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ABC: Atlas-Based Classification

ABC: Atlas-Based Classification. Marcel Prastawa and Guido Gerig Scientific Computing and Imaging Institute University of Utah. Atlas Based Classification. ABC. Atlas template with spatial priors for tissue categories. ABC: Fully Automatic Workflow.

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ABC: Atlas-Based Classification

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  1. ABC: Atlas-Based Classification Marcel Prastawa and Guido Gerig Scientific Computing and Imaging Institute University of Utah

  2. Atlas Based Classification ABC Atlas template with spatial priors for tissue categories

  3. ABC: Fully Automatic Workflow Spatial normalization (linear): intermodality, atlas Tissue Classification Atlas to subject warping Inhomogeneity correction

  4. ABC: Properties • Fully automatic, no user interaction required • Repeatable, reproducible results • Arbitrary #channels/modalities: Co-registration • Integrates brain stripping, bias correction and segmentation into one optimization framework ≠ set of separate procedures • Atlas to subject warping: diffeomorphic fluid flow registration • Generic framework: Needs image(s) and prob. atlas → RUN • Rigorous validation and testing • Run time: Affine atlas matching: 0.5h, deformable: 2-3hrs • In progress: Extension to pathologies • K. Van Leemput, F. Maes, D. Vandermeulen, P. Suetens, P.,Automated model-based tissue classification of MR images of the brain, IEEE TMI, 18(10) 1999 • N. Moon, E. Bullitt, K. van Leemput, G. Gerig, Automatic Brain and Tumor Segmentation, Proc. MICCAI ‘02, Springer LNCS 2488, 09/2002

  5. Typical Output: Registration and Segmentation of T1/T2 T1 T2 Brain Tissue 3D cortical surface T1 T2 Brain Tissue 3D wm surface

  6. “Byproduct”: Bias inhomogeneity correction 3T T1 registered Corrected image Bias field 1.5T

  7. “Byproduct”: Brain Stripping MRI segmentation: ICV as result of brain segmentation (gm+wm+csf) DTI: Brain masking via tissue segmentation of B0 image.

  8. “Byproduct”: Co-Registration of structural MRI for visualizing multi-contrast data MPrage+gad GRE-bleed TSE FLAIRSWI TBI case courtesy J. v Horn , D. Hovda, UCLA

  9. ABC in human traveling phantom across-site MRI calibration MRI and DTI scans of 2 traveling phantoms annually at 6 sites Phantom1 Phantom2 Courtesy ACE-IBIS autism study, Piven, UNC Robust framework for detecting calibration errors

  10. Typical Clinical Study • Drug addition, effects on brain morphometry and function (sMRI/DTI) • Yale: Linda Mayes, Marc Potenza • UNC: Joey Johns • Utah: Gerig/Gouttard • ABC automatically processes MR datasets

  11. Application: Basic Segmentation with T1 only T1 Brain Tissue T1 Brain Tissue

  12. Application: Lobe Parcellation • Use nonlinear registration of template to map parcellation in atlas space Table of gm/wm/csf per lobe -> biostatistical analysis →

  13. Application: multi-modal sMRI of TBI case MPrage+gad GRE-bleed TSE FLAIR SWI Brain/csf segmentation ABC performs co-registration of all input modalities (here 5 MRI channels) and atlas-based segmentation of brain tissue and csf. Bias-correction (all modalities) and brain-stripping is an integrative, automatic part of ABC. White matter lesions and ventricles were segmented via postprocessing using level-set segmentation. MRI data courtesy of UCLA (John Van Horn and David Hovda).

  14. Application: Joint analysis of sMRI and DTI • Co-registration of structural modalities to DTI (baseline image registered to TSE) using ABC. • DTI tensor field and structural images available in same coordinate system. DTI (mean diffusivity) TSE GRE-bleed Segmentation

  15. ABC and Slicer-3: Tractography and joint display of segmented objects and MRI • Tractography, fiber clustering and composition by Ron Kikinis • Co-registration DTI/sMRI and brain/lesion segmentation by Guido Gerig

  16. Application: Monkey Brain Segmentation ABC applied to macaque brain processing: M. Styner, I. Oguz, UNC

  17. Application: Mouse “Brain Stripping” for DTI ABC applied to mouse brain processing: M. Styner, I. Oguz, UNC MD FA

  18. Current Extensions: Lesions and Pathology WM lesions in lupus (MIND, J. Bockholt) 3D T2 FLAIR Marcel Prastawa and Guido Gerig. Brain Lesion Segmentation through Physical Model Estimation. International Symposium on Visual Computing (ISVC) 2008. Lecture Notes in Computer Science (LNCS) 5358, Pages 562-571

  19. Work in Progress: TBI Case 1 TBI data courtesy UCLA (D. Hovda) • T1 difficult quality (low contrast, non-isotropic voxels, brain damage) • Automatic brain segmentation. • User-supervised level-set segmentation of lesions and ventricles. • Cursor points to right frontal brain damage,T1 hyperintense lesions shown in yellow.

  20. Work in Progress: Example TBI Case 2 TBI data courtesy UCLA (D. Hovda) • T1 difficult quality (motion, contrast, non-isotropic voxels) • Automatic brain segmentation. • User-supervised level-set segmentation of lesions and ventricles. • T1 hyperintense lesions shown in yellow.

  21. Work in Progress: Other TBI Cases

  22. Slicer 3 Interface Basic parameters: Input and output files

  23. Slicer 3 Interface Advanced parameter settings: • Type of linear transformation for intra-subject modalities • Bias correction polynomial degree • Amount of deformation of atlas (affine, fluid w. #iterations)

  24. More Information http://www.nitrc.org/projects/abc

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