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, Ohga Saiji

研究成果: 著書/レポートタイプへの貢献会議での発言

抄録

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.

元の言語英語
ホスト出版物のタイトルInternational Forum on Medical Imaging in Asia 2019
編集者Jong Hyo Kim, Feng Lin, Hiroshi Fujita
出版者SPIE
ISBN(電子版)9781510627758
DOI
出版物ステータス出版済み - 1 1 2019
イベントInternational Forum on Medical Imaging in Asia 2019 - Singapore, シンガポール
継続期間: 1 7 20191 9 2019

出版物シリーズ

名前Proceedings of SPIE - The International Society for Optical Engineering
11050
ISSN(印刷物)0277-786X
ISSN(電子版)1996-756X

会議

会議International Forum on Medical Imaging in Asia 2019
シンガポール
Singapore
期間1/7/191/9/19

Fingerprint

Computed Tomography
Lung
Gross
lungs
learning
Tomography
planning
Tumors
Tumor
Similarity Coefficient
Dice
tumors
Segmentation
tomography
Planning
Observer
coefficients
Radiation
Lung Cancer
Repeatability

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

これを引用

Nakano, R., Arimura, H., Haekal, M., & Saiji, O. (2019). Automated segmentation framework of lung gross tumor volumes on 3D planning CT images using dense V-Net deep learning. : J. H. Kim, F. Lin, & H. Fujita (版), International Forum on Medical Imaging in Asia 2019 [110500Y] (Proceedings of SPIE - The International Society for Optical Engineering; 巻数 11050). SPIE. https://doi.org/10.1117/12.2521509

Automated segmentation framework of lung gross tumor volumes on 3D planning CT images using dense V-Net deep learning. / Nakano, Risa; Arimura, Hidetaka; Haekal, Mohammad; Saiji, Ohga.

International Forum on Medical Imaging in Asia 2019. 版 / Jong Hyo Kim; Feng Lin; Hiroshi Fujita. SPIE, 2019. 110500Y (Proceedings of SPIE - The International Society for Optical Engineering; 巻 11050).

研究成果: 著書/レポートタイプへの貢献会議での発言

Nakano, R, Arimura, H, Haekal, M & Saiji, O 2019, Automated segmentation framework of lung gross tumor volumes on 3D planning CT images using dense V-Net deep learning. : JH Kim, F Lin & H Fujita (版), International Forum on Medical Imaging in Asia 2019., 110500Y, Proceedings of SPIE - The International Society for Optical Engineering, 巻. 11050, SPIE, International Forum on Medical Imaging in Asia 2019, Singapore, シンガポール, 1/7/19. https://doi.org/10.1117/12.2521509
Nakano R, Arimura H, Haekal M, Saiji O. Automated segmentation framework of lung gross tumor volumes on 3D planning CT images using dense V-Net deep learning. : Kim JH, Lin F, Fujita H, 編集者, International Forum on Medical Imaging in Asia 2019. SPIE. 2019. 110500Y. (Proceedings of SPIE - The International Society for Optical Engineering). https://doi.org/10.1117/12.2521509
Nakano, Risa ; Arimura, Hidetaka ; Haekal, Mohammad ; Saiji, Ohga. / Automated segmentation framework of lung gross tumor volumes on 3D planning CT images using dense V-Net deep learning. International Forum on Medical Imaging in Asia 2019. 編集者 / Jong Hyo Kim ; Feng Lin ; Hiroshi Fujita. SPIE, 2019. (Proceedings of SPIE - The International Society for Optical Engineering).
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