Deep Residual Inception Encoder-Decoder Network for Medical Imaging Synthesis
Deep Residual Inception Encoder-Decoder Network for Medical Imaging Synthesis
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DOI：
10.1109/jbhi.2019.2912659
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发表时间：
2020-01-01
影响因子：
7.7
通讯作者：
Patel, Bhavika
中科院分区：
文献类型：
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作者：
Gao, Fei;Wu, Teresa;Patel, Bhavika
图像合成是精密医学中的一种新型解决方案，适用于无法获得重要医学成像的场景。卷积神经网络（CNN）是这一任务的理想模型，因为它通过大量的层和可训练的参数具有强大的学习能力。在这项研究中，我们提出了一种新的架构的残差初始编码器-解码器神经网络（RIED网络）学习输入图像和目标输出图像之间的非线性映射。为了评估所提出的方法的有效性，将其与文献中的两个模型进行比较：合成CT深度卷积神经网络（sCT-DCNN）和浅层CNN，使用来自马约诊所亚利桑那州的机构乳房X线照片数据集和来自阿尔茨海默病神经成像倡议的公共神经成像数据集。实验结果表明，建议的RIED-Net优于两个模型在两个数据集上的结构相似性指数，平均绝对误差百分比，和峰值信噪比显着。
Image synthesis is a novel solution in precision medicine for scenarios where important medical imaging is not otherwise available. The convolutional neural network (CNN) is an ideal model for this task because of its powerful learning capabilities through the large number of layers and trainable parameters. In this research, we propose a new architecture of residual inception encoder-decoder neural network (RIED-Net) to learn the nonlinear mapping between the input images and targeting output images. To evaluate the validity of the proposed approach, it is compared with two models from the literature: synthetic CT deep convolutional neural network (sCT-DCNN) and shallow CNN, using both an institutional mammogram dataset from Mayo Clinic Arizona and a public neuroimaging dataset from the Alzheimers Disease Neuroimaging Initiative. Experimental results show that the proposed RIED-Net outperforms the two models on both datasets significantly in terms of structural similarity index, mean absolute percent error, and peak signal-to-noise ratio.