全卷积网络的MRI图像分割

Yabiao Wang, Zeyu Sun, Chang Liu, Wenbo Peng, Juhua Zhang
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引用次数: 12

摘要

随着各种影像技术的发展,医学影像在为医生进行临床诊断决策提供科学依据方面发挥着越来越重要的作用。同时,挖掘隐藏在这些图像中的有价值的信息,用计算机代替医生的一些辅助医疗工作是非常重要的。因此,人们提出了大量的图像分割方法,其中包括一些经典的算法,其中一些算法表现良好。在本文中,我们建立了一个深度卷积神经网络来分割MRI脑图像。结果表明,该网络在脑灰质和白质的分割上具有良好的性能,并具有良好的泛化能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MRI image segmentation by fully convolutional networks
With the development of various imaging technologies, medical imaging has been playing more important roles on providing scientific proof for doctors to make decisions on clinical diagnosis. At the same time, it is very important to excavate valuable information hidden in those images and take over some auxiliary medical works from doctors by the computer. Therefore, a large number of image segmentation methods, including some classic algorithms have been proposed and some of them perform well. In this paper, we built a deep convolutional neural network to segment the MRI brain images. Results show that the network has a good performance on segmentation of the gray and white matter of brains, it also had a good generalization ability.
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