Application of Unsupervised bf Clustering Methods to Medical Imaging

Unsupervised clustering for biomedical time-series analysis Cancer detection in Breast fMRI Data Characterization of fMRI data Local deficits of brain perfusion Conclusions

Application of Unsupervised bf Clustering Methods to Medical Imaging A. Meyer-B¨ase1

P. Gruber1,2 F.J. Theis2 H. Ritter3

1 Florida 2 Institute

A. Wism¨ uller1

State University, Tallahassee, USA

of Biophysics, University of Regensburg, Germany 3 University

of Bielefeld, Germany

WSOM 2005, Paris, France

Meyer-B¨ ase, Theis, Gruber, Wism¨ uller, Ritter

Unsupervised Clustering


Outline Unsupervised clustering for biomedical time-series analysis Vector Quantization Based on Neural Networks Self Organizing Maps Neural Gas Network Cancer detection in Breast fMRI Data Segmentation Results Characterization of fMRI data Artifact removal Detection Task-Related Effects Local deficits of brain perfusion Analysis of Dynamic Perfusion MRI Side Asymmetry Conclusions Meyer-B¨ ase, Theis, Gruber, Wism¨ uller, Ritter



Introduction

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Demonstration of unsupervised clustering methods as a tool for biomedical image time-series analysis Applications: functional magnetic resonance imaging (fMRI), breast MRI, and perfusion MRI I

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Breast MRI: new modality for detection and evaluation for indeterminate lesions Characterization of task-related and artefactual effects for fMRI data analysis Segmentation of perfusion MRI data w.r.t. identification of local deficits of brain perfusion




Introduction

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Introduction

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Introduction

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Introduction

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Vector Quantization Based on Neural Networks Self Organizing Maps Neural Gas Network





Methods

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Analysis methods: model-based (a priori information of activity patterns) and model-free (no a priori information)

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Model-free methods: transformation-based (ICA, PCA) and clustering (neural, fuzzy)

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Transformation-based methods: separate functional response from various artifact sources

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Clustering: classification based on temporal similarity (time-courses)





Methods

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Analysis methods: model-based (a priori information of activity patterns) and model-free (no a priori information)

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Model-free methods: transformation-based (ICA, PCA) and clustering (neural, fuzzy)

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Transformation-based methods: separate functional response from various artifact sources

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Clustering: classification based on temporal similarity (time-courses)





Vector quantization I

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Vector quantization: fast clustering technique for feature vectors describing pixel time courses Goal: Determine cluster centers effectively [Bauer et al., 1999] Cluster centers adaptively updated: wi (t + 1) = wi (t) + (t)ai (x(t), C (t), κ)(x(t) − wi (t)) (t): learning parameter, ai : codebook, C (t): dependent cooperativity function, κ: cooperativity parameter, and x: feature vector. Meyer-B¨ ase, Theis, Gruber, Wism¨ uller, Ritter




SOM I I

Prespecified lattice (mostly 2D) Cluster update rule: d

wi (t + 1) = wi (t) + (t) exp − σij2

x(t) − wi (t)

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Kohonen map: mapping between data space V and prespecified graph G is not necessarily topology-preserving

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Topological structure of V not a priori known or too complicated Incompatible structures of preset graph G and V can lead to unbalanced quantization errors

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2D neural lattice

Mapping

n−dimensional data manifold (pixel time courses in fMRI or MRI)




Neural Gas

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Solution to topology constraints: induced Delaunay triangulation Neural gas network: topology preserving map

Graph spanning manifold

Mapping

n−dimensional data manifold (pixel time courses in fMRI or MRI)





Algorithm

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Method: refreshing of connections in graph

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Neural gas algorithm: codewords are refreshed based on neighborhood ranking

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Advantages: lower distortion rate and faster quantization than Kohonen map

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Update for cluster centers: wi (t + 1) = wi (t) + (t) exp (−ki (x, wi /λ))(x(t) − wi (t))




Segmentation Results





Segmentation and Classification

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MRI: noninvasive breast imaging technique Dynamic contrast-enhanced MRI: injection of gadolinium-based contrast agent Goal: lesion detection and analysis, separation of lesion-related enhancement from image noise, and signal contributions from parenchyma and vasculature Basis for classification: time-signal intensity curves (Kuhl’s method) [Lucht et al., 2001] Malignant (type II or III), benign (type I) Meyer-B¨ ase, Theis, Gruber, Wism¨ uller, Ritter

signal intensity [%]

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Ia Ib

II III

early

intermediate and late postcontrast phase


t



Method I (Manual Threshold) I

Three-step procedure: increase of signal [%] high 100 moderate threshold 50%

50 low

native

1.

