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

transcript

Oscar A. Jiménez del Toro University of Applied Sciences Western Switzerland (HES-SO)

Overview •  Motivation •  VISCERAL •  Method

•  Multi-atlas segmentation •  Image registration •  Hierarchical registration approach

•  Experimental setup •  Results •  Conclusion

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

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)

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

Image Registration •  Atlas = Patient volume + labels •  Coordinate transformation

that increases spatial correlation between images

•  Multi-scale gaussian pyramid

Affine alignment •  Global

•  Local refinement for independent structures

•  Regions of interest based on the morphologically dilated initial estimations

Right Kidney

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

Non-rigid alignment

•  Non-rigid •  B-spline •  Multi-scale approach •  Faster optimization

due to better initial alignment

Label fusion •  Majority voting threshold •  Classification on a per-voxel

basis •  Local registration errors are

reduced •  Threshold optimization

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

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

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

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

Questions???

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

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