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Automatic Segmentation of MRI Images for Brain Tumor using unet

机译:使用unet对脑肿瘤的MRI图像进行自动分割

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More noteworthy test in brain tumor is arranging and huge assessment is assurance of the tumor degree. The non invasive magnetic resonance imaging (MRI) system has risen as a cutting edge analytic device for brain tumors without ionizing radiation. Cerebrum tumor degree division by manually from 3D MRI volumes is a tedious assignment and execution is exceptionally depended on administrator's involvement. In specific circumstance, dependable completely programmed division strategy for the brain tumor division is important for a productive estimation of tumor degree. To investigate this, we propose a completely programmed strategy for brain tumor division that is created utilizing U-Net based deep convolutional network.
机译:正在安排对脑肿瘤进行更值得注意的测试,并且对肿瘤的程度进行大量评估。无创磁共振成像(MRI)系统已经成为一种用于脑肿瘤而无需电离辐射的尖端分析设备。通过手动从3D MRI体积中划分脑肿瘤程度是一项乏​​味的任务,执行情况异常取决于管理员的参与。在特定情况下,可靠的完全编程的脑肿瘤分割策略对于有效估计肿瘤程度非常重要。为了对此进行研究,我们提出了一种使用基于U-Net的深度卷积网络创建的脑肿瘤分裂的完全编程策略。

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