1 / 29

EMSegmentation in Slicer 3

This overview of the EM Segmentation module in Slicer 3 provides a step-by-step guide, live demo, and discussion on its applications in subcortical segmentation. The tool is designed to be easy to use, adaptable to various scenarios, and suitable for large datasets. It aims to separate complex tasks into simpler steps, provide consistent access to help, and allow users to define segmentation scenarios using tree atlas and intensity parameters. The module also offers the ability to edit the tree hierarchy for cortical and subcortical structures.

eduardoc
Télécharger la présentation

EMSegmentation in Slicer 3

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. EMSegmentation in Slicer 3 B. Davis, S. Barre, Y. Yuan, W. Schroeder, P. Golland, K. Pohl

  2. Overview • Introduction • EM Module Step-By-Step • Feedback & Discussion • Live Demo

  3. Motivation

  4. Hierarchical Tree

  5. Applications of EM Segmenter Subcortical SegmentationPsychiatry Neuroimaging LaboratoryBWH, Harvard White Matter LesionCenter for Neurological Imaging BWH, Harvard

  6. Design automatic segmenter that is easy to use adapts to variety of scenarios works on large data sets is a research tool Goals Slicer3 Slicer2

  7. Overview • Introduction • EM Module Step-By-Step • Feedback & Discussion • Live Demo

  8. Separates complex tasks into a sequence of simpler steps Checks user input before each transition Provides consistent access to help Wizard Interface Parameter Set Tree Atlas Target Intensity Parameters Registration Run

  9. Create new parameter set Apply/modify existing parameter set Parameter set defines segmentation scenario: • Atlas, Images, Algorithm parameters Parameter Set Tree Atlas Target Intensity Parameters Registration Run

  10. Defines a hierarchy of anatomical structures Parameter Set Tree Atlas Target Intensity Parameters Registration Run

  11. Assign atlas to anatomical structures Parameter Set Tree Atlas white matter csf Target Intensity grey matter background Parameters Registration Run

  12. Choose input channels Parameter Set Tree Atlas Target T1 Intensity Parameters Registration Run T2

  13. Define intensity distribution for each structure Parameter Set Tree Atlas Target Intensity Parameters Registration Run

  14. Specify node-based segmentation parameters Parameter Set • Influence of • Input channels • Atlas • Smoothing • Relative weight to other structures • Stopping conditions Tree Atlas Target Intensity Parameters Registration Run

  15. Specify atlas-to-input channel registration Parameter Set Tree Atlas white matter csf Target Intensity grey matter background Parameters Registration Run T2 T1

  16. Segment input channels using parameters Parameter Set Tree Atlas Target Intensity Parameters Registration Run

  17. Pipeline 1 2 3 AtlasAlignment EMSegmentation IntensityNormalization

  18. Observed Data (ROI) EM EM Segmenter Image Prior Hierarchy Labelmap

  19. IMAGE BG ICC CSF GM WM Level 1 Prior Information

  20. CSF GM WM Level 2 IMAGE ICC Current Parameter ROI

  21. Example Tree

  22. Overview • Introduction • EM Module Step-By-Step • Feedback & Discussion • Live Demo

  23. Resouces • Slicer3 EMSegment Wiki page:http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM • Project Description • Steps in EMSegment Workflow • Future Work • Implementation Details • EMSegment Tutorial • Slicer2 Material: • Tutorial: http://wiki.na-mic.org/Wiki/index.php/Slicer:Workshops:User_Training_101 • Publications • K.M. Pohl , S. Bouix, R. Kikinis, W.E.L. Grimson, Anatomical Guided Segmentation with Non-Stationary Tissue Class Distributions in an Expectation-Maximization Framework, In Proc. ISBI 2004, pp. 81-84,2004 • K.M. Pohl, S. Bouix, M.E. Shenton, W.E.L. Grimson, R. Kikinis, Automatic Segmentation Using Non-Rigid Registratio, short communications of MICCAI 2005

  24. Feedback & Discussion • Priorities for future development • Class overview panel • Graphical Display • Controlled vocabulary • Library of Templates • One-Step-Segmentation

  25. Acknowledgements • Steve Pieper • Alex Yarmarkovich • Wendy Plesniak • Slicer developer community • Psychiatry Neuroimaging Laboratory • NAMIC

  26. Acknowledgements • Kitware Developer

  27. Overview • Introduction • EM Module Step-By-Step • Feedback & Discussion • Live Demo

  28. Level 3: Cortical Subcortical Editing the Tree

More Related