Segmentation of Dynamic PET Images with Kinetic Spectral Clustering

International audienceSegmentation is often required for the analysis of dynamic positron emission tomography (PET) images. However, noise and low spatial resolution make it a difficult task and several supervised and unsupervised methods have been proposed in the literature to perform the segmentation based on semi-automatic clustering of the time activity curves of voxels. In this paper we propose a new method based on spectral clustering that does not require any prior information on the shape of clusters in the space in which they are identified. In our approach, the p-dimensional data, where p is the number of time frames, is first mapped into a high dimensional space and then clustering is performed in a low-dimensional space of the Laplacian matrix. An estimation of the bounds for the scale parameter involved in the spectral clustering is derived. The method is assessed using dynamic brain PET images simulated with GATE and results on real images are presented. We demonstrate the usefulness of the method and its superior performance over three other clustering methods from the literature. The proposed approach appears as a promising pre-processing tool before parametric map calculation or ROI-based quantification tasks

3D+t segmentation of PET images using spectral clustering

International audienceSegmentation of dynamic PET images is often needed to extract the time activity curve (TAC) of regions. While clustering methods have been proposed to segment the PET sequence, they are generally either sensitive to initial conditions or favor convex shaped clusters. Recently, we have proposed a deterministic and automatic spectral clustering method (AD-KSC) of PET images. It has the advantage of handling clusters with arbitrary shape in the space in which they are identified. While improved results were obtained with AD-KSC compared to other methods, its use for clinical applications is constrained to 2D+t PET data due to its computational complexity. In this paper, we propose an extension of AD-KSC to make it applicable to 3D+t PET data. First, a preprocessing step based on a recursive principle component analysis and a Global K-means approach is used to generate many small seed clusters. AD-KSC is then applied on the generated clusters to obtain the final partition of the data. We validated the method with GATE Monte Carlo simulations of Zubal head phantom. The proposed approach improved the region of interest (ROI) definition and outperformed the K-means algorithm

Automatic and deterministic spectral clustering for segmentation of dynamic PET images

La quantification d’images TEP dynamiques est un outil performant pour l’étude in vivo de la fonctionnalité des tissus. Cependant, cette quantification nécessite une définition des régions d’intérêts pour l’extraction des courbes temps-activité. Ces régions sont généralement identifiées manuellement par un opérateur expert, ce qui renforce leur subjectivité. En conséquent, un intérêt croissant a été porté sur le développement de méthodes de classification. Ces méthodes visent à séparer l’image TEP en des régions fonctionnelles en se basant sur les profils temporels des voxels. Dans cette thèse, une méthode de classification spectrale des profils temporels des voxels est développée. Elle est caractérisée par son pouvoir de séparer des classes non linéaires. La méthode est ensuite étendue afin de la rendre utilisable en routine clinique. Premièrement une procédure de recherche globale est utilisée pour localiser d’une façon déterministe les centres optimaux des données projetées. Deuxièmement, un critère non supervisé de qualité de segmentation est proposé puis optimisé par le recuit simulé pour estimer automatiquement le paramètre d’échelle et les poids temporels associés à la méthode. La méthode de classification spectrale automatique et déterministe proposée est validée sur des images simulées et réelles et comparée à deux autres méthodes de segmentation de la littérature. Elle a présenté une amélioration de la définition des régions et elle paraît un outil prometteur pouvant être appliqué avant toute tâche de quantification ou d’estimation de la fonction d’entrée artérielle.Quantification of dynamic PET images is a powerful tool for the in vivo study of the functionality of tissues. However, this quantification requires the definition of regions of interest for extracting the time activity curves. These regions are usually identified manually by an expert operator, which reinforces their subjectivity. As a result, there is a growing interest in the development of clustering methods that aim to separate the dynamic PET sequence into functional regions based on the temporal profiles of voxels. In this thesis, a spectral clustering method of the temporal profiles of voxels that has the advantage of handling nonlinear clusters is developed. The method is extended to make it more suited for clinical applications. First, a global search procedure is used to locate in a deterministic way the optimal cluster centroids from the projected data. Second an unsupervised clustering criterion is proposed and optimised by the simulated annealing to automatically estimate the scale parameter and the weighting factors involved in the method. The proposed automatic and deterministic spectral clustering method is validated on simulated and real images and compared to two other segmentation methods from the literature. It improves the ROI definition, and appears as a promising pre-processing tool before ROI-based quantification and input function estimation tasks

Dynamic PET Image Segmentation Using Spectral Clustering Method (LAAS International Science Conference 2014)

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Unsupervised spectral clustering for segmentation of dynamic PET images

International audienceSegmentation of dynamic PET images is needed to extract the time activity curves (TAC) of regions of interest (ROI). These TAC can be used in compartmental models for in vivo quantification of the radiotracer target. While unsupervised clustering methods have been proposed to segment PET sequences, they are often sensitive to initial conditions or favour convex shaped clusters. Kinetic spectral clustering (KSC) of dynamic PET images was recently proposed to handle arbitrary shaped clusters in the space in which they are identified. While improved results were obtained with KSC compared to three state of art methods, its use for clinical applications is still hindered by the manual setting of several parameters. In this paper, we develop an extension of KSC to automatically estimate the parameters involved in the method and to make it deterministic. First, a global search procedure is used to locate the optimal cluster centroids from the projected data. Then an unsupervised clustering criterion is tailored and used in a global optimization scheme to automatically estimate the scale parameter and the weighting factors involved in the proposed Automatic and Deterministic Kinetic Spectral Clustering (AD-KSC). We validate the method using GATE Monte Carlo simulations of dynamic numerical phantoms and present results on real dynamic images. The deterministic results obtained with AD-KSC agree well with those obtained with optimal manual parameterization of KSC, and improve the ROI identification compared to three other clustering methods. The proposed approach could have significant impact for quantification of dynamic PET images in molecular imaging studies

Optimized spectral clustering for segmentation of dynamic PET images

he quantification of dynamic PET images requires the definition of regions of interest. The manual delineation is a time consuming and unreproducible process due to the poor resolution of PET images. Approaches were proposed in the literature to classify the kinetic profiles of voxels, however, they are generally either sensitive to initial conditions or favor convex shaped clusters. Recently we have proposed a kinetic spectral clustering (KSC) method for segmentation of dynamic PET images that has the advantage of handling clusters with arbitrary shape in the space in which they are identified. However, its use for clinical applications is still hindered by the manual setting of several parameters. In this paper, we propose an extension of KSC to make it automatic (ASC). A new unsupervised clustering criterion is tailored and a global optimization by a probabilistic metaheuristic algorithm is used to select the scale parameter and the weighting factors involved in the method. We validate our approach with GATE Monte Carlo simulations. Results obtained with ASC compare closely with those obtained with optimal manual parameterization of KSC, and outperform those obtained with two other approaches from the literature