NA-MIC National Alliance for Medical Image Computing Core 1b – Engineering Computational Platform Jim Miller GE Research

National Alliance for Medical Image Computing Core 1b – Engineering 5 Aims / 5 Platforms Architecture – tools, operating paradigms, reporting mechanisms, integration points End-user platform – interactive methods and information visualization for longitudinal analysis, exploratory data analysis, and translational research Computational platform – stream processing, cloud computing, statistical analysis, informatics, machine learning Data management – non-imaging and derived data, DICOM and cloud services Software engineering and software quality – navigable timeline for revision control, build, test, documentation and release

National Alliance for Medical Image Computing Computational platform The objective of the Engineering component of Core 1 is to provide software tools and software development processes to deploy innovative technology to clinical researchers, support the scientific algorithm innovation of the Algorithm scientists, and to foster a community to produce high quality software. Computing architecture Desktop Grid computing Stream computing (GPGPU) Cloud computing Computing architecture Desktop Grid computing Stream computing (GPGPU) Cloud computing Analysis platform Feature libraries Multivariate clustering Machine learning Statistical inference Regression analysis Information visualization Informatics Analysis platform Feature libraries Multivariate clustering Machine learning Statistical inference Regression analysis Information visualization Informatics DBP needs Pathology segmentation Longitudinal analysis Patient specific analysis Population analysis DBP needs Pathology segmentation Longitudinal analysis Patient specific analysis Population analysis Algorithm needs Exploratory methods Longitudinal frameworks Interactive frameworks Statistics Machine learning Algorithm needs Exploratory methods Longitudinal frameworks Interactive frameworks Statistics Machine learning

National Alliance for Medical Image Computing ITKv4 ARRA Funded effort from the National Library of Medicine Kickoff 6/2010 v4.0 released 12/2011 v4.0.1 SPIE v4.2 June 2012

National Alliance for Medical Image Computing ITKv4 Team GE Research Kitware Inc. University of Pennsylvania Harvard University University of Iowa CoSMo Software Mayo Clinic University of Utah ITKv4 A2D2 Team Georgetown University University of Utah University of North Carolina Chapel Hill The Ohio State University Carnegie Mellon University Harvard University William and Mary Old Dominion University Kitware Inc.

National Alliance for Medical Image Computing ITKv4 Overview Apache 2.0 License “Patented” directory removed New software process –Git, Gerrit, Jira, Testing Data Deprecated compilers –Visual Studio 6, Visual Studio 7, Borland 5.5, SUN CC < 5.9, SGI CC, MWORKS, Cygwin, GCC < bit improvements

National Alliance for Medical Image Computing ITKv4 - Modularization ITKv3 – monolithic organization ITKv4 – modular organization

National Alliance for Medical Image Computing ITKv4 – Level sets ITKv3 provides several flavors of level sets –Implemented in a general finite difference framework –Each level set equation encapsulated in a separate filter ITKv4 provides a more general framework –Level set equation can be constructed term by term Advection terms Propagation terms Region terms Regularization terms –Multi-material –Level set stopping criterions

National Alliance for Medical Image Computing ITKv4 - Registration Unbiased registration support Composite transforms Multithreaded metrics New and updated metrics –Neighborhood correlation –Mutual information –Point set metrics –Tensor metrics –Vector metrics New and update transforms –Displacement, Bspline, Poly-affine, Diffeomorphic New optimizers –Efficient for high dimensional transforms Automated parameter initialization

National Alliance for Medical Image Computing ITKv4 - FEM Broader FEM support (not just for registration) ITK conformant Memory and reference management improvements SpatialObjects for IO –Removed other FEM IO mechanisms FEMObject constructs problem instead of the solver More details at

National Alliance for Medical Image Computing ITKv4 – GPU (SPIE release or v4.1) Architecture for ITK filters to utilize GPUs Lazy synchronization of data between CPU and GPU Factory mechanism Samples of various “classes” of algorithms –BinaryThreshold, Mean, DiscreteGaussian, AnisotropicDiffusion, DemonsRegistration Filter on CPUFilter on GPU Filter on CPU GPU buffer CPU buffer

National Alliance for Medical Image Computing ITKv4 - SimpleITK On-ramp to ITK Templateless layer for C++ Function Paradigm, Object Paradigm –but no Pipeline Paradigm Wrapping –Python, Java, C#, Tcl, Lua, Ruby, R Access to most ITK algorithms Simple image viewing

National Alliance for Medical Image Computing ITKv4 - Video Cameras, Ultrasound, Fluoro, OCT, … Video bridge – OpenCV, VXL Video grabber Ringbuffer Video pipelines

National Alliance for Medical Image Computing ITKv4 - Microscopy Point spread function models Deconvolution Denoising Color correction Colocalization IO

National Alliance for Medical Image Computing Nipype Python scriptable engine Local and distributed execution Semantically uniform access to analysis tools –SPM, FSL, FreeSurfer, AFNI, Brains, Slicer, Camino Example: –Huntington’s Disease DBP tying all their tools through Nipype –Nipype as the documentation vehicle for the processing chain –Slicer Execution Model (Command Line Modules) accessible through Nipype

National Alliance for Medical Image Computing Feature libraries Enable exploratory methods and learning based methods ITK filter library –Gabor features –Haralick features –Entropy features –Polynomial fits to time varying data –Histogram features EntropyHistogramHaralick

National Alliance for Medical Image Computing Core 1b – Engineering Computational platform Architecture – tools, operating paradigms, reporting mechanisms, integration points End-user platform – interactive methods and information visualization for longitudinal analysis, exploratory data analysis, and translational research Computational platform – stream processing, cloud computing, statistical analysis, informatics, machine learning Data management – non-imaging and derived data, DICOM and cloud services Software engineering and software quality – navigable timeline for revision control, build, test, documentation and release