Anatomical correlations for a hierarchical
multi-atlas segmentation of the pancreas in
CT images
Oscar A. Jiménez del Toro...
Overview
•  Introduction
•  VISCERAL
•  Method
•  Multi-atlas segmentation
•  Image registration
•  Hierarchical registrat...
Overview
•  Introduction
•  VISCERAL
•  Method
•  Multi-atlas segmentation
•  Image registration
•  Hierarchical registrat...
Introduction
•  Anatomical segmentation is fundamental for
further image analysis1
•  Different methods proposed2,3 (regre...
VISual Concept Extraction challenge in
RAdioLogy
•  EU funded project (2012-2015)
–  HES-SO, ETHZ, UHD, MUW, TUW,
Gencat
•...
Cloud environment
Benchmark 2 Anatomy
•  Automatic segmentation of
anatomical structures (20)
and landmark detection
•  Define challenges in...
Overview
•  Introduction
•  VISCERAL
•  Method
•  Multi-atlas segmentation
•  Image registration
•  Hierarchical registrat...
Overview
•  Introduction
•  VISCERAL
•  Method
•  Multi-atlas segmentation
•  Image registration
•  Hierarchical registrat...
Hierarchical multi-atlas segmentation
•  Use multiple atlases for
the estimation on a target
image
•  Global and local ali...
Image Registration
•  Atlas =
Patient volume + labels
•  Coordinate transformation
that increases spatial
correlation
– Af...
Right
Kidney
Liver
Global
alignment
Urinary Bladder Right Lung Left Lung1st Lumbar Vertebra
Gall-
bladder
Left KidneyTrach...
Label fusion
•  Majority voting threshold
•  Classification on a per-voxel
basis
•  Threshold optimization
Overview
•  Introduction
•  VISCERAL
•  Method
•  Multi-atlas segmentation
•  Image registration
•  Hierarchical registrat...
Overview
•  Introduction
•  VISCERAL
•  Method
•  Multi-atlas segmentation
•  Image registration
•  Hierarchical registrat...
Right
Kidney
Liver
Global
alignment
Urinary Bladder Right Lung Left Lung1st Lumbar Vertebra
Gall-
bladder
Left KidneyTrach...
Right
Kidney
Liver
Global
alignment
Urinary Bladder Right Lung Left Lung1st Lumbar Vertebra
Gall-
bladder
Left KidneyTrach...
Experimental setup
•  VISCERAL Benchmark 1 testset
•  10 contrast-enhanced CT volumes of the trunk
•  Added to segmentatio...
Results
•  Average DICE score for Pancreas: 0.52
Structure DICE Rank in VISCERAL
Benchmark 1
Liver 0.918 1st
Right Kidney ...
Results
•  Average DICE score for Pancreas: 0.52
Structure DICE Rank in VISCERAL
Benchmark 1
Liver 0.918 1st
Right Kidney ...
Conclusion
•  Full automatic method
•  Requires little or no feedback from the user
•  Showed robustness in the segmentati...
Sierre, Switzerland
Questions???
Anatomical correlations for a hierarchical multi-atlas segmentation of the pancreas in CT images
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Anatomical correlations for a hierarchical multi-atlas segmentation of the pancreas in CT images

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Many medical image analysis techniques require an initial localization and segmentation of anatomical structures. As part of the VISCERAL benchmarks on Anatomy segmentation, a hierarchical multi-atlas multi-structure segmentation approach guided by anatomical correlations is proposed. The method begins with a global alignment of the volumes and refines the alignment of the structures locally. The alignment of the bigger structures is used as reference for the smaller and harder to segment structures. The method is evaluated in the ISBI VISCERAL testset on ten anatomical structures in both contrast-enhanced and non-enhanced computed tomography scans. The proposed method obtained the highest DICE overlap score for some structures like kidneys and gallbladder. Similar segmentation accuracies compared to the highest results of the other methods proposed in the challenge are obtained for most of the other structures segmented with the method.

