Hélène Urien

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Combining anatomical and functional information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scans brings great opportunities to improve diagnosis in oncology and treatment planning in radiation oncology. In this work, we propose a PET-guided MR tumor segmentation method minimizing a globally convex energy in a multiphase(More)
In this work, we propose a new criterion based on spatial context to select relevant nodes in a max-tree representation of an image, dedicated to the detection of 3D brain tumors for F -FDG PET images. This criterion prevents the detected lesions from merging with surrounding physiological radiotracer uptake. A complete detection method based on this(More)
In this paper, we propose a novel 3D method for multiple sclerosis segmentation on FLAIR Magnetic Resonance images (MRI), based on a lesion context-based criterion performed on a max-tree representation. The detection criterion is refined using prior information from other available MRI acquisitions (T2, T1, T1 enhanced with Gadolinium and DP). The method(More)
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