International audience— The analysis of images acquired with Positron Emission Tomography (PET) is challenging. In particular, there is no consensus on the best criterion to quantify the metabolic activity for lesion detection and segmentation purposes. Based on this consideration, we propose a versatile knowledge-based segmen-tation methodology for 3D PET imaging. In contrast to previous methods, an arbitrary number of quantitative criteria can be involved and the experts behaviour learned and reproduced in order to guide the segmentation process. The classification part of the scheme relies on example-based learning strategies, allowing interactive example definition and more generally incremental refinement. The image processing part relies on hierarchical segmentation, allowing vectorial attribute handling. Preliminary results on synthetic and real images confirm the relevance of this methodology, both as a segmentation approach and as an experimental framework for criteria evaluation

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Inter / intra operator errors and high-time consumption induced by manual delineation, are the main drawbacks nowadays in clinical PET tumor segmentation. Several methodologies have been proposed to automate this task. However, there is not yet a validated general protocol to use in clinical routine. Multimodality imaging has been shown to provide good performance, taking into account both functional and anatomical scopes together for segmentation decision. In this context, the involved images used are generally required to be spatially corresponding. However, this is not always the case due to acquisition constraints or for multidate follow-up. In this work, we propose a spatially independent algorithm that avoids image pre-processing (e.g. image registration) or acquisition adjustments for multimodal segmentation. In particular, non-spatially correspondent images (such as multitemporal ones) can be directly exploited taking advantage of hierarchical image structure properties. Regions, obtained from hierarchical models of images, are coevaluated to match similar ones such as tumors on PET and CT. Results show good performance in terms of time-computing and robustnesses dealing with PET/CT segmentation problems such as necrosis, compared with other methodologies.

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