Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging

Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and the robustness of brain extraction, therefore, are crucial for the accuracy of the entire brain analysis process. The state-of-the-art brain extraction techniques re...

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Veröffentlicht in:	IEEE transactions on medical imaging 2017-11, Vol.36 (11), p.2319-2330
Hauptverfasser:	Mohseni Salehi, Seyed Sadegh, Erdogmus, Deniz, Gholipour, Ali
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Artificial neural networks auto-context Brain Brain - diagnostic imaging Brain extraction CNN Computer architecture Conditional probability Context convolutional neural network Data processing Datasets Feature extraction Female Fetus - diagnostic imaging Fetuses Geometry Humans Image processing Image registration Image segmentation Imaging, Three-Dimensional - methods Magnetic resonance imaging Magnetic Resonance Imaging - methods Medical imaging MRI Neural networks Neural Networks (Computer) Neuroimaging NMR Nuclear magnetic resonance Pregnancy Registration Resonance State of the art Three-dimensional displays Two dimensional displays U-net whole brain segmentation
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