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AssemblyNet: A large ensemble of CNNs for 3D Whole Brain MRI Segmentation
Whole brain segmentation using deep learning (DL) is a very challenging ...
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VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation
Deep learning (DL) approaches are state-of-the-art for many medical imag...
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3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles
Detailed whole brain segmentation is an essential quantitative technique...
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DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation
Segmentation of brain tumors and their subregions remains a challenging ...
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Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data
Whole brain segmentation on a structural magnetic resonance imaging (MRI...
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Automatic Brain Structures Segmentation Using Deep Residual Dilated U-Net
Brain image segmentation is used for visualizing and quantifying anatomi...
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Segmentation-based Method combined with Dynamic Programming for Brain Midline Delineation
The midline related pathological image features are crucial for evaluati...
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AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation
Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images. To address this problem, previous DL methods proposed to use a global convolution neural network (CNN) or few independent CNNs. In this paper, we present a novel ensemble method based on a large number of CNNs processing different overlapping brain areas. Inspired by parliamentary decision-making systems, we propose a framework called AssemblyNet, made of two "assemblies" of U-Nets. Such a parliamentary system is capable of dealing with complex decisions and reaching a consensus quickly. AssemblyNet introduces sharing of knowledge among neighboring U-Nets, an "amendment" procedure made by the second assembly at higher-resolution to refine the decision taken by the first one, and a final decision obtained by majority voting. When using the same 45 training images, AssemblyNet outperforms global U-Net by 28 of the Dice metric, patch-based joint label fusion by 15 Finally, AssemblyNet demonstrates high capacity to deal with limited training data to achieve whole brain segmentation in practical training and testing times.
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