scholarly journals High quality machine-robust image features: Identification in nonsmall cell lung cancer computed tomography images

2013 ◽  
Vol 40 (12) ◽  
pp. 121916 ◽  
Author(s):  
Luke A. Hunter ◽  
Shane Krafft ◽  
Francesco Stingo ◽  
Haesun Choi ◽  
Mary K. Martel ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Cell Lung Cancer ◽  
Nonsmall Cell Lung Cancer ◽  
Image Features ◽  
High Quality ◽  
Computed Tomography Images ◽  
Robust Image

scholarly journals Automatic Segmentation of the Gross Target Volume in Non-Small Cell Lung Cancer Using a Modified Version of ResNet

2020 ◽  
Vol 19 ◽  
pp. 153303382094748
Author(s):  
Fuli Zhang ◽  
Qiusheng Wang ◽  
Haipeng Li
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Automatic Segmentation ◽  
Target Volume ◽  
Small Cell ◽  
Small Cell Lung ◽  
Computed Tomography Images ◽  
Positive Rate

Radiotherapy plays an important role in the treatment of non-small cell lung cancer. Accurate segmentation of the gross target volume is very important for successful radiotherapy delivery. Deep learning techniques can obtain fast and accurate segmentation, which is independent of experts’ experience and saves time compared with manual delineation. In this paper, we introduce a modified version of ResNet and apply it to segment the gross target volume in computed tomography images of patients with non-small cell lung cancer. Normalization was applied to reduce the differences among images and data augmentation techniques were employed to further enrich the data of the training set. Two different residual convolutional blocks were used to efficiently extract the deep features of the computed tomography images, and the features from all levels of the ResNet were merged into a single output. This simple design achieved a fusion of deep semantic features and shallow appearance features to generate dense pixel outputs. The test loss tended to be stable after 50 training epochs, and the segmentation took 21 ms per computed tomography image. The average evaluation metrics were: Dice similarity coefficient, 0.73; Jaccard similarity coefficient, 0.68; true positive rate, 0.71; and false positive rate, 0.0012. Those results were better than those of U-Net, which was used as a benchmark. The modified ResNet directly extracted multi-scale context features from original input images. Thus, the proposed automatic segmentation method can quickly segment the gross target volume in non-small cell lung cancer cases and be applied to improve consistency in contouring.

scholarly journals Predicting Outcomes of Nonsmall Cell Lung Cancer Using CT Image Features

IEEE Access ◽  
2014 ◽  
Vol 2 ◽  
pp. 1418-1426 ◽  
Author(s):  
Samuel H. Hawkins ◽  
John N. Korecki ◽  
Yoganand Balagurunathan ◽  
Yuhua Gu ◽  
Virendra Kumar ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cell Lung Cancer ◽  
Nonsmall Cell Lung Cancer ◽  
Image Features ◽  
Ct Image

Update on F-18-fluoro-deoxy-glucose-PET/computed tomography in nonsmall cell lung cancer

2015 ◽  
Vol 21 (4) ◽  
pp. 314-321 ◽  
Author(s):  
Edwin A. Usmanij ◽  
Lioe-Fee de Geus-Oei ◽  
Jan Bussink ◽  
Wim J.G. Oyen
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Cell Lung Cancer ◽  
Nonsmall Cell Lung Cancer
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