Anatomical correlations for a hierarchical multi-atlas segmentation of the pancreas in CT images
•
0 j'aime•741 vues
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.
Recommandé
Recommandé
Contenu connexe
Similaire à Anatomical correlations for a hierarchical multi-atlas segmentation of the pancreas in CT images
Similaire à Anatomical correlations for a hierarchical multi-atlas segmentation of the pancreas in CT images (20)
Plus de Institute of Information Systems (HES-SO)
Plus de Institute of Information Systems (HES-SO) (20)
Dernier
Dernier (20)
Anatomical correlations for a hierarchical multi-atlas segmentation of the pancreas in CT images
- 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. Overview • Introduction • VISCERAL • Method • Multi-atlas segmentation • Image registration • Hierarchical registration approach • Pancreas segmentation • Results 2
- 3. Overview • Introduction • VISCERAL • Method • Multi-atlas segmentation • Image registration • Hierarchical registration approach • Pancreas segmentation • Results 3
- 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. 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
- 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. Overview • Introduction • VISCERAL • Method • Multi-atlas segmentation • Image registration • Hierarchical registration approach • Pancreas segmentation • Results 8
- 9. Overview • Introduction • VISCERAL • Method • Multi-atlas segmentation • Image registration • Hierarchical registration approach • Pancreas segmentation • Results 9
- 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. Image Registration • Atlas = Patient volume + labels • Coordinate transformation that increases spatial correlation – Affine: Rotate, translate, scale – B-spline: Non-rigid
- 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. Label fusion • Majority voting threshold • Classification on a per-voxel basis • Threshold optimization
- 14. Overview • Introduction • VISCERAL • Method • Multi-atlas segmentation • Image registration • Hierarchical registration approach • Pancreas segmentation • Results 14
- 15. Overview • Introduction • VISCERAL • Method • Multi-atlas segmentation • Image registration • Hierarchical registration approach • Pancreas segmentation • Results 15
- 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. 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. 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. 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. 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
- 22. 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