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27 March 2019 Automated segmentation framework of lung gross tumor volumes on 3D planning CT images using dense V-Net deep learning
Risa Nakano, Hidetaka Arimura, Mohammad Haekal, Saiji Ohga
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Proceedings Volume 11050, International Forum on Medical Imaging in Asia 2019; 110500Y (2019) https://doi.org/10.1117/12.2521509
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore
Abstract
Gross tumor volume (GTV) regions of lung tumors should be determined with repeatability and reproducibility on planning computed tomography (CT) in radiation treatment planning to reduce intra- and inter-observer variations of GTV regions. Therefore, we have attempted to develop an automated segmentation framework of the GTV regions on planning CT images using dense V-Net deep learning (DenseVDL). In order to evaluate the GTV regions extracted by the DenseVDL network, Dice similarity coefficient (DSC) was used in this study. The proposed framework achieved average 2D-DSC of 0.73 and 3D-DSC of 0.76 for sixteen cases. The proposed framework using the DenseVDL may be useful for assisting in radiation treatment planning for lung cancer.
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Risa Nakano, Hidetaka Arimura, Mohammad Haekal, and Saiji Ohga "Automated segmentation framework of lung gross tumor volumes on 3D planning CT images using dense V-Net deep learning", Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500Y (27 March 2019); https://doi.org/10.1117/12.2521509
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KEYWORDS
Image segmentation
Computed tomography
3D image processing
Radiotherapy
Lung cancer
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