乳腺肿瘤是一种常见的恶性肿瘤,其临床诊断不但费时费力,还容易出现误诊.本文旨在建立一个基于乳腺数据自动分割的乳腺肿瘤计算机辅助诊断模型,提高临床诊断的速度和准确率.为了用卷积神经网络U-Net模型分割对比增强锥光束乳腺计算机断...乳腺肿瘤是一种常见的恶性肿瘤,其临床诊断不但费时费力,还容易出现误诊.本文旨在建立一个基于乳腺数据自动分割的乳腺肿瘤计算机辅助诊断模型,提高临床诊断的速度和准确率.为了用卷积神经网络U-Net模型分割对比增强锥光束乳腺计算机断层扫描(Contrast-Enhanced Cone-Beam Breast CT,CE-CBBCT)数据,本文首先沿冠状面将3维数据转换成2维切片,通过数据默认的窗口对其进行归一化处理.实验结果显示,使用U-Net卷积神经网络对数据进行分割,Dice系数和IoU(Intersection over Union)分别为0.7920和0.6962.然后,本文用不同骨干网络(即各种深度学习分类网络)去替换U-Net的编码器,再次进行分割并对比不同特征提取对分割性能的影响,发现旋转增广方式可以提升各分割网络的性能.其中,基于ResNet152的U形分割网络的性能最好,Dice系数和IoU分别达到0.8410和0.7576.随后,本文又在所有模型中选取5个性能最好的模型组成一个集成模型,重复分割实验,发现此模型有最佳分割性能,平均Dice系数和IoU可达0.8463和0.7676,性能显著提升.值得指出的是,在处理数据时本文仅使用数据默认的窗口,降低了对人工的依赖.展开更多
Because of the growing concern over the radiation dose delivered to patients, X-ray cone-beam CT(CBCT) imaging of low dose is of great interest. It is difficult for traditional reconstruction methods such as Feldkamp ...Because of the growing concern over the radiation dose delivered to patients, X-ray cone-beam CT(CBCT) imaging of low dose is of great interest. It is difficult for traditional reconstruction methods such as Feldkamp to reduce noise and keep resolution at low doses. A typical method to solve this problem is using optimizationbased methods with careful modeling of physics and additional constraints. However, it is computationally expensive and very time-consuming to reach an optimal solution. Recently, some pioneering work applying deep neural networks had some success in characterizing and removing artifacts from a low-dose data set. In this study,we incorporate imaging physics for a cone-beam CT into a residual convolutional neural network and propose a new end-to-end deep learning-based method for slice-wise reconstruction. By transferring 3D projection to a 2D problem with a noise reduction property, we can not only obtain reconstructions of high image quality, but also lower the computational complexity. The proposed network is composed of three serially connected sub-networks: a cone-to-fan transformation sub-network, a 2D analytical inversion sub-network, and an image refinement sub-network. This provides a comprehensive solution for end-to-end reconstruction for CBCT. The advantages of our method are that the network can simplify a 3D reconstruction problem to a 2D slice-wise reconstruction problem and can complete reconstruction in an end-to-end manner with the system matrix integrated into the network design. Furthermore, reconstruction can be less computationally expensive and easily parallelizable compared with iterative reconstruction methods.展开更多
文摘乳腺肿瘤是一种常见的恶性肿瘤,其临床诊断不但费时费力,还容易出现误诊.本文旨在建立一个基于乳腺数据自动分割的乳腺肿瘤计算机辅助诊断模型,提高临床诊断的速度和准确率.为了用卷积神经网络U-Net模型分割对比增强锥光束乳腺计算机断层扫描(Contrast-Enhanced Cone-Beam Breast CT,CE-CBBCT)数据,本文首先沿冠状面将3维数据转换成2维切片,通过数据默认的窗口对其进行归一化处理.实验结果显示,使用U-Net卷积神经网络对数据进行分割,Dice系数和IoU(Intersection over Union)分别为0.7920和0.6962.然后,本文用不同骨干网络(即各种深度学习分类网络)去替换U-Net的编码器,再次进行分割并对比不同特征提取对分割性能的影响,发现旋转增广方式可以提升各分割网络的性能.其中,基于ResNet152的U形分割网络的性能最好,Dice系数和IoU分别达到0.8410和0.7576.随后,本文又在所有模型中选取5个性能最好的模型组成一个集成模型,重复分割实验,发现此模型有最佳分割性能,平均Dice系数和IoU可达0.8463和0.7676,性能显著提升.值得指出的是,在处理数据时本文仅使用数据默认的窗口,降低了对人工的依赖.
基金supported by the National Natural Science Foundation of China(Nos.61771279,11435007)the National Key Research and Development Program of China(No.2016YFF0101304)
文摘Because of the growing concern over the radiation dose delivered to patients, X-ray cone-beam CT(CBCT) imaging of low dose is of great interest. It is difficult for traditional reconstruction methods such as Feldkamp to reduce noise and keep resolution at low doses. A typical method to solve this problem is using optimizationbased methods with careful modeling of physics and additional constraints. However, it is computationally expensive and very time-consuming to reach an optimal solution. Recently, some pioneering work applying deep neural networks had some success in characterizing and removing artifacts from a low-dose data set. In this study,we incorporate imaging physics for a cone-beam CT into a residual convolutional neural network and propose a new end-to-end deep learning-based method for slice-wise reconstruction. By transferring 3D projection to a 2D problem with a noise reduction property, we can not only obtain reconstructions of high image quality, but also lower the computational complexity. The proposed network is composed of three serially connected sub-networks: a cone-to-fan transformation sub-network, a 2D analytical inversion sub-network, and an image refinement sub-network. This provides a comprehensive solution for end-to-end reconstruction for CBCT. The advantages of our method are that the network can simplify a 3D reconstruction problem to a 2D slice-wise reconstruction problem and can complete reconstruction in an end-to-end manner with the system matrix integrated into the network design. Furthermore, reconstruction can be less computationally expensive and easily parallelizable compared with iterative reconstruction methods.
