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  • 1
    Keywords: TOOL ; RECOGNITION ; methods ; PATTERN-RECOGNITION
    Abstract: In den letzten Jahren hat sich der Workshop "Bildverarbeitung für die Medizin" durch erfolgreiche Veranstaltungen etabliert.Ziel ist auch 2006 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespräche zwischen Wissenschaftlern, Industrie und Anwendern.Die Beiträge dieses Bandes - einige in englischer Sprache - behandeln alle Bereiche der medizinischen Bildverarbeitung, insbesondere Algorithmen, Soft- und Hardwaresysteme sowie deren klinische Anwendungen.
    Type of Publication: Book chapter
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  • 2
    Keywords: CANCER ; carcinoma ; PROSTATE ; QUANTIFICATION ; DISEASE ; TISSUE ; QUALITY ; MRI ; SPECTROSCOPY ; prostate cancer ; LOCALIZATION ; PATTERN ; pattern recognition ; postprocessing ; proton MR spectroscopic imaging
    Abstract: RATIONALE AND OBJECTIVES: The aim of this study was to assess (1) automated analysis methods versus manual evaluation by human experts of three-dimensional proton magnetic resonance spectroscopic imaging (MRSI) data from patients with prostate cancer and (2) the contribution of spatial information to decision making. MATERIALS AND METHODS: Three-dimensional proton MRSI was applied at 1.5 T. MRSI data from 10 patients with histologically proven prostate adenocarcinoma, scheduled either for prostatectomy or intensity-modulated radiation therapy, were evaluated. First, two readers manually labeled spectra using spatial information to identify the localization of spectra and neighborhood information, establishing the reference set of this study. Then, spectra were labeled again manually in a blinded and randomized manner and evaluated automatically using software that applied spectral line fitting as well as pattern recognition routines. Statistical analysis of the results of the different approaches was performed. RESULTS: Altogether, 1018 spectra were evaluable by all methods. Numbers of evaluable spectra differed significantly depending on patient and evaluation method. Compared to automated analysis, the readers made rather binary decisions, using information from neighboring spectra in ambiguous cases, when evaluating MRSI data as a whole. Differences between anatomically blinded and unblinded evaluation were larger than differences between evaluations using blinded data and automated techniques. CONCLUSIONS: An automated approach, which evaluates each spectrum individually, can be as good as an anatomy-blinded human reader. Spatial information is routinely used by human experts to support their final decisions. Automated procedures that consider anatomic information for spectral evaluation will enhance the diagnostic impact of MRSI of the human prostate.
    Type of Publication: Journal article published
    PubMed ID: 22578226
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  • 3
    Keywords: SPECTRA ; CANCER ; tumor ; COMBINATION ; Germany ; CLASSIFICATION ; imaging ; QUANTIFICATION ; TISSUE ; PATTERNS ; COMPONENT ; BRAIN-TUMORS ; pattern recognition ; methods ; MRSI ; SIGNALS ; GENERALIZED LINEAR-REGRESSION ; magnetic resonance spectroscopic imaging ; SHORT ECHO TIME
    Abstract: Despite its diagnostic value and technological availability, H-1 NMR spectroscopic imaging (MRSI) has not found its way into clinical routine yet. Prerequisite for the clinical application is an automated and reliable method for the diagnostic evaluation of MRS images. In the present paper, different approaches to the estimation of tumor probability from MRSI in the prostate are assessed. Two approaches to feature extraction are compared: quantification (VARPRO, AMARES, QUEST) and sub-space methods on spectral patterns (principal components, independent components, nonnegative matrix factorization, partial least squares). Linear as well as nonlinear classifiers (support vector machines, Gaussian processes, random forests) are applied and discussed. Quantification-based approaches are much more sensitive to the choice and parameterization of the quantification algorithm than to the choice of the classifier. Furthermore, linear methods based on magnitude spectra easily achieve equal performance and also allow for biochemical interpretation in combination with subspace methods. Nonlinear methods operating directly on magnitude spectra achieve the best results but are less transparent than the linear methods
    Type of Publication: Journal article published
    PubMed ID: 17191229
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  • 4
    Keywords: brain ; CELLS ; GROWTH ; IN-VITRO ; proliferation ; tumor ; TUMOR-CELLS ; MODEL ; MODELS ; INFORMATION ; segmentation ; TISSUE ; TUMORS ; DYNAMICS ; SIMULATION ; FIBER ; EVOLUTION ; WHITE-MATTER ; PROJECT ; GLIOMAS ; BRAIN-TUMORS ; GLIOMA ; CELLULAR-AUTOMATON ; MR-IMAGES ; Tissue type ; GLIOMA GROWTH ; HAMILTON-JACOBI EQUATIONS
