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Supervised learning for medical imaging analysis and diagnosis: segmentation and detection in 3D Le Lu Siemens Corporate Research Computed Aided Medical Imaging Diagnosis Ultimate Goal Semantic understanding of functions of human body via medical imaging modalities Quantitative measurement and diagnosis for more accurate, better performed healthcare “Human-machine” collaborative system; CAD as a second-reader Computed Aided Medical Imaging Diagnosis “Historical” heuristic approach “Natural” Mainstream, useful, can be limited … Statistical learning approach Supervised (discriminative boosting, SVM, …) Generative (density model, …) Hybrid, … Exploit learned anatomical domain knowledge Two samples of work Representation + Computation Accurate Polyp Segmentation for 3D CT Colonography Using Multi-Staged Probabilistic Binary Learning and Compositional Model1, Le Lu, et. al., CVPR'2008: IEEE Conf. on Computer Vision and Pattern Recognition, June, 2008, Anchorage, USA. Simultaneous Detection and Registration for Ileo-Cecal Valve Detection in 3D CT Colonography2, Le Lu, et. al., ECCV'2008: European Conf. on Computer Vision, October, 2008, Marseille, France. 1 Clinic talk at New Era of Virtual Colonoscopy meeting at MICCAI’08 2 Clinic evaluation and talk at RSNA’07 Previous work J. Yao, M. Miller, M. Franaszek and R. Summers, Colonic polyp segmentation in CT Colongraphy-based on fuzzy clustering and deformable models, IEEE Trans. on Medical Imaging, 23(11):13441352, 2004. A. Jerebko, S. Lakare, P. Cathier, S. Periaswamy, L. Bogoni, Symmetric Curvature Patterns for Colonic Polyp Detection, MICCAI (2) 2006: 169-176. R. Summers, J. Yao, C. Johnson, CT Colonography with ComputerAided Detection: Automated Recognition of Ileo-cecal Valve to Reduce Number of False-Positive Detections, Radiology, 233:266272, (2004). Building blocks Learner: Probabilistic Boosting Tree Z. Tu, Probabilistic boosting-tree: Learning discriminative methods for classification, recognition, and clustering, Int’l Conf. Computer Vision, 2005. Features: Multiscale Steerable features: Axis-pattern, Boxpattern in 3D (ICCV’07, CVPR’08, ECCV’08) Curve-parsing features: boundary (or bi-partition) learning in 1D (CVPR’08) Colon CAD system What’s a polyp (in textbook)? Copyright by …. Polyps in 3D/2D pictures Our polyp segmentation system makes use of a three-stage binary classification framework and a hierarchical, compositional shape representation integrates low-, and mid-level contextual information for discriminative learning shows superior polyp segmentation reliability rate of 98.2% (i.e., errors =< 3mm), compared with previous work of about 75% ~ 80% offers robustness testing with disturbances (thanks to compositional shape model) Flow-chart Step 0: CAD-input Step 1: polyp tip finding 1. 2. 3. 3D Point-detector (with probability output) Grouping by C-C Geometric centroid on surface Probabilistic spatial prior Step 1.5: marching-cubes & polarcoordinates Step 2: polyp interior-exterior detection Output of step 2 Step 3: polyp boundary detection Step 3.5: smooth & measurement Smoothness: Gaussian, Viterbi-like Dynamic Programming, Loopy belief-propagation Flow-chart Experiments-1: accuracy Five-fold cross-validation: Training (left, 221 polyps) versus Testing (right, 54 polyps) Experiments-2: comparison Left [Jerebko06] Right [without stacked learning] Experiments-3: comparison Experiments-4: Robustness See table 1 for numerical results Experiments-4: Robustness Discussion on stacked learning Stacked generality: a classifier combination method to learn a linear or non-linear function of multiple classifier outputs D. H. Wolpert, Stacked generalization, Neural Networks, 5(2): 241-259, 1992. Our stacked learning is learning a new (hopefully easier) task from the structure outputs of another classifier (i.e., supervised embedding) Summary Our multi-staged probabilistic learning framework decomposes a complex learning task as a sequence of better trainable subtasks. A local-to-global scaled 3D data evidences are gradually integrated with this learning process to achieve robustness. Hierarchical, stacked Learning did improve direct, multi-parts, polyp profile learning. Our compositional model tackles the problem of “curse of dimensionality”, which makes statistical learning practically more feasible when applying to a highly complex 3D medical images problem. Robustness of polyp measurement w.r.t. multi-clicks is achieved, thanks to shared curve learning patterns among different polyps. What’s Ileo-cecal Valve? Ileo-Cecal Valve can present with bumpy, polyp-like substructures • Importance: a CAD system can mistakenly detect those bumps – resulting in polyp false-positives (FPs), up to 15~20% • Previous approach: Summers et al. 2004, Radiology – technique not fully automatic Why difficult? ICV appears huge within-class variations in both its internal shape/appearance and external spatial configurations. ICV is a relatively small size (compared with heart, liver, even kidney) and deformable human organ which opens and closes as a valve (connecting colon and small intestine). ICV