learning based hierarchical vessel segmentation learning based hierarchical vessel segmentation...
TRANSCRIPT
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Learning Based Learning Based Hierarchical Hierarchical Vessel SegmentationVessel Segmentation
Presenter: Richard Socher Richard Socher
www.socher.org
Authors:Authors: Richard SocherRichard Socher Adrian BarbuAdrian Barbu Dorin ComaniciuDorin Comaniciu
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OverviewOverview
• Background– Machine Learning
• Marginal Space Learning• Probabilistic Boosting Trees
– Visual Features• Haar and Steerable Features
• Hierarchical Vessel Segmentation
• Results and Future Work
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Marginal Space LearningMarginal Space Learning
• General Framework which tackles problem of high dimensional parameter spaces
• Posterior distribution of the parameters lies in a small region of the n-dimensional parameter space
• Idea: Start in small marginal spaces and increase dimensionality of the search space
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• Fewer parameters have to be examined
• Large speed-ups
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Marginal Spaces of VesselsMarginal Spaces of Vessels
• Marginal Space 1:Gradient Candidates
• Marginal Space 2: Cross Segments
• Marginal Space 3:Quadrilaterals
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Machine Learning: Probabilistic Machine Learning: Probabilistic Boosting TreesBoosting Trees
• Each node is a strong boosting classifier:
• Transform into probability:
• During training, samples are divided into subnodes
• During testing, the top recursively collects the probabilities:
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f T (x) = 1Z
P Tt=1 ®tht(x)
q(+1jx) = exp(2f T (x))1+exp(2f T (x))
~pN (yjx) = q(+1jx)~pr ight(y) (3)
+ q(¡ 1jx)~plef t(y) (4)
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Visual FeaturesVisual Features• Sample features and use
them as input to classifier
• Haar Features– Thousands of cheap
features through integral image:
• Steerable Features– Useful for finding the
orientation and scale of an object, given its location
– Intensity, gradient,…
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Hierarchical Vessel Hierarchical Vessel SegmentationSegmentation
1. Learning Based Edge Detection
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2. Cross-Segment Detection: Width
3. Quadrilateral Detection: Length 4. Dynamic Programming
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Level 1 – Learning Based Edge Level 1 – Learning Based Edge DetectionDetection
• Goal: Rough estimation of vessel borders
• Candidates are pixels with large gradient
• Annotation used to create positive and negative samples for the PBT learning
• Fast and little limitation for higher levels
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Samples with gradient direction (gy,-gx)
Mx;y = PPBT(I (x;y) = edgejHaarx;y)Original Frame Large Gradients
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Level 2: Cross Segment DetectionLevel 2: Cross Segment Detection
• Goal: cross segments, loosely corresponding to width of vessel
• Candidates are created by going in opposite gradient direction from all locations of Level 1, until another point from Level 1 is hit
• Segments and their Haar Features are given to a PBT
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Level 3: Quadrilateral DetectionLevel 3: Quadrilateral Detection• Goal: find pairs of cross segments that, if connected as a quadrilateral, capture an area of the vessel.• The probability of such a quadrilateral shows how likely two cross segments are connected in the complete vessel.
• Steerable Features are sampled and used for training a PBT:• Gradient, grey value; probability map of Level 1, differences in grey value
Coordinate System for steerable Features Positive and Negative Training Samples
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Level 4: Dynamic ProgrammingLevel 4: Dynamic Programming
• Goal: Final vessel segmentation, the most likely connection of cross segments
• Formulation as lowest cost path in weighted graph G = (V,E)V = cross-segments, E = edges between segments, if they form a quadrilateralWeight(e(v1,v2)) = log((1-p)/p) p = P(Quadrialateral(v1,v2))
• Solved by dynamic programming
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ResultsResults
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Results on Results on unseen data of very unseen data of very low qualitylow quality
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Results on Testing SetResults on Testing Set• Training on 134 frames• Testing on 64 frames• Detection Rate: 90.1% False Alarm 29%
• Example of distracting side vessel14
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Future WorkFuture Work
• Extension to full vessel tree through a junction point detector
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ConclusionConclusion• Hierarchical learning based vessel segmentation method
– highly driven by data: applicable to any tube like structure– generalizes well to lower quality X-ray images.
• New representation of a vessel consisting of three marginal spaces: border points, vessel width and vessel pieces (quadrilaterals)
• Novel use of MSL and steerable features in segmentation of objects without a mean shape.
• Results for single vessel segmentation are preliminary but promising• Work in progress: time consistency, full tree, …
• More details in my thesis: www.socher.org
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Thank you!Thank you!Questions?Questions?
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1. Learning Based Edge Detection 2. Cross-Segment Detection: Width
3. Quadrilateral Detection: Length 4. Dynamic Programming
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ReferencesReferences• Friedman, J. H., Hastie, T. and Tibshirani, R., "Additive Logistic Regression: a
Statistical View of Boosting." (Aug. 1998)• A. Torralba, K. P. Murphy and W. T. Freeman. (2004). "Sharing features: efficient
boosting procedures for multiclass object detection". Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). Pp 762- 769.
• http://people.csail.mit.edu/torralba/shortCourseRLOC/boosting/boosting.html• T. Zhang, "Convex Risk Minimization", Annals of Statistics, 2004.• Zhuowen Tu, “Probabilistic boosting-tree: Learning discriminative models for
classification, recognition, and clustering.,” in ICCV, 2005, pp. 1589–1596.• http://www.stat.ucla.edu/~ztu/publication/tu_z_pbt.pdf• Y. Zheng, A. Barbu, B. Georgescu, M. Scheuering, and D. Comaniciu, “Fast automatic
heart chamber segmentation from 3d ct data using marginal space learning and steerable features,” in IEEE Int’l Conf. Computer Vision (ICCV’07), Rio de Janeiro, Brazil, 2007.
• http://www.caip.rutgers.edu/~comanici/Papers/HeartSegmentation_ICCV07.pdf• P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple
features,” 2001.• http://research.microsoft.com/~viola/Pubs/Detect/violaJones_CVPR2001.pdf
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