    Abstract: Reaction-diffusion based tumor growth models have been widely used in the literature for modeling the growth of brain gliomas. Lately, recent models have started integrating medical images in their formulation. Including different tissue types, geometry of the brain and the directions of white matter fiber tracts improved the spatial accuracy of reaction-diffusion models. The adaptation of the general model to the specific patient cases on the other hand has not been studied thoroughly yet. In this paper, we address this adaptation. We propose a parameter estimation method for reaction-diffusion tumor growth models using time series of medical images. This method estimates the patient specific parameters of the model using the images of the patient taken at successive time instances. The proposed method formulates the evolution of the tumor delineation visible in the images based on the reaction-diffusion dynamics; therefore, it remains consistent with the information available. We perform thorough analysis of the method using synthetic tumors and show important couplings between parameters of the reaction-diffusion model. We show that several parameters can be uniquely identified in the case of fixing one parameter, namely the proliferation rate of tumor cells. Moreover, regardless of the value the proliferation rate is fixed to, the speed of growth of the tumor can be estimated in terms of the model parameters with accuracy. We also show that using the model-based speed, we can simulate the evolution of the tumor for the specific patient case. Finally, we apply our method to two real cases and show promising preliminary results
    Type of Publication: Journal article published
    PubMed ID: 19605320
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  • 5
    Keywords: brain ; SPECTRA ; tumor ; Germany ; IN-VIVO ; VIVO ; ALGORITHM ; CLASSIFICATION ; FOLLOW-UP ; INFORMATION ; QUANTIFICATION ; TUMORS ; ACCURACY ; TIME ; PATIENT ; SIGNAL ; MAGNETIC-RESONANCE ; LESIONS ; VECTOR ; NUMBER ; LINE ; PCR ; TRANSFORMATION ; HIGH-LEVEL ; support vector machines ; REGRESSION ; BRAIN-TUMORS ; PATTERN-RECOGNITION ; GRADE ; EXTRACTION ; brain tumors ; CHANNELS ; in vivo ; SIGNALS ; clinical studies ; H1 ; magnetic resonance spectroscopy ; nonlinear ; preprocessing ; postprocessing ; NMR-SPECTRA ; benchmark ; chemometrics ; COMPONENT ANALYSIS ; DOMAIN METHODS ; GLIOMATOSIS CEREBRI ; human brain tumor ; MR SPECTROSCOPY QUANTITATION ; statistical learning
    Abstract: We describe the optimal high-level postprocessing of single-voxel H-1 magnetic resonance spectra and assess the benefits and limitations of automated methods as diagnostic aids in the detection of recurrent brain tumor. In a previous clinical study, 90 long-echo-time single-voxel spectra were obtained from 52 patients and classified during follow-up (30/28/ 32 normal/non-progressive tumor/tumor). Based on these data, a large number of evaluation strategies, including both standard resonance line quantification and algorithms from pattern recognition and machine learning, were compared in a quantitative evaluation. Results from linear and non-linear feature extraction, including ICA, PCA and wavelet transformations, and'also the data from resonance line quantification were combined systematically with different classifiers such as LDA, chemometric methods (PLS, PCR), support vector machines and ensemble methods. Classification accuracy was assessed using a leave-one-out cross-validation scheme and the area under the curve (AUC) of the receiver operator characteristic (ROC). A regularized linear regression on spectra with binned channels reached 91% classification accuracy compared with 83% from quantification. Interpreting the loadings of these regressions, we find that lipid and lactate signals are too unreliable to be used in a simple machine rule. Choline and NAA are the main source of relevant information. Overall, we find that fully automated pattern recognition algorithms perform as well as, or slightly better than, a manually controlled and optimized resonance line quantification. Copyright (C) 2006 John Wiley & Sons, Ltd
    Type of Publication: Journal article published
    PubMed ID: 16642460
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  • 6
    Keywords: FIELD, FIELDS, IMAGES, MAGNETIC-RESONANCE, tumor
    Abstract: Magnetic resonance spectral images provide information on metabolic processes and can thus be used for in vivo tumor diagnosis. However, each single spectrum has to be checked manually for tumorous changes by an expert, which is only possible for very few spectra in clinical routine. We propose a semi-supervised procedure which requires only very few labeled spectra as input and can hence adapt to patient and acquisition specific variations. The method employs a discriminative random field with highly flexible single-side and parameter-free pair potentials to model spatial correlation of spectra. Classification is performed according to the label set that minimizes the energy of this random field. An iterative procedure alternates a parameter update of the random field using a kernel density estimation with a classification by means of the GraphCut algorithm. The method is compared to a single spectrum approach on simulated and clinical data.