2.

3.

4.

5.

6.

7.

8.

minute after contrast agent

1. threshold selection of pixels

2. resulting regions

3. lesion determined by region growing





Method I results I

Lesion extent and average time-signal intensity curve 100

slice 14

slice 15

75 50

sai: 80.38 sv : −5.81 p

25

slice 16

slice 17

0 1

sai : precontrast signal intensity I

2

3

4

5

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svp : postcontrast signal intensity

Disadvantage: False negative rate about 0.15, when using threshold give by a radiologist in early stage Meyer-B¨ ase, Theis, Gruber, Wism¨ uller, Ritter




Method II (Automatic Detection)

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Region of interest (ROI): whole breast, classification of pixels by neural network

Cluster assignment map:


1

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3

4

5

6

7

8

9




Method II results I

Codebook vectors: 1

150

2

150 sai: 5.84 sv : −0.47

100

p

50

p

50

0 2

3

4

5

6

4 sai: −6.46 svp: −2.83

50

0

0 1

2

3

4

5

6

7

150

sai: 25.42 svp: 1.58

50

0 2

3

4

5

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6

1

2

3

4

5

6

6

150

sai: 152.17 svp: −6.57

100

0 2

3

4

5

6

8

1

sai: 43.54 svp: 3.49

2

3

4

5

6

9

150

100

0

4

50

1

50

1

3

sai: −0.12 svp: 0.32

150

100

2 5

100

50

p

0 1

150

100

sai: 15.38 sv : 0.36

100

50

0 1

150

3

150 sai: 10.53 sv : −1.12

100

sai: 85.94 svp: −3.01

100

50

0 1


2

3

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5

6

1

2

3

4

5

6




Method III (Refined Segmentation) I I

Combines method 1 and 2: threshold + classification Example of benign lesion (fibroadenoma) based on method 3 300 250

slice 22

250 200

150

150

100

slice 21

300

Cluster 1

200

50

sai: 208.72 svp: 13.93

Cluster 2

100

sai: 147.46 svp: 13.55

50

0

0 1 2 3 4 5 6

300 250

1 2 3 4 5 6 300

Cluster 3

250

200 100

sai: 96.49 svp: 5.68

150 100

50

slice 23

Cluster 4

200 sai: 52.79 svp: 4.88

150

50

0

0 1 2 3 4 5 6

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1 2 3 4 5 6

Example of benign lesion (fibroadenoma) based on method 1 slice 21

slice 22

125 100 75 slice 23

50

sai: 100.04 sv : 9.03 p

25 0 1


2

3

4

5

6




Method III results I

Example of malignant lesion (ductal carcinoma in situ) based on method 3 300 250

300

Cluster 1

250

200 100

slice 6

slice 7

50

Cluster 2

200

150

sai: 100.09 svp: 9.59

150 sai: 217.15 sv : −10.09

100 50

p

0

0 1 2 3 4 5 6

300 250

1 2 3 4 5 6 300

Cluster 3

250

200 sai: 58.32 svp: 36.35

150 100

150 100

50

slice 8

Cluster 4

200 sai: 154.11 svp: 12.97

50

0

0 1 2 3 4 5 6

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1 2 3 4 5 6

Example of malignant lesion (ductal carcinoma in situ) based on method 1 slice 6

slice 7

200

150

100 slice 8

sa : 124.50 i svp: 2.77

50

0 1


2

3

4

5

6



Artifact removal Detection Task-Related Effects





fMRI Data I I I

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Idea of exploratory data analysis for biomedical image time-series analysis demonstrated as an example on fMRI fMRI: advance in MRI, noninvasive [Ogawa et al., 1993] Basis: BOLD (blood oxygenation level-dependent) contrast, deoxyhemoglobin acts as an endogenous paramagnetic agent Neuronal activity leads to increase in blood flow and decrease in deoxyhemoglobin Correlation between neural activities and MR signal changes Meyer-B¨ ase, Theis, Gruber, Wism¨ uller, Ritter




