基于unet的脑肿瘤MRI图像自动分割

Bhargavi S. Vittikop, S. R. Dhotre
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引用次数: 4

摘要

脑肿瘤中更值得注意的检查是排列和巨量评估是肿瘤程度的保证。非侵入性核磁共振成像(MRI)系统已成为一种尖端的无电离辐射脑肿瘤分析设备。通过手动从三维MRI体积中划分脑肿瘤程度是一项繁琐的任务,执行特别依赖于管理员的参与。在特定情况下,可靠的完全程序化的脑肿瘤分割策略对于有效地估计肿瘤程度是非常重要的。为了研究这一点,我们提出了一个完全程序化的脑肿瘤分割策略,该策略是利用基于U-Net的深度卷积网络创建的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Segmentation of MRI Images for Brain Tumor using unet
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.
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