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• W2890924277 abstract "Segmentation of mandibles in CT scans during virtual surgical planning is crucial for 3D surgical planning in order to obtain a detailed surface representation of the patients bone. Automatic segmentation of mandibles in CT scans is a challenging task due to large variation in their shape and size between individuals. In order to address this challenge we propose a convolutional neural network approach for mandible segmentation in CT scans by considering the continuum of anatomical structures through different planes. The proposed convolutional neural network adopts the architecture of the U-Net and then combines the resulting 2D segmentations from three different planes into a 3D segmentation. We implement such a segmentation approach on 11 neck CT scans and then evaluate the performance. We achieve an average dice coefficient of $ 0.89 $ on two testing mandible segmentation. Experimental results show that our proposed approach for mandible segmentation in CT scans exhibits high accuracy." @default.
• W2890924277 created "2018-09-27" @default.
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• W2890924277 date "2018-09-18" @default.
• W2890924277 modified "2023-09-27" @default.
• W2890924277 title "3D segmentation of mandible from multisectional CT scans by convolutional neural networks" @default.
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• W2890924277 hasPublicationYear "2018" @default.
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