Automatic segmentation of lung tumors on CT images based on a 2D & 3D hybrid convolutional neural network

A stable and accurate automatic tumor delineation method has been developed to facilitate the intelligent design of lung cancer radiotherapy process. The purpose of this paper is to introduce an automatic tumor segmentation network for lung cancer on CT images based on deep learning. In this paper,...

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Published in:	British journal of radiology Vol. 94; no. 1126; p. 20210038
Main Authors:	Gan, Wutian, Wang, Hao, Gu, Hengle, Duan, Yanhua, Shao, Yan, Chen, Hua, Feng, Aihui, Huang, Ying, Fu, Xiaolong, Ying, Yanchen, Quan, Hong, Xu, Zhiyong
Format:	Journal Article
Language:	English
Published:	England The British Institute of Radiology 01.10.2021
Subjects:	Humans Imaging, Three-Dimensional Lung Neoplasms - diagnostic imaging Neural Networks, Computer Radiographic Image Interpretation, Computer-Assisted - methods Tomography, X-Ray Computed
ISSN:	0007-1285, 1748-880X, 1748-880X
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Abstract	A stable and accurate automatic tumor delineation method has been developed to facilitate the intelligent design of lung cancer radiotherapy process. The purpose of this paper is to introduce an automatic tumor segmentation network for lung cancer on CT images based on deep learning. In this paper, a hybrid convolution neural network (CNN) combining 2D CNN and 3D CNN was implemented for the automatic lung tumor delineation using CT images. 3D CNN used V-Net model for the extraction of tumor context information from CT sequence images. 2D CNN used an encoder-decoder structure based on dense connection scheme, which could expand information flow and promote feature propagation. Next, 2D features and 3D features were fused through a hybrid module. Meanwhile, the hybrid CNN was compared with the individual 3D CNN and 2D CNN, and three evaluation metrics, Dice, Jaccard and Hausdorff distance (HD), were used for quantitative evaluation. The relationship between the segmentation performance of hybrid network and the GTV volume size was also explored. The newly introduced hybrid CNN was trained and tested on a dataset of 260 cases, and could achieve a median value of 0.73, with mean and stand deviation of 0.72 ± 0.10 for the Dice metric, 0.58 ± 0.13 and 21.73 ± 13.30 mm for the Jaccard and HD metrics, respectively. The hybrid network significantly outperformed the individual 3D CNN and 2D CNN in the three examined evaluation metrics ( < 0.001). A larger GTV present a higher value for the Dice metric, but its delineation at the tumor boundary is unstable. The implemented hybrid CNN was able to achieve good lung tumor segmentation performance on CT images. The hybrid CNN has valuable prospect with the ability to segment lung tumor.
AbstractList	A stable and accurate automatic tumor delineation method has been developed to facilitate the intelligent design of lung cancer radiotherapy process. The purpose of this paper is to introduce an automatic tumor segmentation network for lung cancer on CT images based on deep learning.OBJECTIVEA stable and accurate automatic tumor delineation method has been developed to facilitate the intelligent design of lung cancer radiotherapy process. The purpose of this paper is to introduce an automatic tumor segmentation network for lung cancer on CT images based on deep learning.In this paper, a hybrid convolution neural network (CNN) combining 2D CNN and 3D CNN was implemented for the automatic lung tumor delineation using CT images. 3D CNN used V-Net model for the extraction of tumor context information from CT sequence images. 2D CNN used an encoder-decoder structure based on dense connection scheme, which could expand information flow and promote feature propagation. Next, 2D features and 3D features were fused through a hybrid module. Meanwhile, the hybrid CNN was compared with the individual 3D CNN and 2D CNN, and three evaluation metrics, Dice, Jaccard and Hausdorff distance (HD), were used for quantitative evaluation. The relationship between the segmentation performance of hybrid network and the GTV volume size was also explored.METHODSIn this paper, a hybrid convolution neural network (CNN) combining 2D CNN and 3D CNN was implemented for the automatic lung tumor delineation using CT images. 