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Toward Automated Validation of Sketch-based 3D Segmentation Editing Tools

Toward Automated Validation of Sketch-based 3D Segmentation Editing Tools. Frank Heckel 1 , Momchil I. Ivanov 2 , Jan H. Moltz 1 , Horst K. Hahn 1,2. 1 Fraunhofer MEVIS, Bremen, Germany, 2 Jacobs University, Bremen , Germany.

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Toward Automated Validation of Sketch-based 3D Segmentation Editing Tools

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  1. Toward Automated Validation ofSketch-based 3D Segmentation Editing Tools Frank Heckel1, Momchil I. Ivanov2, Jan H. Moltz1, Horst K. Hahn1,2 1 Fraunhofer MEVIS, Bremen, Germany, 2Jacobs University, Bremen, Germany 18th Scandinavian Conference on Image Analysis, Espoo, Finland, June 2013

  2. Motivation Solution Results Outlook Conclusion • Segmentation is one of the essential tasks in medical image analysis • Many sophisticated automatic segmentation algorithms exist … • … which might fail in some cases • Low contrast, noise, biological variability, … • What is segmentation editing and why isn’t it trivial? • What to do?

  3. Motivation Solution Results Outlook Conclusion • Intuitive interaction in 2D – Estimate the user’s intention in 3D • As few interactions as possible • The segmentation problems are typically hard • What is segmentation editing and why isn’t it trivial? • Locally correct the error until it satisfies the specific needs F. Heckel et al., ”3D contour based local manual correction of tumor segmentations in CT scans”, SPIE Medical Imaging: Image Processing, 2009 F. Heckel et al., “Sketch-based Image-independent Editing of 3D Tumor Segmentations using VariationalInterpolation”, Eurographics Workshop on Visual Computing for Biology and Medicine, 2012

  4. Motivation Solution Results Outlook Conclusion • What is segmentation editing and why isn’t it trivial? F. Heckel et al., “Sketch-based Image-independent Editing of 3D Tumor Segmentations using VariationalInterpolation”, Eurographics Workshop on Visual Computing for Biology and Medicine, 2012

  5. Motivation Solution Results Outlook Conclusion • The segmentation editing process visually performed by the user intended result that the user only has in mind

  6. Motivation Solution Results Outlook Conclusion • “Static” quality measurements exist • Volume overlap / dice coefficient • Average / maximum surface distance • Interactive segmentation process • Measuring the quality of the final result only is not enough • Acceptance suffers from bad intermediate results • Additional quality factors like number of steps, computation time, … • User is mandatory • High effort – New evaluations after algorithmic changes • Bad reproducibility • The difficulty in validation of segmentation editing tools

  7. Solution Results Outlook Conclusion Motivation • The automatic validation process use common quality measurements once generated by an expert

  8. Solution Results Outlook Conclusion Motivation • Step 1: Find the most probably corrected error • Subtract intermediate from reference segmentation • 3D connected components define “errors” • Select an error to be corrected • Largest volume + compactness • Largest Hausdorffdistance • Editing simulation reference segmentation intermediate segmentation

  9. Solution Results Outlook Conclusion Motivation • Step 2: Select slice and view where the error is most probably corrected • Largest area + compactness • Largest Hausdorffdistance • Editing simulation Slice 44 Slice 48 Slice 52

  10. Solution Results Outlook Conclusion Motivation • Step 3: Generate user-input for sketching • Get surface of error • Remove voxels that are on the surface of the intermediate segmentation as well • Step 4: Apply editing algorithm • Editing simulation

  11. Results Outlook Conclusion Solution • Volume-based strategy

  12. Results Outlook Conclusion Solution • Distance-based strategy

  13. Results Outlook Conclusion Solution Volume-based strategy Distance-based strategy

  14. Outlook Conclusion Results • Solve current limitations (e.g., correction of holes) • Extend simulation • Model inaccuracy in drawing sketches • Model more correction strategies • “Finish” an error before moving to the next • Perform correction in “one of the error’s first slices” • Investigate how the quality of editing tools is measured best • Apply simulation-based validation to a larger database

  15. Conclusion Outlook • Segmentation editing: • Is an indispensable step in the segmentation process • Efficient editing in 3D is challenging • Validation of 3D editing algorithms: • Needs to consider the dynamic nature of such tools • User studies are time consuming and lack reproducibility • Proposed solution: Simulate the user • Allows objective and reproducible validation and comparison • Allows better quality assessment

  16. Thank you! frank.heckel@mevis.fraunhofer.de

  17. Appendix • Computation time Volume-based strategy Distance-based strategy

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