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Master EEAP
Systems and Images
Coronary lumen segmentation and axis extraction
in CTA images
Supervisors: Maciej Orkisz, C...
Arterial diseases remain as one of the main
causes of death in the world. In 2008 they
represented 30%of the total of deat...
Difficulties in coronaries
- Size of data: 512 x 512 x 250 voxels
(Resolution0.3 x 0.3 x 0.4 mm)
- Arteries diameters (1 –...
AGENDA
06/04/2012 4
Coronary lumen segmentation and axis
extraction in CTA images
- Master’s1st year – Lumen segmentation
...
Main existing approaches [Lesage 2009]
- Regiongrowing
- Activecontours
- Centerline-basedmethods [minimal path techniques...
Extraction of
3D features
Classification
strategy
Annotated
data
Supervised learning
Binary image
White= artery
CTA Image
...
Features
- Multi-scale analysis based on Gaussian
filtering [Deriche 1993]
- Eleven scales according to arteries
radius
Ca...
Results in carotids
Original image (left), SVM result (center), reference (right)
Partial results
• Many non-lumen voxels ...
AGENDA
06/04/2012 9
Coronary lumen segmentation and axis
extraction in CTA images
- Master’s1st year – Lumen segmentation
...
Master’s2nd year – Lumen segmentation
Extraction of
3D featuresin
spheres
Classification
strategy
Unsupervised learning
Bi...
Best results using
k-means clustering(k=2)
Results using
thresholding
TP:39632
TN:105798
FP:39331
FN:621
TP:25882
TN:13187...
Conclusionsand current work
• Still low Dice score
• The sphere can contain more than two classes, problems with K-means c...
AGENDA
06/04/2012 13
Coronary lumen segmentation and axis
extraction in CTA images
- Master’s1st year – Lumen segmentation...
Master’s2nd year – Axis extraction
Minimization ofthe total energy
How to detect the central axis?
P0
P1
energy
potential(...
Master’s2nd year – Axis extraction
Cost function
- Eigenvalues
– Metz. et al, 2008
– Krissian. etal, 1998
- Multi-scale an...
Master’s2nd year – Axis extraction
Based on:
- Multi-scale gradient analysis
- Flux
- Rings
Medialness measure
Probability...
Master’s2nd year – Axis extraction
- Circularity
- Flux
- Dijkstra
- Front propagation
Minimal cost
path
Cost function
CT
...
Master’s2nd year – Axis extraction
- VOI sampling
-13 oriented planes
- For each plane, find m(x,y)
- Analyse8 directions ...
Master’s2nd year – Axis extraction Minimalcost
path
-Use the cost map to find
* Minimal cost path
+ Two seeds
* Front prop...
The end…
Questions ??
06/04/2012 20
Coronary lumen segmentation and axis
extraction in CTA images
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ICVP CREATIS Presentation April 2012

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ICVP CREATIS Presentation April 2012. About coronary segmentation and vessel centerline extraction

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Transcript of "ICVP CREATIS Presentation April 2012"

  1. 1. 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
  2. 2. 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
  3. 3. 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
  4. 4. AGENDA 06/04/2012 4 Coronary lumen segmentation and axis extraction in CTA images - Master’s1st year – Lumen segmentation - Master’s2nd year • Coronary lumen segmentation • Axis extraction
  5. 5. 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 Coronary lumen segmentation and axis extraction in CTA images Master’s1st year – Lumen segmentation
  6. 6. 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 06/04/2012 6 Coronary lumen segmentation and axis extraction in CTA images Master’s1st year – Lumen segmentation
  7. 7. 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 Coronary lumen segmentation and axis extraction in CTA images Master’s1st year – Lumen segmentation
  8. 8. 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 Coronary lumen segmentation and axis extraction in CTA images
  9. 9. AGENDA 06/04/2012 9 Coronary lumen segmentation and axis extraction in CTA images - Master’s1st year – Lumen segmentation - Master’s2nd year • Coronary lumen segmentation • Axis extraction
  10. 10. 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 Coronary lumen segmentation and axis extraction in CTA images
  11. 11. 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 Coronary lumen segmentation and axis extraction in CTA images
  12. 12. 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 Coronary lumen segmentation and axis extraction in CTA images
  13. 13. AGENDA 06/04/2012 13 Coronary lumen segmentation and axis extraction in CTA images - Master’s1st year – Lumen segmentation - Master’s2nd year • Coronary lumen segmentation • Axis extraction
  14. 14. 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 Coronary lumen segmentation and axis extraction in CTA images
  15. 15. Master’s2nd year – Axis extraction 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 Coronary lumen segmentation and axis extraction in CTA images
  16. 16. Master’s2nd year – Axis extraction Based on: - Multi-scale gradient analysis - Flux - Rings Medialness measure Probability of being artery Edgeness 06/04/2012 16 Coronary lumen segmentation and axis extraction in CTA images
  17. 17. Master’s2nd year – Axis extraction - 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 Coronary lumen segmentation and axis extraction in CTA images
  18. 18. Master’s2nd year – Axis extraction - VOI sampling -13 oriented planes - For each plane, find m(x,y) - Analyse8 directions in2D - Get highestm Cost function 06/04/2012 18 Coronary lumen segmentation and axis extraction in CTA images
  19. 19. 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 Coronary lumen segmentation and axis extraction in CTA images Cost map
  20. 20. The end… Questions ?? 06/04/2012 20 Coronary lumen segmentation and axis extraction in CTA images

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