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12 March 2018 Multi-pathways CNN for robust vascular segmentation
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Vascular structures are important information for education purpose, surgical planning and analysis. Extraction of blood vessels of the organ is a challenging task in the area of medical image processing and it is the first step before obtaining the structure. It is difficult to get accurate vessel segmentation results even with manually labeling by human being. The difficulty of vessels segmentation is the complicated structure of blood vessels and its large variations that make them hard to recognize. In this paper, we present deep artificial neural network architecture to automatically segment the vessels from computed tomography (CT) image. We proposed deep neural network (DNN) architecture for vessel segmentation from a medical CT volume, which consists of multi deep convolution neural networks to extract features from difference planes of CT data. Due to the problem of varies constrains that we cannot control, we add normalization process to make sure our network will well perform on clinical data. To validate effectiveness and efficiency of our proposed method, we conduct experiments on 20 clinical CT volumes. Our network can yield an average dice coefficient 0.879 on clinical data which better than state-of-the-art methods such as level set, Frangi, and submodular graph cuts.
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Titinunt Kitrungrotsakul, Xian-Hua Han, Xiong Wei, and Yen-Wei Chen "Multi-pathways CNN for robust vascular segmentation", Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105781S (12 March 2018);

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