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

doi:10.1117/12.2521509

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

研究成果: 書籍/レポートタイプへの寄稿 › 会議への寄与

1 被引用数 (Scopus)

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.

本文言語	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
出版社	SPIE
ISBN（電子版）	9781510627758
DOI	https://doi.org/10.1117/12.2521509
出版ステータス	出版済み - 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月 7 2019 → 1月 9 2019

出版物シリーズ

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

会議

会議	International Forum on Medical Imaging in Asia 2019
国/地域	シンガポール
City	Singapore
Period	1/7/19 → 1/9/19

!!!All Science Journal Classification (ASJC) codes

電子材料、光学材料、および磁性材料
凝縮系物理学
コンピュータサイエンスの応用
応用数学
電子工学および電気工学

UN SDG

この成果は、次の持続可能な開発目標に貢献しています

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10.1117/12.2521509

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引用スタイル

Nakano, R., Arimura, H., Haekal, M., & Ohga, S. (2019). Automated segmentation framework of lung gross tumor volumes on 3D planning CT images using dense V-Net deep learning. In J. H. Kim, F. Lin, & H. Fujita (Eds.), International Forum on Medical Imaging in Asia 2019 記事 110500Y (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 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 その他.
International Forum on Medical Imaging in Asia 2019. ed. / Jong Hyo Kim; Feng Lin; Hiroshi Fujita. SPIE, 2019. 110500Y (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11050).

研究成果: 書籍/レポートタイプへの寄稿 › 会議への寄与

Nakano, R, Arimura, H, Haekal, M & Ohga, S 2019, Automated segmentation framework of lung gross tumor volumes on 3D planning CT images using dense V-Net deep learning. in JH Kim, F Lin & H Fujita (eds), International Forum on Medical Imaging in Asia 2019., 110500Y, Proceedings of SPIE - The International Society for Optical Engineering, vol. 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, Risa ; Arimura, Hidetaka ; Haekal, Mohammad その他. / 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. editor / Jong Hyo Kim ; Feng Lin ; Hiroshi Fujita. SPIE, 2019. (Proceedings of SPIE - The International Society for Optical Engineering).

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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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AB - 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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Automated segmentation framework of lung gross tumor volumes on 3D planning CT images using dense V-Net deep learning

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出版物シリーズ

会議

!!!All Science Journal Classification (ASJC) codes

UN SDG

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