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.

1. Anatomical correlations for a hierarchical
multi-atlas segmentation of CT images
Oscar A. Jiménez del Toro
University of Applied Sciences
Western Switzerland
(HES-SO)

2. Overview
• Motivation
• VISCERAL
• Method
• Multi-atlas segmentation
• Image registration
• Hierarchical registration approach
• Experimental setup
• Results
• Conclusion
2

3. Overview
• Motivation
• VISCERAL
• Method
• Multi-atlas segmentation
• Image registration
• Hierarchical registration approach
• Experimental setup
• Results
• Conclusion
3

4. Motivation
• Anatomical segmentation is fundamental for
further image analysis and Computer-Aided
Diagnosis1
• Manual annotation and visual inspection is
time consuming for radiologists
• Accurate large scale data analysis
techniques are needed
1 K.Doi. Current status and future potential of computer-aided diagnosis in medical
imaging. British Journal of Radiology, 78:3-19, 2005.
4

5. VISCERAL Benchmarks
• Automatic segmentation of
anatomical structures (20)
– Visceral Benchmark 1: 12
ceCT test volumes*10
structures
– ISBI challenge: 5 ceCT, 5
wbCT test volumes*10
structures
• CT and MR images
(contrast-enhanced and
non-enhanced)

6. Overview
• Motivation
• VISCERAL
• Method
• Multi-atlas segmentation
• Image registration
• Hierarchical registration approach
• Experimental setup
• Results
• Conclusion
6

7. Overview
• Motivation
• VISCERAL
• Method
• Multi-atlas segmentation
• Image registration
• Hierarchical registration approach
• Experimental setup
• Results
• Conclusion
7

8. Hierarchical multi-atlas segmentation
• Use multiple atlases for the
estimation on a target
image
• Global and local alignment
• Hierarchical selection of the
registrations improves
results2
• Label fusion
2Jiménez del Toro et.al., Multi-structure Atlas-Based Segmentation using Anatomical
Regions of Interest. In proceeding of: Medical Image Computing and Computer Assisted
Intervention (MICCAI2013) MCV workshop, Nagoya, Japan, 2013

9. Image Registration
• Atlas =
Patient volume + labels
• Coordinate transformation
that increases spatial
correlation between images
• Multi-scale gaussian pyramid

10. Affine alignment
• Global

11. Affine alignment
• Global

12. Affine alignment
• Global

13. Affine alignment
• Global
• Local refinement for
independent structures

14. Affine alignment
• Global
• Local refinement for
independent structures
• Regions of interest based on
the morphologically dilated
initial estimations

15. Affine alignment
• Global
• Local refinement for
independent structures
• Regions of interest based on
the morphologically dilated
initial estimations

16. Affine alignment
• Global
• Local refinement for
independent structures
• Regions of interest based on
the morphologically dilated
initial estimations

17. Right
Kidney
Liver
Global
alignment
Urinary
Bladder
Right
Lung
Left
Lung
1st Lumbar
Vertebra
Gall-
bladder
Left
KidneyTrachea
Spleen
2nd Local
Affine
Hierarchical Registration approach
Affine
Local Affine
B-spline non-
rigid

18. Non-rigid alignment
• Non-rigid
• B-spline
• Multi-scale approach
• Faster optimization
due to better initial
alignment

19. Label fusion
• Majority voting threshold
• Classification on a per-voxel
basis
• Local registration errors are
reduced
• Threshold optimization

20. Overview
• Motivation
• VISCERAL
• Method
• Multi-atlas segmentation
• Image registration
• Hierarchical registration approach
• Experimental setup
• Results
• Conclusion
20

21. Overview
• Motivation
• VISCERAL
• Method
• Multi-atlas segmentation
• Image registration
• Hierarchical registration approach
• Experimental setup
• Results
• Conclusion
21

22. Experimental setup
• VISCERAL ISBI testset
• 5 contrast-enhanced CT volumes of the trunk
• 5 non-enhanced whole body CT
• Applied to 10 anatomical structures:
– Liver, lungs, kidneys, gallbladder, urinary bladder,
1st lumbar vertebra, trachea and spleen
• 7 independent atlases as trainingset

23. Results ISBI Challenge
Structure DICE ceCT DICE wbCT
Liver 0.908 0.823
Right Kidney 0.905 0.649
Left Kidney 0.923 0.678
Right Lung 0.963 0.967
Left Lung 0.952 0.969
Spleen 0.859 0.677
Trachea 0.83 0.855
Gallbladder 0.4 0.271
Urinary bladder 0.68 0.616
1st Lumbar vertebra 0.472 0.44

24. Results ISBI Challenge
Structure DICE ceCT DICE wbCT
Liver 0.908 0.823
Right Kidney 0.905 0.649
Left Kidney 0.923 0.678
Right Lung 0.963 0.967
Left Lung 0.952 0.969
Spleen 0.859 0.677
Trachea 0.83 0.855
Gallbladder 0.4 0.271
Urinary bladder 0.68 0.616
1st Lumbar vertebra 0.472 0.44

25. Conclusion
• Straightforward and fully automatic method
• Showed robustness in the segmentation of
multiple structures with high overlap for the
bigger structures (e.g. kidneys, liver, lungs)
• Smaller structures fared well compared to the
other approaches
• Future work:
– Extend to method to other modalities (CTwb ISBI challenge, MR)
– Improve speed of the algorithm

26. Questions???