    Type of Publication: Journal article published
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  • 7
    Keywords: brain ; SPECTRA ; tumor ; evaluation ; Germany ; human ; CLASSIFICATION ; DIAGNOSIS ; imaging ; INFORMATION ; QUANTIFICATION ; NUCLEAR-MEDICINE ; TIME ; QUALITY ; MR ; QUANTITATION ; MAGNETIC-RESONANCE-SPECTROSCOPY ; RECOGNITION ; score ; RELIABILITY ; nuclear medicine ; radiology ; PATTERN ; BRAIN-TUMORS ; INCREASE ; analysis ; NUCLEAR ; USA ; MEDICINE ; quantitative ; ERRORS ; magnetic resonance spectroscopic imaging ; SHORT ECHO TIME ; interactive ; DIAGNOSTIC EVALUATION ; artifact recognition ; automated diagnostic systems ; CONFIDENCE IMAGES ; CRAMER-RAO BOUNDS ; expert systems ; quality classification ; SPECTRAL-ANALYSIS
    Abstract: Besides the diagnostic evaluation of a spectrum, the assessment of its quality and a check for plausibility of its information remains a highly interactive and thus time-consuming process in MR spectroscopic imaging (MRSI) data analysis. In the automation of this quality control, a score is proposed that is obtained by training a machine learning classifier on a representative set of spectra that have previously been classified by experts into evaluable data and nonevaluable data. In the first quantitative evaluation of different quality measures on a test set of 45,312 long echo time spectra in the diagnosis of brain tumor, the proposed pattern recognition (using the random forest classifier) separated high- and low-quality spectra comparable to the human operator (area-under-the-curve of the receiver-operator-characteristic, AUC 〉 0.993), and performed better than decision rules based on the signal-to-noise-ratio (AUC 〈 0.934) or the estimated Cramer-Rao-bound on the errors of a spectral fitting (AUC 〈 0.952). This probabilistic assessment of the data quality provides comprehensible confidence images and allows filtering the input of any subsequent data processing, i.e., quantitation or pattern recognition, in an automated fashion. It thus can increase robustness and reliability of the final diagnostic evaluation and allows for the automation of a tedious part of MRSI data analysis
    Type of Publication: Journal article published
    PubMed ID: 18421692
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  • 8
    Keywords: Germany ; MODEL ; ALGORITHM ; CLASSIFICATION ; REDUCTION ; BIOLOGY ; bioinformatics ; PREDICTION ; SELECTION ; ELIMINATION ; FEATURES ; REGRESSION ; SUBSET ; INFRARED-SPECTROSCOPY ; TESTS ; RELEVANCE ; COEFFICIENTS ; biotechnology ; CLASSIFIERS ; WELL ; NOISE ; CONTINUUM REGRESSION ; MULTIVARIATE CALIBRATION ; PARTIAL LEAST-SQUARES ; PLS ; VARIABLE IMPORTANCE MEASURES
    Abstract: Background: Regularized regression methods such as principal component or partial least squares regression perform well in learning tasks on high dimensional spectral data, but cannot explicitly eliminate irrelevant features. The random forest classifier with its associated Gini feature importance, on the other hand, allows for an explicit feature elimination, but may not be optimally adapted to spectral data due to the topology of its constituent classification trees which are based on orthogonal splits in feature space. Results: We propose to combine the best of both approaches, and evaluated the joint use of a feature selection based on a recursive feature elimination using the Gini importance of random forests' together with regularized classification methods on spectral data sets from medical diagnostics, chemotaxonomy, biomedical analytics, food science, and synthetically modified spectral data. Here, a feature selection using the Gini feature importance with a regularized classification by discriminant partial least squares regression performed as well as or better than a filtering according to different univariate statistical tests, or using regression coefficients in a backward feature elimination. It outperformed the direct application of the random forest classifier, or the direct application of the regularized classifiers on the full set of features. Conclusion: The Gini importance of the random forest provided superior means for measuring feature relevance on spectral data, but - on an optimal subset of features - the regularized classifiers might be preferable over the random forest classifier, in spite of their limitation to model linear dependencies only. A feature selection based on Gini importance, however, may precede a regularized linear classification to identify this optimal subset of features, and to earn a double benefit of both dimensionality reduction and the elimination of noise from the classification task
