A Framework for the Generation of Realistic Brain Tumor Phantoms and Applications

Rexilius, Jan; Hahn, Horst K.; Schlüter, Mathias; Kohle, S.; Bourquain, Holger; Böttcher, Joachim; Peitgen, Heinz‐Otto

doi:10.1007/978-3-540-30136-3_31

Cited by 10 publications

(10 citation statements)

References 15 publications

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“…In [24], [25] the authors applied tumor growth models to register anatomical atlases of healthy subjects onto patient images bearing tumors. In some other works [26], [27] growth models have been used to create synthetic images bearing tumors which are then used to evaluate segmentation algorithms.…”

Section: A Previous Work On Reaction-diffusion Type Modelsmentioning

confidence: 99%

Image Guided Personalization of Reaction-Diffusion Type Tumor Growth Models Using Modified Anisotropic Eikonal Equations

Konukoğlu

Clatz

Menze

et al. 2010

IEEE Trans. Med. Imaging

165

221

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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 work 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 2 real cases and show promising preliminary results.

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Section: A Previous Work On Reaction-diffusion Type Modelsmentioning

confidence: 99%

Image Guided Personalization of Reaction-Diffusion Type Tumor Growth Models Using Modified Anisotropic Eikonal Equations

Konukoğlu

Clatz

Menze

et al. 2010

IEEE Trans. Med. Imaging

165

221

View full text Add to dashboard Cite

show abstract

“…There are also more complex approaches for generating DTI data, e.g., the generation of synthetic DTI data of a human brain with a tumor [94,96]. In these cases, the growth of tumors is simulated including biochemical processes, i.e., the approaches use rather complex simulation concepts.…”

Section: Tensor Field Visualizationmentioning

confidence: 99%

Generative Data Models for Validation and Evaluation of Visualization Techniques

Schulz

Nocaj

El‐Assady

et al. 2016

Proceedings of the Sixth Workshop on Beyond Time and Errors on Novel Evaluation Methods for Visualization

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We argue that there is a need for substantially more research on the use of generative data models in the validation and evaluation of visualization techniques. For example, user studies will require the display of representative and unconfounded visual stimuli, while algorithms will need functional coverage and assessable benchmarks. However, data is often collected in a semi-automatic fashion or entirely hand-picked, which obscures the view of generality, impairs availability, and potentially violates privacy. There are some sub-domains of visualization that use synthetic data in the sense of generative data models, whereas others work with real-world-based data sets and simulations. Depending on the visualization domain, many generative data models are "side projects" as part of an ad-hoc validation of a techniques paper and thus neither reusable nor general-purpose. We review existing work on popular data collections and generative data models in visualization to discuss the opportunities and consequences for technique validation, evaluation, and experiment design. We distill handling and future directions, and discuss how we can engineer generative data models and how visualization research could benefit from more and better use of generative data models.

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“…Rexilius et al proposed one of the first models for this problem in [21]. They have modeled the tumor with three compartments: the active tumor tissue, the necrotic (dead) tumor core and the edema.…”

Section: Segmentationmentioning

confidence: 99%

Tumor Growth Modeling in Oncological Image Analysis

Konukoğlu

Pennec

Clatz

et al. 2009

Handbook of Medical Image Processing and Analysis

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A Framework for the Generation of Realistic Brain Tumor Phantoms and Applications

Cited by 10 publications

References 15 publications

Image Guided Personalization of Reaction-Diffusion Type Tumor Growth Models Using Modified Anisotropic Eikonal Equations

Image Guided Personalization of Reaction-Diffusion Type Tumor Growth Models Using Modified Anisotropic Eikonal Equations

Generative Data Models for Validation and Evaluation of Visualization Techniques

Tumor Growth Modeling in Oncological Image Analysis

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