基于深度卷积神经网络的低剂量CT肺部去噪

吕晓琪; 吴凉; 谷宇; 张明; 李菁

doi:10.11999/JEIT170769

基于深度卷积神经网络的低剂量CT肺部去噪

doi: 10.11999/JEIT170769

吕晓琪¹,
吴凉¹,
谷宇¹,
张明¹,
李菁¹

基金项目:

国家自然科学基金(61771266, 61179019)，内蒙古自治区自然科学基金(2015MS0604)，包头市科技计划项目(2015C2006-14)，内蒙古自治区高等学校科学研究项目(NJZY145)

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- 文章访问数: 1990
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- 被引次数: 0
出版历程
- 收稿日期: 2017-08-01
- 修回日期: 2017-12-01
- 刊出日期: 2018-06-19

Low Dose CT Lung Denoising Model Based on Deep Convolution Neural Network

Funds:

The National Natural Science Foundation of China (61771266, 61179019), The Natural Science Foundation of the Inner Mongolia Autonomous region (2015MS0604), The Science and Technology Plan Projects of Baotou City (2015C2006-14), The Institutions of Higher Learning Scientific Research Projects of the Inner Mongolia Autonomous region (NJZY145)

摘要

摘要: 为了降低低剂量CT肺部噪声对肺癌筛查后期诊断的影响，该文提出一种基于深度卷积神经网络的低剂量CT肺部去噪算法。以完整的CT肺部图像作为输入，池化层对输入图像进行降维处理；批规范化解决随着网络深度的增加性能降低的问题；引入残差学习，学习模型中每一层的残差，最后输出去噪图像。与经典去噪算法实验结果对比，所提方法在解决去噪方面达到了很好的滤波效果，同时也较好地保留了肺部图像的细节信息，大大优于传统的去噪算法。
- 卷积神经网络 /
- 诊断 /
- 肺部去噪 /
- 残差学习 /
- 批规范化
Abstract: In order to reduce the effect of low dose CT lung noise on the late diagnosis of lung cancer screening, a denoising model of low-dose CT lung based on deep convolution neural network is proposed. The input of the model is the complete CT lung image. The pooling layer reduces the dimension of input. Batch normalization works out the poor performance with the increase of network depth. The residuals of each layer are learned with residual learning. Finally, the denoised image is produced. Compared with classical methods, the proposed method achieves good filtering effect in solving the denoising method, and also retaining the details of lung image information, which is much better than the traditional filtering algorithm.
- Convolution Neural Network (CNN) /
- Diagnosis /
- Lung denoising /
- Residual learning /
- Batch normalization

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参考文献(32)

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