3D-CNN with residual sub-blocks for automatic detection of lung nodules from temporal subtraction images
3D-CNN with residual sub-blocks for automatic detection of lung nodules from temporal subtraction images
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DOI：
10.1109/icaiic48513.2020.9065197
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发表时间：
2020-02
期刊：
影响因子：
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通讯作者：
Yuriko Yoshino;Huimin Lu-;Hyoungseop Kim;T. Aoki;S. Kido
中科院分区：
文献类型：
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作者：
Yuriko Yoshino;Huimin Lu-;Hyoungseop Kim;T. Aoki;S. Kido
时间减影（TS）技术是计算机辅助诊断（CAD）系统的一种。通过从当前图像中减去先前图像来获得TS图像，先前图像被扭曲以在先前图像的结构与当前图像之一的结构之间匹配。TS技术去除了正常结构，增强了间隔变化。然而，可以被检测为假阳性的许多减影伪影仍然保留在TS图像上。在本文中，我们提出了基于3D-VGG 16-like架构的具有残差子块的3D-CNN，用于从TS图像中准确检测结节。
Temporal subtraction (TS) technique is one of computer aided diagnosis (CAD) systems. A TS image is obtained by subtracting a previous image, which are warped to match between the structures of the previous image and one of a current image, from the current image. TS technique removes normal structures and enhances interval changes. However, many subtraction artifacts that can be detected as false positives still remain on a TS image. In this paper, we propose 3D-CNNs with residual sub-blocks based on 3D-VGG16-like architecture for detection of nodules accurately from TS images.