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- W2945173631 abstract "White matterWhite matter fibers constituteSupervised classification the main information transfer network of the brain and their accurate digital representation and classification is an important goal of neuroscience image computing. In current clinicalWhite matter practice, the reconstructionNeural networks of desired fibers generally involves manual selection of regions of interest by an expert, which is time-consuming and subject to user bias, expertise and fatigue. Hence, automation of the process is desired. To that end, we propose a supervised classificationSupervised classification approach that utilizes an ensemble of neural networks. EachNeural networks streamline is represented by the fiber orientation distributions in its neighborhood, while the resolved fiber orientations are obtained by generalized q-sampling imaging (GQI) and a subsequent diffusion decomposition method. In order to make the supervised fiber classification succeed in a real scenario where a substantial portion of reconstructed fiber tracts contain spurious fibers, we present a way to create an “invalid” class label through a dedicated training setTraining set creation scheme with an ensemble of networks. The performance of the proposed classification method is demonstrated on major fiber pathways in the brainstem. 30 subjects from Human Connectome Project (HCP)’s publicly available “WU-Minn 500 Subjects + MEG2 dataset” are used as the dataset." @default.
- W2945173631 created "2019-05-29" @default.
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- W2945173631 date "2019-01-01" @default.
- W2945173631 modified "2023-10-01" @default.
- W2945173631 title "Supervised Classification of White Matter Fibers Based on Neighborhood Fiber Orientation Distributions Using an Ensemble of Neural Networks" @default.
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- W2945173631 doi "https://doi.org/10.1007/978-3-030-05831-9_12" @default.
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