NA-MIC National Alliance for Medical Image Computing Process-, Work-Flow in Medical Image Processing Guido Gerig

National Alliance for Medical Image Computing Need for Process Flow Image Processing and Analysis: –Sequence of processing steps (readers, filters, mappers, writers, visualization) –Clinical studies: between 30 and x00 datasets –Research: Prototyping Environment Process Flow System: –Fully automated (batch) and/or user-guided –Guides user through processing steps –Improved reliability and efficiency –Relieves user from repetitive tasks –Simplified sharing of processing sequences Process Flow System: Beyond Script Files (≠UNIX script/PERL/Python)

National Alliance for Medical Image Computing Example: User-Guided 3-D Level- Set Segmentation (SNAP) 3D Snake Segmentation: –Preprocessing (features) –Initialization –Post-editing –User-guidance Challenge: Use by non- experts Tool: SNAP-ITK (Yushkevich, Ho, Gerig) 5years Project

National Alliance for Medical Image Computing Level Set Segmentation Pipeline Preprocessing Initialization Segmentation A wizard guides the user through the segmentation process

National Alliance for Medical Image Computing ITK-SNAP Tour: Preprocessing Region competition stopping criterion (thresholding) Intensity edge stopping criterion

National Alliance for Medical Image Computing ITK-SNAP Tour: Initialization Spherical ‘bubbles’ or a coarse manual segmentation are used to initialize the level set

National Alliance for Medical Image Computing ITK-SNAP Tour: Parameters Different user interfaces: –Intuitive mode –Mathematical mode Preview of the forces acting on the level set

National Alliance for Medical Image Computing ITK-SNAP Tour: Segmentation

National Alliance for Medical Image Computing Example: EMS-ITK: Atlas- based brain MRI Segmentation T1T2TissueCortex

National Alliance for Medical Image Computing Example: Hippocampus Shape Analysis Workflow MRI Reformat Manual Landmarking Gray-value Normalization Hippocampus Segmentation via Model Deformation Spherical Parameterization SPHARM- PDM Shape QC Shape & Corresp. Alignment & Scaling Feature Computation e.g. Parcellation or Difference to Model Prior Models QC of Features & Statistical Results Statistical Analysis Of Features

National Alliance for Medical Image Computing Example: DTI Analysis in large clinical study (N>100) Co-registration of DTI Registration of DTI of each subject with: structural MRI segmentation maps lobe parcellation user-defined ROIs Statistical analysis per ROI Group 1 Group 2

National Alliance for Medical Image Computing DTI processing pipeline 4 DTI shots (.dcm) 4 DTI shots (.hdr) Average DTI (.gipl) FA/ADC maps (Gipl)Tensor field Average DTI (GE format) ROI and Lobe analysisFiber Tracking analysis Analysis using ImagineUsing the FiberTracking tool TensorCalc gipl2GE dcm2hdr DTIChecker

National Alliance for Medical Image Computing DTI processing pipeline (ctd.) FA/ADC maps Data Fusion Linear and nonlinear registration Writing Statistics sMRI (T1/T2/PD) EM-Segmentation ROIs Co-registration ROI and Lobe Analysis Brain Lobe Atlas MRI atlas template

National Alliance for Medical Image Computing UNC Solution: IMAGINE (Matthieu Jomier) Download:

National Alliance for Medical Image Computing UNC IMAGINE Imagine can generate Graphic User Interface automatically. Here, an example demonstrating the GUI generation for a recursive Gaussian filter. Cross-platform GUI-based visual programming environment Command line applications integration: Add your own modules Full integration ITK/vtk Modules executed as thread Memory manager: allocate/disallocate mem. Visual feedback/log file Generates Source code (C++) and makefile (Dyoxygen document.) Generates stand-alone cross- platform software with GUI

National Alliance for Medical Image Computing “Imagine” & “Batchmake” (Matthieu & Julien Jomier) Parallel processing with BatchMake interface and script generation. With Batchmake, you can follow progress of your pipeline online

National Alliance for Medical Image Computing Demonstration Imagine 2 Toy Example: Data Fusion: Registration of DTI to sMRI: –Registration T1 and T2/PD –Registration of baseline DTI-0 to T2 (linear, nonlinear) –Use transformation to register FA/ADC to T1/T2/PD

National Alliance for Medical Image Computing Discussion Process Flow Architecture significantly improves efficiency of research / exchange / “time to market” / large-scale studies Experience at UNC: Since introduction in ‘04, the ITK-based ProcessFlow environment has become standard tool (backbone) NA-MIC: Four uses: 1.Process flow in dedicated tasks (level-set segmentation, DTI processing, shape analysis, segmentation, etc.) 2.Research environment to facilitate prototyping/ exchange/ comparison: Facilitates transfer of research tools to Core 2 3.Clinical studies Core 3: Process flow systems to set-up a proc. system for individual tasks Run Batch jobs on large clinical studies → parallel/grid computing Verify results via qualitative visualization 4.Training/Dissemination Core 5: Process flow systems with visual feedback are excellent for teaching of methodology and tools Architectures: LONI Pipeline / AVS / SCIRun / UNC Imagine-1 and 2 / MevisLab / ….

National Alliance for Medical Image Computing Criteria ITK- and NA-MIC toolkit users don’t need to program, does not require advanced programming skills Cross-platform Pipeline processing and visual programming environment Easy integration, e.g. command-line integration of own modules Facilitates tests/comparison/exchange even of complex software and whole systems GUI generation, e.g. creation of stand-alone cross-platform software from Pipeline Parallel Processing / Script Generation Clinical studies: Multi-data processing Desirable for clinical studies: Visual programming language structures like “for loop”, “if… then … else” and “do… while” functions