Anatomical correlations for a hierarchical multi-atlas segmentation of CT images
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Transcript of Anatomical correlations for a hierarchical multi-atlas segmentation of CT images
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Anatomical correlations for a hierarchical multi-atlas segmentation of CT images
Oscar A. Jiménez del Toro University of Applied Sciences Western Switzerland (HES-SO)
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Overview • Motivation • VISCERAL • Method
• Multi-atlas segmentation • Image registration • Hierarchical registration approach
• Experimental setup • Results • Conclusion
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Overview • Motivation • VISCERAL • Method
• Multi-atlas segmentation • Image registration • Hierarchical registration approach
• Experimental setup • Results • Conclusion
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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
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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)
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Overview • Motivation • VISCERAL • Method
• Multi-atlas segmentation • Image registration • Hierarchical registration approach
• Experimental setup • Results • Conclusion
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Overview • Motivation • VISCERAL • Method
• Multi-atlas segmentation • Image registration • Hierarchical registration approach
• Experimental setup • Results • Conclusion
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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
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Image Registration • Atlas = Patient volume + labels • Coordinate transformation
that increases spatial correlation between images
• Multi-scale gaussian pyramid
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Affine alignment • Global
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Affine alignment • Global
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Affine alignment • Global
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Affine alignment • Global
• Local refinement for independent structures
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Affine alignment • Global
• Local refinement for independent structures
• Regions of interest based on the morphologically dilated initial estimations
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Affine alignment • Global
• Local refinement for independent structures
• Regions of interest based on the morphologically dilated initial estimations
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Affine alignment • Global
• Local refinement for independent structures
• Regions of interest based on the morphologically dilated initial estimations
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Right Kidney
Liver
Global alignment
Urinary Bladder
Right Lung
Left Lung
1st Lumbar Vertebra
Gall- bladder
Left Kidney Trachea
Spleen
2nd Local Affine
Hierarchical Registration approach
Affine
Local Affine
B-spline non-rigid
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Non-rigid alignment
• Non-rigid • B-spline • Multi-scale approach • Faster optimization
due to better initial alignment
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Label fusion • Majority voting threshold • Classification on a per-voxel
basis • Local registration errors are
reduced • Threshold optimization
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Overview • Motivation • VISCERAL • Method
• Multi-atlas segmentation • Image registration • Hierarchical registration approach
• Experimental setup • Results • Conclusion
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Overview • Motivation • VISCERAL • Method
• Multi-atlas segmentation • Image registration • Hierarchical registration approach
• Experimental setup • Results • Conclusion
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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
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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
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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
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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
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Questions???