3D CNN used V-Net model for the extraction of tumor context information from CT sequence images. 2D CNN used an encoder-decoder structure based on dense connection scheme, which could expand information flow and promote feature propagation. Next, 2D features and 3D features were fused through a hybrid module. Meanwhile, the hybrid CNN was compared with the individual 3D CNN and 2D CNN, and three evaluation metrics, Dice, Jaccard and Hausdorff distance (HD), were used for quantitative evaluation. The relationship between the segmentation performance of hybrid network and the GTV volume size was also explored.The newly introduced hybrid CNN was trained and tested on a dataset of 260 cases, and could achieve a median value of 0.73, with mean and stand deviation of 0.72 ± 0.10 for the Dice metric, 0.58 ± 0.13 and 21.73 ± 13.30 mm for the Jaccard and HD metrics, respectively. The hybrid network significantly outperformed the individual 3D CNN and 2D CNN in the three examined evaluation metrics (p < 0.001). A larger GTV present a higher value for the Dice metric, but its delineation at the tumor boundary is unstable.RESULTSThe newly introduced hybrid CNN was trained and tested on a dataset of 260 cases, and could achieve a median value of 0.73, with mean and stand deviation of 0.72 ± 0.10 for the Dice metric, 0.58 ± 0.13 and 21.73 ± 13.30 mm for the Jaccard and HD metrics, respectively. The hybrid network significantly outperformed the individual 3D CNN and 2D CNN in the three examined evaluation metrics (p < 0.001). A larger GTV present a higher value for the Dice metric, but its delineation at the tumor boundary is unstable.The implemented hybrid CNN was able to achieve good lung tumor segmentation performance on CT images.CONCLUSIONSThe implemented hybrid CNN was able to achieve good lung tumor segmentation performance on CT images.The hybrid CNN has valuable prospect with the ability to segment lung tumor.ADVANCES IN KNOWLEDGEThe hybrid CNN has valuable prospect with the ability to segment lung tumor. A stable and accurate automatic tumor delineation method has been developed to facilitate the intelligent design of lung cancer radiotherapy process. The purpose of this paper is to introduce an automatic tumor segmentation network for lung cancer on CT images based on deep learning. In this paper, a hybrid convolution neural network (CNN) combining 2D CNN and 3D CNN was implemented for the automatic lung tumor delineation using CT images. 3D CNN used V-Net model for the extraction of tumor context information from CT sequence images. 2D CNN used an encoder-decoder structure based on dense connection scheme, which could expand information flow and promote feature propagation. Next, 2D features and 3D features were fused through a hybrid module. Meanwhile, the hybrid CNN was compared with the individual 3D CNN and 2D CNN, and three evaluation metrics, Dice, Jaccard and Hausdorff distance (HD), were used for quantitative evaluation. The relationship between the segmentation performance of hybrid network and the GTV volume size was also explored. The newly introduced hybrid CNN was trained and tested on a dataset of 260 cases, and could achieve a median value of 0.73, with mean and stand deviation of 0.72 ± 0.10 for the Dice metric, 0.58 ± 0.13 and 21.73 ± 13.30 mm for the Jaccard and HD metrics, respectively. The hybrid network significantly outperformed the individual 3D CNN and 2D CNN in the three examined evaluation metrics ( < 0.001). A larger GTV present a higher value for the Dice metric, but its delineation at the tumor boundary is unstable. The implemented hybrid CNN was able to achieve good lung tumor segmentation performance on CT images. The hybrid CNN has valuable prospect with the ability to segment lung tumor.
Author	Shao, Yan Duan, Yanhua Ying, Yanchen Xu, Zhiyong Gan, Wutian Quan, Hong Chen, Hua Huang, Ying Feng, Aihui Gu, Hengle Wang, Hao Fu, Xiaolong
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SubjectTerms	Humans Imaging, Three-Dimensional Lung Neoplasms - diagnostic imaging Neural Networks, Computer Radiographic Image Interpretation, Computer-Assisted - methods Tomography, X-Ray Computed
Title	Automatic segmentation of lung tumors on CT images based on a 2D & 3D hybrid convolutional neural network
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