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- W2805544462 abstract "Positron emission tomography and computed tomography (PET-CT) dual-modality imaging provides critical diagnostic information in modern cancer diagnosis and therapy. Automated accurate tumor delineation is essentially important in computer-assisted tumor reading and interpretation based on PET-CT. In this paper, we propose a novel approach for the segmentation of lung tumors that combines the powerful fully convolutional networks (FCN) based semantic segmentation framework (3D-UNet) and the graph cut based co-segmentation model. First, two separate deep UNets are trained on PET and CT, separately, to learn high level discriminative features to generate tumor/non-tumor masks and probability maps for PET and CT images. Then, the two probability maps on PET and CT are further simultaneously employed in a graph cut based co-segmentation model to produce the final tumor segmentation results. Comparative experiments on 32 PET-CT scans of lung cancer patients demonstrate the effectiveness of our method." @default.
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- W2805544462 date "2018-04-01" @default.
- W2805544462 modified "2023-10-03" @default.
- W2805544462 title "3D fully convolutional networks for co-segmentation of tumors on PET-CT images" @default.
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- W2805544462 doi "https://doi.org/10.1109/isbi.2018.8363561" @default.
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