论著摘要 |【AI-CT】用于肺结节分类的三维多视图卷积神经网络(双语版)

2018-01-15 18:09:41 admin 0
标签:   人工智能 AI CT 肺结节 分类

3D multi-view convolutional neural networks for lung nodule classification.

发表日期: 2017.11.16   来源:PLoS One. 2017 Nov 16;12(11):e0188290.

作者:

Kang G1,2, Liu K1, Hou B1, Zhang N1.

作者介绍:

1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China.

2. Beijing Ciji Network Technology Co., Ltd., Beijing, China.

摘要

三维卷积神经网络(CNN)能够充分利用肺结节的三维空间信息,多视图策略有助于提高二维CNN在肺结节分类中的性能。在本文中,我们利用三维多视点卷积神经网络(MV-CNN),结合链式结构和无回路有向图结构(包括3D开端和3D开端-网络)探索肺结节的分类。所有网络采用多视图一网络策略。我们在肺部图像数据库联盟和图像数据库资源计划数据库(LIDC-IDRI)的计算机断层扫描(CT)图像上进行二元分类(良性和恶性)和三元分类(良性,原发恶性和恶性转移)。所有结果都通过10倍交叉验证获得。对于链式结构的MV-CNN,结果通过重要边缘,显示3D MV-CNN的性能优于2D MV-CNN。最终,3D 开端网络的二进制分类错误率为4.59%,三元分类错误率为7.70%,这两者对于相应的任务都表现出优越的结果。我们比较多视图一网络战略和一视图一网络战略,结果表明,多视图一网络策略与一观一网的战略相比可以实现较低的误差。

Abstact

The 3D convolutional neural network (CNN) is able to make full use of the spatial 3D context information of lung nodules, and the multi-view strategy has been shown to be useful for improving the performance of 2D CNN in classifying lung nodules. In this paper, we explore the classification of lung nodules using the 3D multi-view convolutional neural networks (MV-CNN) with both chain architecture and directed acyclic graph architecture, including 3D Inception and 3D Inception-ResNet. All networks employ the multi-view-one-network strategy. We conduct a binary classification (benign and malignant) and a ternary classification (benign, primary malignant and metastatic malignant) on Computed Tomography (CT) images from Lung Image Database Consortium and Image Database Resource Initiative database (LIDC-IDRI). All results are obtained via 10-fold cross validation. As regards the MV-CNN with chain architecture, results show that the performance of 3D MV-CNN surpasses that of 2D MV-CNN by a significant margin. Finally, a 3D Inception network achieved an error rate of 4.59% for the binary classification and 7.70% for the ternary classification, both of which represent superior results for the corresponding task. We compare the multi-view-one-network strategy with the one-view-one-network strategy. The results reveal that the multi-view-one-network strategy can achieve a lower error rate than the one-view-one-network strategy.

阅读原文:PMID: 29145492  PMCID: PMC5690636  DOI: 10.1371/journal.pone.0188290


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