendsen-Wang (SW) algorithm, where I mentioned that SW has better convergence properties than Gibbs when the inverse temperature parameter β is large. This difference can be quantified by initialising the two algorithms at known starting points and measuring how many iterations it takes to converge. This is the second in a series of posts describing the functions and algorithms that I have implemented in the R package bayesImageS, which is now available on CRAN.

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A case study in Bayesian computation using Rcpp and OpenMP

At the beginning of December, I presented a seminar for the PhD students of the joint Oxford-Warwick Centre for Doctoral Training (otherwise known as the OxWaSP CDT). My slides and abstract are below:

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bayesImageS available on CRAN

My R package was rejected the first time, due to an old bug in RcppArmadillo (details below). I also forgot to add ‘cran-comments.html’ to my .Rbuildignore after following Hadley Wickham’s otherwise excellent advice on how to develop a package for CRAN. The source package and Windows binaries are now available, with OS X soon to follow. Using R-hub was definitely helpful, since it allowed me to test my package on various flavours of Linux and versions of R before submitting it. The NOTEs didn’t come as a surprise, since running rhub::check_for_cran had already made me aware of them. Hopefully R-hub will add support for Mac OS and SPARC Solaris soon. My code has multiple compile errors in Solaris Studio 12.3, which would be painful to fix without access to a virtual machine. Continuous integration with Travis might also have been useful, but my code is hosted on Bitbucket not GitHub.

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R-hub on Mac OS X

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Since I’ve been invited to give a seminar at an OxWaSP mini-symposium, I decided it was finally time to get my R package bayesImageS in shape for submission to the CRAN repository. Recently, the R-hub builder was released as a public beta. I was keen to check this out, since it is meant to make the process of submitting packages to CRAN simpler, particularly for first time package authors such as myself.

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OpenMP on OS X using gcc

Screen Shot 2016-07-12 at 13.28.28

These instructions are for OS X Mavericks. Upgrading to el Capitan will almost certainly prevent this from working, due to the system integrity protection features that were introduced.

Clang 3.8.0 supports OpenMP, but the version of clang included with the Xcode command-line tools does not. Since clang is the default compiler for R on OS X, R packages that use OpenMP directives will run in parallel on Linux or Windows, but single-threaded on Mac OS. Both of my R packages, serrsBayesbayesImageS, were written with Rcpp using OpenMP, so this is a problem for me. I do most of my heavy computation on a Linux cluster, but I develop and test a lot of code on my iMac. I’d really like my code to make full use of the multicore i7 CPU, rather than taking 5-8 times longer to run.

There are two main options for compiling with OpenMP on Mac OS: installing the latest Clang from LLVM, or installing gcc and replacing the symbolic link in /usr/bin. When I first encountered this issue, LLVM clang still didn’t support all of the #pragma omp directives, so I chose the second option. You still need to install gcc to get gfortran support on OS X, so that seemed like the best option. However, in Mac OS 10.11 (el Capitan), /usr/bin is read only.

Whichever option you choose, you then need to add the following lines to your personal or site-specific Makevars file:

CC=/usr/local/bin/gcc
CXX=/usr/local/bin/g++ -arch x86_64 -ftemplate-depth-256
PKG_LIBS = `$(R_HOME)/bin/Rscript -e "Rcpp:::LdFlags()"` -fopenmp
CXXFLAGS += -fopenmp

Warning: it is definitely a bad idea to add these lines to an R package Makevars, since it will break the build for any Mac OS user who compiles with clang instead of gcc (sorry, Jake…)

Single-threaded, my Metropolis-Hastings algorithm takes 45 seconds for 10,000 iterations. When I compile with OpenMP enabled, it takes 27 sec. Not a huge speedup perhaps, but it makes a big difference for longer-running computation.

Note that some R packages, such as rstan, modify the personal Makevars file. If OpenMP suddenly stops working, that could be the culprit. Check ~/.R/Makevars and make sure that the above settings are still intact.

Haar wavelets in Google docs (and R)

temp0

Igor recently posted some interesting data, showing the change in temperature over time with 2 different types of fan, or without any cooling. This data exhibits some unusual features: the underlying trend is smooth, over a long timeframe (minutes) but there is also periodic switching between “low” and “high” signal, which is sometimes accompanied by jitter. I’ll first show how to generate some similar data in R, then how we might compute the discrete wavelet transform (DWT) in Google Sheets. Of course, the simplest solution is often the best (unless you work in research…), so check out Igor’s post for a quicker way to filter this data.

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