    Type of Publication: Journal article published
    PubMed ID: 19591666
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  • 9
    Keywords: CANCER ; Germany ; IN-VIVO ; MODEL ; MODELS ; PROSTATE ; VIVO ; ALGORITHM ; COMMON ; IMAGES ; imaging ; VISUALIZATION ; SITES ; TUMORS ; RESOLUTION ; TIME ; PATIENT ; REDUCTION ; CONTRAST ; CONTRAST AGENT ; MRI ; FIELD ; MAGNETIC-RESONANCE ; PARAMETERS ; pharmacokinetic ; TRACER ; MAPS ; AGENT ; radiology ; SINGLE ; MS ; structure ; bias ; TECHNOLOGY ; USA ; PHARMACOKINETIC PARAMETERS ; ERROR ; ROOT ; DCE-MRI ; WELL ; Application ; Dynamic contrast-enhanced imaging ; TIMES ; Block iterated conditional modes ; kinetic parameter maps ; Markov random field ; nonlinear least squares ; NONLINEAR-REGRESSION
    Abstract: Dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging can be used to study microvascular structure in vivo by monitoring the abundance of an injected diffusible contrast agent over time. The resulting spatially resolved intensity-time curves are usually interpreted in terms of kinetic parameters obtained by fitting a pharmacokinetic model to the observed data. Least squares estimates of the highly nonlinear model parameters, however, can exhibit high variance and can be severely biased. As a remedy, we bring to bear spatial prior knowledge by means of a generalized Gaussian Markov random field (GGMRF). By using information from neighboring voxels and computing the maximum a posteriori solution for entire parameter maps at once, both bias and variance of the parameter estimates can be reduced thus leading to smaller root mean square error (RMSE). Since the number of variables gets very big for common image resolutions, sparse solvers have to be employed. To this end, we propose a generalized iterated conditional modes (ICM) algorithm operating on blocks instead of sites which is shown to converge considerably faster than the conventional ICM algorithm. Results on simulated DCE-MR images show a clear reduction of RMSE and variance as well as, in some cases, reduced estimation bias. The mean residual bias (MRB) is reduced on the simulated data as well as for all 37 patients of a prostate DCE-MRI dataset. Using the proposed algorithm, average computation times only increase by a factor of 1.18 (871 ms per voxel) for a Gaussian prior and 1.51 (1.12 s per voxel) for an edge-preserving prior compared to the single voxel approach (740 ms per voxel)
    Type of Publication: Journal article published
    PubMed ID: 19369150
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  • 10
    Keywords: Germany ; MODELS ; INFORMATION ; QUANTIFICATION ; QUALITY ; QUANTITATION ; MAGNETIC-RESONANCE ; SPECTROSCOPY ; LOCALIZATION ; RECONSTRUCTION ; BRAIN-TUMORS ; MRSI ; spatial prior knowledge ; spectral fitting
    Abstract: We propose a Bayesian smoothness prior in the spectral fitting of MRS images which can be used in addition to commonly employed prior knowledge. By combining a frequency-domain model for the free induction decay with a Gaussian Markov random field prior, a new optimization objective is derived that encourages smooth parameter maps. Using a particular parameterization of the prior, smooth damping, frequency and phase maps can be obtained whilst preserving sharp spatial features in the amplitude map. A Monte Carlo study based on two sets of simulated data demonstrates that the variance of the estimated parameter maps can be reduced considerably, even below the Cramer-Rao lower bound, when using spatial prior knowledge. Long-TE (1) H MRSI at 1.5 T of a patient with a brain tumor shows that the use of the spatial prior resolves the overlapping peaks of choline and creatine when a single voxel method fails to do so. Improved and detailed metabolic maps can be derived from high-spatial-resolution, short-TE (1) H MRSI at 3 T. Finally, the evaluation of four series of long-TE brain MRSI data with various signal-to-noise ratios shows the general benefit of the proposed approach.
    Type of Publication: Journal article published
    PubMed ID: 21538636
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