Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Atlas-based segmentation using iterative atlas selection or How to select patients for virtual organ donation Robin Langer...
Introduction <ul><li>Radiotherapy treatment requires the delineation of target organs in medical images </li></ul><ul><li>...
Atlas-based segmentation <ul><li>Atlas-based segmentation is a method that derives a delineation from similar delineations...
What is atlas-based segmentation? ? L 1 L n I 1 I n • • • • I t T 1 T n 1  Registration 1  Registration
What is atlas-based segmentation? ? I n I 1 L 1 L n • • • • I t T 1 T n L 1 L n 1  Registration 1  Registration 2  Propaga...
What is atlas-based segmentation? ? I n I 1 L 1 L n • • • • I t T 1 T n L 1 L n 1  Registration 1  Registration 2  Propaga...
Label fusion: selecting the best labels <ul><li>Labels can be fused by a simple majority vote rule </li></ul><ul><li>The r...
A smart selection of labels ? I t L 1 L n L t ≈ I 1 ’ I n ≈ ≈ Image  Similarity
A smart selection of labels ? I t L 1 L n L t ≈ I 1 ’ I n ≈ ≈ Image  Similarity Label Similarity
A smart selection of labels ? I t L 1 L n L t ≈ I 1 ’ I n ≈ ≈ Label fusion Label fusion Increased performance estimation
Results <ul><li>Iterative </li></ul><ul><li>label selection </li></ul>Image similarity Known label performance Number   32...
Results <ul><li>Atlas-based </li></ul><ul><li>segmentation </li></ul>Human experts Accuracy compared to golden   0.84+0.07...
Conclusions <ul><li>Atlas-based segmentation with iterative label selection outperforms selection based on image similarit...
Acknowledgements <ul><li>This work was supported by the Dutch Cancer Society </li></ul><ul><li>Implementations of B-spline...
Upcoming SlideShare
Loading in …5
×

Robin Langerak

714 views

Published on

Published in: Technology, Health & Medicine
  • Be the first to comment

Robin Langerak

  1. 1. Atlas-based segmentation using iterative atlas selection or How to select patients for virtual organ donation Robin Langerak 1 , Alexis Kotte 2 , Uulke van der Heide 2 , Josien Pluim 1 1 Image Sciences Institute, 2 Department of Radiotherapy University Medical Center Utrecht September 10, NFBI Symposium
  2. 2. Introduction <ul><li>Radiotherapy treatment requires the delineation of target organs in medical images </li></ul><ul><li>Delineation is an expert task, that is time-consuming and subjective. </li></ul><ul><li>How to automate delineation? </li></ul>
  3. 3. Atlas-based segmentation <ul><li>Atlas-based segmentation is a method that derives a delineation from similar delineations of past patients. </li></ul><ul><li>Atlas-based segmentation is fast and eliminates the human factor without eliminating the human factor. </li></ul>
  4. 4. What is atlas-based segmentation? ? L 1 L n I 1 I n • • • • I t T 1 T n 1 Registration 1 Registration
  5. 5. What is atlas-based segmentation? ? I n I 1 L 1 L n • • • • I t T 1 T n L 1 L n 1 Registration 1 Registration 2 Propagation 2 Propagation
  6. 6. What is atlas-based segmentation? ? I n I 1 L 1 L n • • • • I t T 1 T n L 1 L n 1 Registration 1 Registration 2 Propagation 2 Propagation L t 3 Label fusion ≈
  7. 7. Label fusion: selecting the best labels <ul><li>Labels can be fused by a simple majority vote rule </li></ul><ul><li>The result of atlas-based segmentation can be improved by selecting only the ‘best’ labels </li></ul><ul><li>Problem: the performance of individual labels is unknown. </li></ul>
  8. 8. A smart selection of labels ? I t L 1 L n L t ≈ I 1 ’ I n ≈ ≈ Image Similarity
  9. 9. A smart selection of labels ? I t L 1 L n L t ≈ I 1 ’ I n ≈ ≈ Image Similarity Label Similarity
  10. 10. A smart selection of labels ? I t L 1 L n L t ≈ I 1 ’ I n ≈ ≈ Label fusion Label fusion Increased performance estimation
  11. 11. Results <ul><li>Iterative </li></ul><ul><li>label selection </li></ul>Image similarity Known label performance Number 32 + 8 20 20 --------------------------------------------------------------------------------- Similarity with ground truth 0.85 + 0.06 0.77 + 0.09 0.87 + 0.05 segmentation --------------------------------------------------------------------------------- Correlation with ground truth 0.97 + 0.01 0.57 + 0.12 1 label performance
  12. 12. Results <ul><li>Atlas-based </li></ul><ul><li>segmentation </li></ul>Human experts Accuracy compared to golden 0.84+0.07 0.88+0.06 standard
  13. 13. Conclusions <ul><li>Atlas-based segmentation with iterative label selection outperforms selection based on image similarity. </li></ul><ul><li>The results are almost as good as that of human experts. </li></ul><ul><li>There is very little room for improvement of atlas selection </li></ul><ul><li>Future work will be on the selection of labels prior to registration </li></ul>
  14. 14. Acknowledgements <ul><li>This work was supported by the Dutch Cancer Society </li></ul><ul><li>Implementations of B-spline deformation in the ITK library were used. Registration was done using Elastix and DROP. </li></ul><ul><li>Thank you for you attention! </li></ul><ul><li>Questions? </li></ul>

×