Hierarchical forest attributes for multimodal tumor segmentation on FDG-PET/contrast-enhanced CT

Abstract : Accurate tumor volume delineation is a crucial step for disease assessment, treatment planning and monitoring of several kinds of cancers. However, this process is complex due to variations in tumors properties. Recently, some methods have been proposed for taking advantage of the spatial and spectral information carried by coupled modalities (e.g., PET-CT, MRI-PET). Simultaneously, the development of attributebased approaches has contributed to improve PET image analysis. In this work, we aim at developing a coupled multimodal / attribute-based approach for image segmentation. Our proposal is to take advantage of hierarchical image models for determining relevant and specific attribute from each modality. These attributes then allow us to define a unique, semantic vectorial image. Sequentially, this image can be processed by a standard segmentation method, in our case a random-walker approach, for segmenting tumors based on their intrinsic attribute-based properties. Experimental results emphasize the relevance of computing region-based attributes from both modalities.
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Francisco Javier Alvarez Padilla, Barbara Romaniuk, Benoît Naegel, Stéphanie Servagi-Vernat, David Morland, et al.. Hierarchical forest attributes for multimodal tumor segmentation on FDG-PET/contrast-enhanced CT. International Symposium on Biomedical Imaging (ISBI), 2018, Washington, United States. pp.163-167, ⟨10.1109/ISBI.2018.8363546⟩. ⟨hal-01695078⟩

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