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Shaoting Zhang, Yiqiang Zhan, Yan Zhou, and Dimitris N. Metaxas

Efficient Sparse Shape Composition with its Applications in Biomedical Image Analysis: An Overview. Shaoting Zhang, Yiqiang Zhan, Yan Zhou, and Dimitris N. Metaxas. Outline. Introduction Our methods Experiments Future work. Introduction Motivations. Chest X-ray.

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Shaoting Zhang, Yiqiang Zhan, Yan Zhou, and Dimitris N. Metaxas

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  1. Efficient Sparse Shape Composition with its Applications in Biomedical Image Analysis: An Overview Shaoting Zhang, Yiqiang Zhan, Yan Zhou, and Dimitris N. Metaxas

  2. Outline • Introduction • Our methods • Experiments • Future work

  3. IntroductionMotivations Chest X-ray Rat brain structure in MR Microscopy Lung CAD Liver in low-dose whole-body CT Segmentation (i.e., finding 2D/3D ROI) is a fundamental problem in medical image analysis. We use deformable models and shape prior. Shape representation is based on landmarks.

  4. IntroductionMotivations Segmentation system • Shape prior is important: • Automatic, accuracy, efficiency, robustness. • Handle weak or misleading appearance cues from image information. • Discover or preserve complex shape details.

  5. IntroductionChallenges – shape prior modeling • Good shape prior method needs to: • Handle gross errors of the input data. • Model complex shape variations. • Preserve local shape details.

  6. IntroductionRelevant work – shape prior methods • Handling gross errors. • RANSAC + ASM [M. Rogers, ECCV’02] • Robust Point Matching [J. Nahed, MICCAI’06] • Complex shape variation (multimodal distribution). • Mixture of Gaussians [T. F. Cootes, IVC’97] • Patient-specific shape [Y. Zhu, MICCAI’09] • Recovering local details. • Sparse PCA [K. Sjostrand, TMI’07] • Hierarchical ASM [D. Shen, TMI’03]

  7. Our ApproachShape prior using sparse shape composition ... ≈ • Our method is based on two observations: • An input shape can be approximately represented by a sparse linear combination of training shapes. • The given shape information may contain gross errors, but such errors are often sparse.

  8. Our ApproachShape prior using sparse shape composition Number of nonzero elements ... ... ≈ ≈ Global transformation operator Global transformation parameter Input y Weight x Aligned data matrix D Dense x Sparse x • Formulation: • Sparse linear combination:

  9. Our ApproachShape prior using sparse shape composition • Non-Gaussian errors:

  10. Our ApproachShape prior using sparse shape composition Zhang, Metaxas, et.al.: MedIA’11 • Why it works? • Robust: Explicitly modeling “e” with L0 norm constraint. Thus it can detect gross (sparse) errors. • General: No assumption of a parametric distribution model (e.g., a unimodal distribution assumption in ASM). Thus it can model complex shape statistics. • Lossless: It uses all training shapes. Thus it is able to recover detail information even if the detail is not statistically significant in training data.

  11. Experiments2D lung localization in X-ray • Setting: • Manually select landmarks for training purpose. • 200 training and 167 testing. • To locate the lung, detect landmarks, then predict a shape to fit them. • Sensitivity (P), Specificity (Q), Dice Similarity Coefficient.

  12. Experiments 2D lung localization in X-ray • Handling gross errors Detection PA ASM RASM NN TPS Sparse1 Sparse2 Procrustes analysis Active Shape Model Robust ASM Nearest Neighbors Thin-plate-spline Without modeling “e” Proposed method

  13. Experiments2D lung localization in X-ray • Multimodal distribution Detection PA ASM/RASM NN TPS Sparse1 Sparse2

  14. Experiments2D lung localization in X-ray • Recover local detail information Detection PA ASM/RASM NN TPS Sparse1 Sparse2

  15. Experiments2D lung localization in X-ray • Sparse shape components • ASM modes: ≈ + + 0.5760 0.2156 0.0982

  16. Experiments2D lung localization in X-ray • Mean values (µ) and standard deviations (σ). σ µ Left lung Right lung 1)PA, 2)ASM, 3)RASM, 4)NN, 5)TPS, 6)Sparse1, 7)Sparse2

  17. Future WorkDictionary Learning Dictionary size = 128 #Coefficients = 800 #Input samples = 800 ... ... ... ≈

  18. D Initialize D Sparse Coding Use MP or BP YT Dictionary Update Column-by-Column by SVD computation Future WorkDictionary Learning Use K-SVD to learn a compact dictionary Aharon, Elad, & Bruckstein (‘04) * The slides are from http://www.cs.technion.ac.il/~elad/talks/

  19. Future WorkOnline Update Dictionary ... ≈ ...

  20. Future WorkMesh Partitioning for Local Shape Priors Heart Lung Rib Abdomen Segmentation system Colon

  21. Thanks!Questions and comments

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