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ICVP CREATIS Presentation April 2012
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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

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

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ICVP CREATIS Presentation April 2012 ICVP CREATIS Presentation April 2012 Presentation Transcript

  • 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 Coronary lumen segmentation and axis extraction in CTA images - 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 Coronary lumen segmentation and axis extraction in CTA images 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 06/04/2012 6 Coronary lumen segmentation and axis extraction in CTA images Master’s1st year – Lumen segmentation
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • The end… Questions ?? 06/04/2012 20 Coronary lumen segmentation and axis extraction in CTA images