size and shape are sensitive to the patient weight and/or whether ICV is diseased. ICV position and orientation also vary, of being a part of colon which is highly deformable. Looking for an easier job? Is there an easier job preceding the final task? More importantly, how it can make the final task easier, more solvable (data bootstrapping, back tracing; searching range, …)? An intuitive example, “surface-aided object localization”, or “rotation-invariant face detection”? Overall: computationally less expensive! (Easier) Local step: trainable!! (via classifier ROC analysis) Global solution: back-traceable!!! (via training data bootstrapping) Brief review of our solution A general 3D object detection algorithm by proposed incremental parameter learning in full 3D space Prior learning using domain specific knowledge for efficiency Prior learning in the same framework (or, spirit) of incremental parameter learning T -> S -> R System Incremental Parameter Learning for 3D object localization Analogy to twenty-questions [Geman & Jedynak], but simpler Equivalent to exhaustive search in {T,S,R} if we can train a perfect classifier (100% recall at 0% false positive rate) at each step. Trade explicit, exhaustive searching for parameter estimation with implicit within-class variation modeling using data-driven clustering inside supervised classifier training (especially at early learning stage). PBT, cluster based tree, multiplicative kernels, … Robustness for non-perfect classifier Keeping multiple hypotheses relaxes the requirement for training/detection accuracy (sequential MC) Cluster based sampling or Non-Maximum Suppression for multiple object detection Detection Accuracy: Decreasing distances from the positive-class decision boundary to the ground-truth (annotation) Decreasing distance margins between positive and negative class decision boundaries over stages Training ROCs Experiments Learning-based Component for Suppression of False Positives Located on the Ileo-Cecal Valve1: Evaluation of Performance on 802 CTC Volumes L. Bogoni, A. Barbu, S. Lakare, M. Dundar, M. Wolf, L. Lu Computer-Aided Diagnosis and Knowledge Solutions Siemens Medical Solutions USA, Inc. 1research/product prototype, not commercially available RSNA 2007, Chicago, USA Training Data Cases with clean prep 116 volumes 8 sites Siemens, GE, Toshiba MDCT 4, 16 and 64 slice scanners 116 ileo-cecal valves were box annotated and then used for training Results – Standalone System Tested on 116 training cases Detection Rate: 98.3% (114 out of 116) 1 false positive Tested on 142 unseen clean cases Detection Rate: 93.7% (133 out of 142) 5 false positives None of the false positives is a polyp Running time is 4~10 seconds/volume Detection Results (Clean) Detection Results (Tagged) Results – Polyp FP Reduction 412 Test Cases total (data are independent!) Clean preparation 211 patients, 407 volumes 10 sites Siemens, GE, Toshiba MDCT 4, 16 and 64 slice scanners Tagged preparation (combinations of iodine & barium) 201 patients, 395 volumes 4 sites Siemens and GE MDCT 16 and 64 slice scanners No E-cleansing needed! Integration into CAD Prototype* Processing Flow: Input Data Candidate Generation Feature Computation Classification * Work in Progress, not available commercially CAD marks Integration into CAD Prototype ICV Detector as Post-Filter: Input Data Candidate Generation Feature Computation Classification ICV Suppression CAD marks Integrated Results – Post Filter Clean cases Tagged cases Per Patient FP count reduced from 3.92 to 3.72 (5.5%) Per Volume FP count reduced from 2.04 to 1.92 (5.9%) Per Patient FP count reduced from 6.2 to 5.78 (6.8%) Per Volume FP count reduced from 3.15 to 2.94 (6.7%) One polyp out of 124 polyps was mislabeled as ICV (close to ICV) S. Kim, et al. Two- versus Three-dimensional Colon Evaluation with Recently Developed Virtual Dissection Software for CT Colonography, Radiology 2007; 244: 852-864. Integration into CAD Prototype ICV detector integrated at feature stage: Input Data Candidate Generation Feature Computation ICV Suppression Classification CAD marks Integrated Results – FC Stage The same performance of polyp FP reduction is maintained. No polyp out of 124 polyps was labeled as ICV. The previously lost polyp was preserved when combining the output of the ICV detector with additional features A N-box ICV model was later proposed in ECCV’08, which increases the mean overlap ratio from 74.9% to 88.2% and surprisingly removes 30.2% more Polyp FPs without losing true polyps (N=2). Conclusion Explicit ICV anatomical knowledge can be learned by system to improve CAD performance Approach generalizes well in both clean and tagged CT volumes Benefits CAD marks on ICV are suppressed both in clean and tagged preparation Modest Reduction in false positives (especially nuisance fps) Can potentially reduce interpretation time for Radiologists No detriment to system sensitivity Can potentially increase acceptance of CAD systems by avoiding obvious false positives Acknowledgement Dr Adrian Barbu for technical collaboration Other coauthors for discussion, clinical and system support Questions?