Efficient sparse shape composition with its applications in biomedical image analysis an overview
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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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Efficient sparse shape composition with its applications in biomedical image analysis an overview

Efficient Sparse Shape Composition with its Applications in Biomedical Image Analysis: An Overview

Shaoting Zhang, Yiqiang Zhan, Yan Zhou, and

Dimitris N. Metaxas


Outline
Outline Biomedical Image Analysis:

  • Introduction

  • Our methods

  • Experiments

  • Future work


Introduction motivations
Introduction Biomedical Image Analysis: Motivations

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.


Introduction motivations1
Introduction Biomedical Image Analysis: Motivations

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.


Introduction challenges shape prior modeling
Introduction Biomedical Image Analysis: Challenges – shape prior modeling

  • Good shape prior method needs to:

    • Handle gross errors of the input data.

    • Model complex shape variations.

    • Preserve local shape details.


Introduction relevant work shape prior methods
Introduction Biomedical Image Analysis: Relevant 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]


Our approach shape prior using sparse shape composition
Our Approach Biomedical Image Analysis: Shape 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.


Our approach shape prior using sparse shape composition1
Our Approach Biomedical Image Analysis: Shape 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:


Our approach shape prior using sparse shape composition2
Our Approach Biomedical Image Analysis: Shape prior using sparse shape composition

  • Non-Gaussian errors:


Our approach shape prior using sparse shape composition3
Our Approach Biomedical Image Analysis: Shape 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.


Experiments 2d lung localization in x ray
Experiments Biomedical Image Analysis: 2D 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.


Experiments 2d lung localization in x ray1
Experiments Biomedical Image Analysis: 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


Experiments 2d lung localization in x ray2
Experiments Biomedical Image Analysis: 2D lung localization in X-ray

  • Multimodal distribution

Detection PA ASM/RASM NN TPS Sparse1 Sparse2


Experiments 2d lung localization in x ray3
Experiments Biomedical Image Analysis: 2D lung localization in X-ray

  • Recover local detail information

Detection PA ASM/RASM NN TPS Sparse1 Sparse2


Experiments 2d lung localization in x ray4
Experiments Biomedical Image Analysis: 2D lung localization in X-ray

  • Sparse shape components

  • ASM modes:

+

+

0.5760

0.2156

0.0982


Experiments 2d lung localization in x ray5
Experiments Biomedical Image Analysis: 2D 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


Future work dictionary learning
Future Work Biomedical Image Analysis: Dictionary Learning

Dictionary size = 128

#Coefficients = 800

#Input samples = 800

...

...

...


Future work dictionary learning1

D Biomedical Image Analysis:

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/


Future work online update dictionary
Future Work Biomedical Image Analysis: Online Update Dictionary

...

...


Future work mesh partitioning for local shape priors
Future Work Biomedical Image Analysis: Mesh Partitioning for Local Shape Priors

Heart

Lung

Rib

Abdomen

Segmentation system

Colon


Thanks questions and comments
Thanks! Biomedical Image Analysis: Questions and comments


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