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Anatomical correlations for a hierarchical multi-atlas segmentation of the pancreas in CT images

  1. 1. Anatomical correlations for a hierarchical multi-atlas segmentation of the pancreas in CT images Oscar A. Jiménez del Toro University of Applied Sciences Western Switzerland (HES-SO)
  2. 2. Overview •  Introduction •  VISCERAL •  Method •  Multi-atlas segmentation •  Image registration •  Hierarchical registration approach •  Pancreas segmentation •  Results 2
  3. 3. Overview •  Introduction •  VISCERAL •  Method •  Multi-atlas segmentation •  Image registration •  Hierarchical registration approach •  Pancreas segmentation •  Results 3
  4. 4. Introduction •  Anatomical segmentation is fundamental for further image analysis1 •  Different methods proposed2,3 (regression random forests, level set…) •  Comparison of multiple approaches for the same public dataset is uncommon 4
  5. 5. VISual Concept Extraction challenge in RAdioLogy •  EU funded project (2012-2015) –  HES-SO, ETHZ, UHD, MUW, TUW, Gencat •  Organize competitions on medical image analysis on big data •  All computation done in the cloud •  Segmentation benchmark •  Retrieval benchmark •  Annotation by medical doctors
  6. 6. Cloud environment
  7. 7. Benchmark 2 Anatomy •  Automatic segmentation of anatomical structures (20) and landmark detection •  Define challenges in large scale data (aprox. 10TB) processing •  CT and MR images (contrast-enhanced and non-enhanced)
  8. 8. Overview •  Introduction •  VISCERAL •  Method •  Multi-atlas segmentation •  Image registration •  Hierarchical registration approach •  Pancreas segmentation •  Results 8
  9. 9. Overview •  Introduction •  VISCERAL •  Method •  Multi-atlas segmentation •  Image registration •  Hierarchical registration approach •  Pancreas segmentation •  Results 9
  10. 10. Hierarchical multi-atlas segmentation •  Use multiple atlases for the estimation on a target image •  Global and local alignment •  Hierarchical selection of the registrations improves results •  Label fusion
  11. 11. Image Registration •  Atlas = Patient volume + labels •  Coordinate transformation that increases spatial correlation – Affine: Rotate, translate, scale – B-spline: Non-rigid
  12. 12. Right Kidney Liver Global alignment Urinary Bladder Right Lung Left Lung1st Lumbar Vertebra Gall- bladder Left KidneyTrachea Spleen 2nd Local Affine Hierarchical Registration approach Affine Local Affine B-spline non- rigid
  13. 13. Label fusion •  Majority voting threshold •  Classification on a per-voxel basis •  Threshold optimization
  14. 14. Overview •  Introduction •  VISCERAL •  Method •  Multi-atlas segmentation •  Image registration •  Hierarchical registration approach •  Pancreas segmentation •  Results 14
  15. 15. Overview •  Introduction •  VISCERAL •  Method •  Multi-atlas segmentation •  Image registration •  Hierarchical registration approach •  Pancreas segmentation •  Results 15
  16. 16. Right Kidney Liver Global alignment Urinary Bladder Right Lung Left Lung1st Lumbar Vertebra Gall- bladder Left KidneyTrachea Spleen 2nd Local Affine Pancreas segmentation Affine Local Affine B-spline non- rigid
  17. 17. Right Kidney Liver Global alignment Urinary Bladder Right Lung Left Lung1st Lumbar Vertebra Gall- bladder Left KidneyTrachea Spleen 2nd Local Affine Affine Local Affine B-spline non- rigid Liver Right Kidney Pancreas segmentation
  18. 18. Experimental setup •  VISCERAL Benchmark 1 testset •  10 contrast-enhanced CT volumes of the trunk •  Added to segmentation method of 10 structures: – Liver, lungs, kidneys, gallbladder, urinary bladder, 1st lumbar vertebra, trachea and spleen •  7 independent atlases as trainingset
  19. 19. Results •  Average DICE score for Pancreas: 0.52 Structure DICE Rank in VISCERAL Benchmark 1 Liver 0.918 1st Right Kidney 0.913 1st Left Kidney 0.921 1st Right Lung 0.965 3rd Left Lung 0.955 3rd Spleen 0.852 3rd Trachea 0.836 2nd Gallbladder 0.566 1st Urinary bladder 0.7 3rd 1st Lumbar vertebra 0.522 2nd
  20. 20. Results •  Average DICE score for Pancreas: 0.52 Structure DICE Rank in VISCERAL Benchmark 1 Liver 0.918 1st Right Kidney 0.913 1st Left Kidney 0.921 1st Right Lung 0.965 3rd Left Lung 0.955 3rd Spleen 0.852 3rd Trachea 0.836 2nd Gallbladder 0.566 1st Urinary bladder 0.7 3rd 1st Lumbar vertebra 0.522 2nd
  21. 21. Conclusion •  Full automatic method •  Requires little or no feedback from the user •  Showed robustness in the segmentation of multiple structures with high overlap •  Fared well when compared to other methods of the VISCERAL Benchmark 1 •  Future work: –  Extend to method to other modalities (CTwb ISBI challenge, MR) –  Test in a bigger dataset for VISCERAL Benchmark 2 Anatomy
  22. 22. Sierre, Switzerland Questions???

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