OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: The implemented hybrid CNN was able to achieve good lung tumor segmentation performance on CT images. ADVANCES IN KNOWLEDGE: The hybrid CNN has valuable prospect with the ability to segment lung tumor.
OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: The implemented hybrid CNN was able to achieve good lung tumor segmentation performance on CT images. ADVANCES IN KNOWLEDGE: The hybrid CNN has valuable prospect with the ability to segment lung tumor.
Authors: Shuchao Pang; Anan Du; Mehmet A Orgun; Zhenmei Yu; Yunyun Wang; Yan Wang; Guanfeng Liu Journal: Eur J Nucl Med Mol Imaging Date: 2020-03-28 Impact factor: 9.236
Authors: S Jane Henley; Elizabeth M Ward; Susan Scott; Jiemin Ma; Robert N Anderson; Albert U Firth; Cheryll C Thomas; Farhad Islami; Hannah K Weir; Denise Riedel Lewis; Recinda L Sherman; Manxia Wu; Vicki B Benard; Lisa C Richardson; Ahmedin Jemal; Kathleen Cronin; Betsy A Kohler Journal: Cancer Date: 2020-03-12 Impact factor: 6.921
Authors: Vemund Fredriksen; Svein Ole M Sevle; André Pedersen; Thomas Langø; Gabriel Kiss; Frank Lindseth Journal: PLoS One Date: 2022-04-05 Impact factor: 3.240