ICVP CREATIS Presentation April 2012

Master EEAP
Systems and Images
Coronary lumen segmentation and axis extraction
in CTA images
Supervisors: Maciej Orkisz, CREATIS
Marcela Hernández, Universidad de los Andes
Ricardo A. Corredor
Fabián Gutiérrez

Arterial diseases remain as one of the main
causes of death in the world. In 2008 they
represented 30%of the total of deaths.
World Health Organization
Fact Sheet No 317 September 2011
Research subjects
- Axis extraction
- Lumen segmentation
- Detectionand quantification
of disease
Diagnosis Treatment
HEALTHY STENOTIC
- Carotids
- Cerebralarteries
- Coronary tree
Coronary tree
Prevention
IMAGE PROCESSING
06/04/2012 2

Difficulties in coronaries
- Size of data: 512 x 512 x 250 voxels
(Resolution0.3 x 0.3 x 0.4 mm)
- Arteries diameters (1 – 7 mm)
- Shape variability
- Imageartifacts (heart movement, noise,…)
- Contrast attenuation, anomalies, presence
of structures with similar intensities.
Planes orthogonal to the central axis near the ostium
(top), with a calcification (middle), and in a distal
zone (bottom)
06/04/2012 3
Coronary lumen segmentation and axis
extraction in CTA images

AGENDA
06/04/2012 4
- Master’s1st year – Lumen segmentation
- Master’s2nd year
• Coronary lumen segmentation
• Axis extraction

Main existing approaches [Lesage 2009]
- Regiongrowing
- Activecontours
- Centerline-basedmethods [minimal path techniques]
- Stochastic frameworks
Alternative
- Machinelearning
* Methods for automation of vascular lesions detection in computed
tomographyimages [Zuluaga 2011]
* Robust shape regressionfor supervised vessel segmentationand its
applicationto coronarysegmentationin CTA [Schaap 2011]
* Machine learningbased vesselness measurement for coronary artery
segmentationincardiac CT volumes [Zheng 2011]
* Applications in 2D medical images (angiography [Socher 2008],
retina[Lupascu 2010]) 5
Master’s1st year – Lumen segmentation

Extraction of
3D features
Classification
strategy
Annotated
data
Supervised learning
Binary image
White= artery
CTA Image
Arteries
Carotidsand coronaries
Features
Next slide…
Learning technique
Support Vector Machines
Random Forests
Evaluation
Dicescore
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Features
- Multi-scale analysis based on Gaussian
filtering [Deriche 1993]
- Eleven scales according to arteries
radius
Carotids, 3mm - 12mm
Coronaries, 1mm - 6mm
- Hessian matrix eigenvalues
- Gradient magnitude
- Intensity
TOTAL: 55 features by voxel
SVM
- Kernel RBF [Chang 2011]
- C: regularization constant
- : kernel parameter
Random Forest
- mtry = amount of features
- mtree= amount of trees
06/04/2012 7

Results in carotids
Original image (left), SVM result (center), reference (right)
Partial results
• Many non-lumen voxels removed
from result (high TN)
• With current features, vein = artery
High false positives rate
Lowdice ( best score: 35% )
• Slow training
(Worstcases 30 hourswith 1’200.000voxels)
Evaluation
MICCAI 2008 - Coronary Artery Tracking
- 8 training annotated (axis + radius) datasets
MICCAI 2009 - Carotid lumen segmentation
- 15 training annotated (lumen mask) datasets
06/04/2012 8

AGENDA
06/04/2012 9
• Axis extraction

Master’s2nd year – Lumen segmentation
Extraction of
3D featuresin
spheres
Classification
strategy
Unsupervised learning
Binary image
White= artery
Arteries
Coronaries
Features
Same features + distance to
axis
Learning technique
K-means clustering
Evaluation
Dicescore
Axisextracted
Spheres
[Carrillo 2007]
06/04/2012 10

Best results using
k-means clustering(k=2)
Results using
thresholding
TP:39632
TN:105798
FP:39331
FN:621
TP:25882
TN:131878
FP:13251
FN:14371
Accuracy: 0.7844
Specificity: 0.7289
Sensitivity: 0.9845
DICESCORE: 0.6648
Accuracy: 0.8510
Specificity: 0.9086
Sensitivity: 0.6429
DICESCORE:0.6520
TOTAL VOXELS IN VOI: 185.382
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
TP TN FP FN
Clustering
Thresholding
RCA Reference mask Clustering result Thresholding
Gray-level [1150-1900]
06/04/2012 11

Conclusionsand current work
• Still low Dice score
• The sphere can contain more than two classes, problems with K-means clustering
• Detailed analysis of features through the vessel. (Add new features, e.g. contours
information?)
• Try a more robust clustering technique (Mean shift?)
Master’s2nd year – Lumen segmentation
06/04/2012 12

AGENDA
06/04/2012 13
• Axis extraction

Master’s2nd year – Axis extraction
Minimization ofthe total energy
How to detect the central axis?
P0
P1
energy
potential(cost)
regularization
06/04/2012 14

Cost function
- Eigenvalues
– Metz. et al, 2008
– Krissian. etal, 1998
- Multi-scale analysis
– Wink. et al, 2004
– Li. et al, 2007
- Other approaches
– GulsunTek2008(a)
– GulsunTek. etal, 2008 (b)
– Lessage. et al, 2009
– Tessman. etal, 2011
06/04/2012 15

Based on:
- Multi-scale gradient analysis
- Flux
- Rings
Medialness measure
Probability of
being artery
Edgeness
06/04/2012 16

- Circularity
- Flux
- Dijkstra
- Front propagation
Minimal cost
path
Cost function
CT
Image
Seed(s)
Axis
Hypothesis
- The vessel contour has a higher contrast
- Circularity assumption in orthogonal planes
- Minimum and maximum radius
Cost
map
06/04/2012 17

- VOI sampling
-13 oriented planes
- For each plane, find m(x,y)
- Analyse8 directions in2D
- Get highestm
Cost function
06/04/2012 18

Master’s2nd year – Axis extraction Minimalcost
path
-Use the cost map to find
* Minimal cost path
+ Two seeds
* Front propagation
+ Starting seed at ostium location
-Detect bifurcations analyzing the surface
of the VOI
06/04/2012 19
Cost
map

The end…
Questions ??
06/04/2012 20

ICVP CREATIS Presentation April 2012

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ICVP CREATIS Presentation April 2012