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首页> 外文会议>International conference on brain informatics >Segmentation of Brain Tumor Tissues in Multi-channel MRI Using Convolutional Neural Networks
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Segmentation of Brain Tumor Tissues in Multi-channel MRI Using Convolutional Neural Networks

机译：利用卷积神经网络分割多通道MRI脑肿瘤组织的分割

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Unmanned segmentation of brain tumors is one of the hardest tasks to be solved in Computer Vision. In this work, we focus on Convolutional Neural Network model to segment tumorous cells in MRI brain scans. The inputs to the network are multi-channel MR image intensity information extracted from patches around each point to be predicted. The pre-processing steps are employed to precise the magnetic field bias and then intensity values are normalized using Z-score technique. The training was done for both HGG and LGG and the network was optimized with SGD in which the gradients are calculated using Nesterov Accelerated Gradient. The obtained results are promising for the complete tumor, the core tumor and the enhancing tumor segmentation. The propounded model achieved a dice score of 0.86, 0.62 and 0.65 for complete, core and enhancing tumor.
机译：脑肿瘤的无人分割是计算机视觉中最难以解决的任务之一。在这项工作中，我们专注于卷积神经网络模型，以在MRI脑扫描中分段瘤细胞。网络的输入是从要预测的每个点周围的补丁提取的多通道MR图像强度信息。采用预处理步骤精确磁场偏置，然后使用Z分数技术归一化强度值。为HGG和LGG进行培训，并使用SGD进行优化，其中使用Nesterov加速梯度计算梯度。所得结果对完全肿瘤，核心肿瘤和增强肿瘤细分有望。对于完全，核心和增强肿瘤，取得的模型达到0.86,0.62和0.65的骰子得分。

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