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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationInternational Forum on Medical Imaging in Asia 2019
EditorsJong Hyo Kim, Feng Lin, Hiroshi Fujita
PublisherSPIE
ISBN (Electronic)9781510627758
DOIs
Publication statusPublished - Jan 1 2019
EventInternational Forum on Medical Imaging in Asia 2019 - Singapore, Singapore
Duration: Jan 7 2019Jan 9 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11050
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceInternational Forum on Medical Imaging in Asia 2019
CountrySingapore
CitySingapore
Period1/7/191/9/19

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

Cite this

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. In J. H. 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. 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. ed. / Jong Hyo Kim; Feng Lin; Hiroshi Fujita. SPIE, 2019. 110500Y (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11050).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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. 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, Singapore, 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. In Kim JH, Lin F, Fujita H, editors, 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. editor / Jong Hyo Kim ; Feng Lin ; Hiroshi Fujita. SPIE, 2019. (Proceedings of SPIE - The International Society for Optical Engineering).
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