A Neural Network Approach to Functional MRI Pattern Analysis — Clustering of Time-Series by Hierarchical Vector Quantization

Wismüller, Axel; Dersch, Dominik R.; Lipinski, Bernadette; Hahn, Klaus; Auer, Dorothee P.

doi:10.1007/978-1-4471-1599-1_133

Cited by 10 publications

(10 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The standard K-means algorithm has also been used with some success [Ding et al, 1994;Toft et al, 1997;Goutte et al, 1999], as have hierarchical approaches to clustering [Goutte et al, 1999;Filzmoser et al, 1999]. Also, using hierarchical arguments together with a nonparametric approach, innovative methods are starting to appear [e.g., Wismü ller et al, 1998;Domany, 1999;Goutte, unpublished research].…”

Section: Feature-space Clusteringmentioning

confidence: 97%

Feature‐space clustering for fMRI meta‐analysis

Goutte¹,

Hansen²,

Liptrot³

et al. 2001

Human Brain Mapping

118

View full text Add to dashboard Cite

Clustering functional magnetic resonance imaging (fMRI) time series has emerged in recent years as a possible alternative to parametric modeling approaches. Most of the work so far has been concerned with clustering raw time series. In this contribution we investigate the applicability of a clustering method applied to features extracted from the data. This approach is extremely versatile and encompasses previously published results [Goutte et al., 1999] as special cases. A typical application is in data reduction: as the increase in temporal resolution of fMRI experiments routinely yields fMRI sequences containing several hundreds of images, it is sometimes necessary to invoke feature extraction to reduce the dimensionality of the data space. A second interesting application is in the meta-analysis of fMRI experiment, where features are obtained from a possibly large number of single-voxel analyses. In particular this allows the checking of the differences and agreements between different methods of analysis. Both approaches are illustrated on a fMRI data set involving visual stimulation, and we show that the feature space clustering approach yields nontrivial results and, in particular, shows interesting differences between individual voxel analysis performed with traditional methods.

show abstract

Section: Feature-space Clusteringmentioning

confidence: 97%

Feature‐space clustering for fMRI meta‐analysis

Goutte¹,

Hansen²,

Liptrot³

et al. 2001

Human Brain Mapping

118

View full text Add to dashboard Cite

show abstract

“…The details of the method were described previously. 10,12,17,20 VQ approaches determine the cluster centers w i by an iterative adaptive update based on the following equation:…”

Section: Dynamic Data Analysis-vqmentioning

confidence: 99%

“…[12][13][14][15][16][17] The present study investigates the application of unsupervised vector quantization (VQ) in dynamic breast MRI. Unsupervised learning is a method of machine learning where a model is fit to observations.…”

mentioning

confidence: 99%

Classification of Small Contrast Enhancing Breast Lesions in Dynamic Magnetic Resonance Imaging Using a Combination of Morphological Criteria and Dynamic Analysis Based on Unsupervised Vector-Quantization

Schlossbauer¹,

Leinsinger²,

Wismüller³

et al. 2008

Investigative Radiology

View full text Add to dashboard Cite

Purpose-To evaluate the diagnostic value of breast magnetic resonance imaging (MRI) in small focal lesions using dynamic analysis based on unsupervised vector quantization in combination with a score for morphologic criteria.Materials and Methods-We examined 85 mammographically indetermintate lesions (BIRADS 3-4; 47 malignant, mean lesion size 1.2 cm; 38 benign, mean lesion size 1.1 cm). MRI was performed with a dynamic T1-weighted gradient echo sequence (1 precontrast and 5 postcontrast series). Lesions with an initial contrast enhancement ≥50% were selected with semiautomatic segmentation. For conventional dynamic analysis, we calculated the mean initial signal increase and postinitial course of all voxels included in a lesion. Secondly, all voxels within the lesions were assigned to 4 clusters using minimal-free-energy vector quantization. Dynamic and morphologic criteria were summarized in a diagnostic score and evaluated by receiver operating characteristic analysis.Results-In the present collection of small lesions, morphologic criteria [area under the curve (AUC) = 0.610] were inferior to dynamic criteria in the detection of breast cancer. Dynamic analysis with vector quantization (AUC = 0.760) presented slightly better results compared with standard dynamic analysis (AUC = 0.693). There was no benefit for combined morphologic and dynamic analysis.Conclusion-In small MR-mammographic lesions, dynamic analysis with vector quantization alone tends to result in a higher diagnostic accuracy compared with combined morphologic and dynamic analysis. Keywords breast cancer; breast MRI; vector quantizationContrast-enhanced MR imaging of the breast was reported to be a highly sensitive method for the detection of invasive breast cancer. 1,2 Different investigators described that certain dynamic signal intensity (SI) characteristics (rapid and intense contrast enhancement followed by a wash out phase) obtained in dynamic studies are a strong indicator for malignancy. 3

show abstract

“…In the clustering process, a time course with n points is represented by one point in an n-dimensional Euclidean space, which is subsequently partitioned into clusters based on the proximity of the input data. These groups or clusters are represented by prototypical time series called codebook vectors (CV) located at the center of the corresponding clusters [6,7,[10][11][12]. VQ approaches determine the cluster centers w i by an iterative adaptive update based on the following equation:…”

Section: Algorithm Of Vector Quantizationmentioning

confidence: 99%

“…Neural networks can be used to analyze complex patterns of SI time series in dynamic MRI [6][7][8][9]. The present study investigated the application of neural networks in dynamic breast MRI.…”

Section: Introductionmentioning

confidence: 99%

Cluster analysis of signal-intensity time course in dynamic breast MRI: does unsupervised vector quantization help to evaluate small mammographic lesions?

Leinsinger¹,

Schlossbauer²,

Scherr³

et al. 2006

Eur Radiol

Self Cite

View full text Add to dashboard Cite

We examined whether neural network clustering could support the characterization of diagnostically challenging breast lesions in dynamic magnetic resonance imaging (MRI). We examined 88 patients with 92 breast lesions (51 malignant, 41 benign). Lesions were detected by mammography and classified Breast Imaging and Reporting Data System (BIRADS) III (median diameter 14 mm). MRI was performed with a dynamic T1-weighted gradient echo sequence (one precontrast and five postcontrast series). Lesions with an initial contrast enhancement >or=50% were selected with semiautomatic segmentation. For conventional analysis, we calculated the mean initial signal increase and postinitial course of all voxels included in a lesion. Secondly, all voxels within the lesions were divided into four clusters using minimal-free-energy vector quantization (VQ). With conventional analysis, maximum accuracy in detecting breast cancer was 71%. With VQ, a maximum accuracy of 75% was observed. The slight improvement using VQ was mainly achieved by an increase of sensitivity, especially in invasive lobular carcinoma and ductal carcinoma in situ (DCIS). For lesion size, a high correlation between different observers was found (R(2) = 0.98). VQ slightly improved the discrimination between malignant and benign indeterminate lesions (BIRADS III) in comparison with a standard evaluation method.

show abstract

A Neural Network Approach to Functional MRI Pattern Analysis — Clustering of Time-Series by Hierarchical Vector Quantization

Cited by 10 publications

References 4 publications

Feature‐space clustering for fMRI meta‐analysis

Feature‐space clustering for fMRI meta‐analysis

Classification of Small Contrast Enhancing Breast Lesions in Dynamic Magnetic Resonance Imaging Using a Combination of Morphological Criteria and Dynamic Analysis Based on Unsupervised Vector-Quantization

Cluster analysis of signal-intensity time course in dynamic breast MRI: does unsupervised vector quantization help to evaluate small mammographic lesions?

Contact Info

Product

Resources

About