Analysis of fMRI Data

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BOLD signal complexity and task reference function: Exp (experimental) and Con (control)

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Goal: Analysis methods to find response waveform and associated activated regions

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Analysis using SOMs e.g. by [Chuang et al., 1999]





Automatic clustering

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Many applications of unsupervised clustering [Wism¨ uller et al., 2002] I I I

ROI selection Artifact removal Detection of Task-Related Effects

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Similar to other explorative techniques like ICA

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Yield good results also in preprocessing steps





Results I

Co-activation of the frontal eye fields:

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Region of inner ventricles:





Correlation Coefficients I

Visualization of correlation coefficient and quantization error for 8, 16, and 36 codebook vectors 4

Correlation

Quant. Error

x 10 NG

0.8

1.9

0.7

1.8

0.6

1.7

0.5 SOM

NG

FC

SOM

NG

FC

SOM

NG

FC

SOM

NG Algorithms

FC

4

x 10 1.8

0.84

1.7

0.82

1.6 0.8 1.5 0.78 SOM

NG

FC 4

x 10

0.86 0.85

1.6

0.84

1.5

0.83

1.4

0.82

1.3 SOM

NG Algorithms

FC





Cluster assignment maps I

Cluster assignment maps for neural gas network for 36 codebook vectors





ICs I

Associated ICs: 1

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36




Analysis of Dynamic Perfusion MRI Side Asymmetry





Dynamic Perfusion MRI I

Goal: computation of conventional perfusion parameter maps

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Results: identification of local deficits of brain perfusion, vessel sizes, side asymmetries

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Patient with stroke in the right basal ganglia:


Diffusion weighted

MTT map

rCBV map

rCBF map




Cluster assignment maps I

Cluster assignment maps of a neural gas clustering:





Codebook vectors I

Codebook vectors:





Side Asymmetry I

Analysis w.r.t. side asymmetry to quantify defect

Cluster #15: infarct region

Contiguous and contralateral ROI

Average CTC of affected ROI

Average CTC of nonaffected ROI




Conclusions

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Results for Exploratory data analysis of biomedical image time-series analysis

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Examination of breast lesions based on dynamic contrast-enhanced MRI

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Characterization of task-related and artefactual effects for fMRI data analysis

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Analysis of dynamic perfusion MRI

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All applications: fast and accurate segmentation techniques

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Breast and perfusion MRI: some expert intervention possible




Conclusions

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Conclusions

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Appendix References

Bibliography

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References H. Bauer, M. Hermann, and T. Villmann. Neural maps and topographic vector quantization. Neural Networks, 12:659–676, 1999. K. Chuang, M. Chiu, C. Lin, and J. Chen. Model-free functional MRI analysis using kohonen clustering neural network and fuzzy c-means. IEEE Transactions on Medical Imaging, 18:1117–1128, 1999. E. Lucht, M. Knopp, and G. Brix. Classification of signal-time curves from dynamic (MR) mammography by neural networks. Magnetic Resonance Imaging, 19:51–57, 2001. S. Ogawa, T. Lee, and B. Barrere. The sensitivity of magnetic resonace image signals of a rat brain to changes in the cerebral venous blood oxygenation activation. Magentic Resonance Medical, 29: 205–210, 1993. A. Wism¨ uller, O. Lange, D. Dersch, G. Leisinger, K. Hahn, B. P¨ utz, and D. Auer. Cluster analysis of biomedical image time-series. International Journal on Computer Vision, 18:102–128, 2002.



Application of Unsupervised bf Clustering Methods to Medical Imaging

Application of Unsupervised bf Clustering Methods to Medical Imaging

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