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• W2751068216 abstract "Automatic labeling of anatomical structures in brain images plays an important role in neuroimaging analysis. Among all methods, multi-atlas based segmentation methods are widely used, due to their robustness in propagating prior label information. However, non-linear registration is always needed, which is time-consuming. Alternatively, the patch-based methods have been proposed to relax the requirement of image registration, but the labeling is often determined independently by the target image information, without getting direct assistance from the atlases. To address these limitations, in this paper, we propose a multi-atlas guided 3D fully convolutional networks (FCN) for brain image labeling. Specifically, multi-atlas based guidance is incorporated during the network learning. Based on this, the discriminative of the FCN is boosted, which eventually contribute to accurate prediction. Experiments show that the use of multi-atlas guidance improves the brain labeling performance." @default.
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• W2751068216 date "2017-01-01" @default.
• W2751068216 modified "2023-09-27" @default.
• W2751068216 title "Brain Image Labeling Using Multi-atlas Guided 3D Fully Convolutional Networks" @default.
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• W2751068216 doi "https://doi.org/10.1007/978-3-319-67434-6_2" @default.
• W2751068216 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5669261" @default.
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