If you made any changes in Pure these will be visible here soon.

Fingerprint Dive into the research topics where Hidetaka Arimura is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 3 Similar Profiles
Radiotherapy Medicine & Life Sciences
Magnetic resonance Engineering & Materials Science
radiation therapy Physics & Astronomy
Radiosurgery Medicine & Life Sciences
cancer Physics & Astronomy
Intracranial Aneurysm Medicine & Life Sciences
Lung Medicine & Life Sciences
planning Physics & Astronomy

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 1994 2020

Homological radiomics analysis for prognostic prediction in lung cancer patients

Ninomiya, K. & Arimura, H., Jan 2020, In : Physica Medica. 69, p. 90-100 11 p.

Research output: Contribution to journalArticle

homology
lungs
Lung Neoplasms
cancer
predictions

Automated classification of histological subtypes of NSCLC using support vector machines with radiomic features

Yamada, M., Arimura, H., Ninomiya, K. & Soufi, M., Jan 1 2019, International Forum on Medical Imaging in Asia 2019. Kim, J. H., Fujita, H. & Lin, F. (eds.). SPIE, 110500P. (Proceedings of SPIE - The International Society for Optical Engineering; vol. 11050).

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

Support vector machines
Support Vector Machine
polynomials
cancer
Polynomials

Automated estimation of sizes of unruptured intracranial aneurysms in MRA images using localized principal component analysis

Ma, Z., Arimura, H., Kakeda, S. & Korogi, Y., Jan 1 2019, International Forum on Medical Imaging in Asia 2019. Lin, F., Fujita, H. & Kim, J. H. (eds.). SPIE, 110501L. (Proceedings of SPIE - The International Society for Optical Engineering; vol. 11050).

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

Aneurysm
principal components analysis
Principal component analysis
Principal Component Analysis
Overlapping

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

Nakano, R., Arimura, H., Haekal, M. & Ohga, S., Jan 1 2019, International Forum on Medical Imaging in Asia 2019. Kim, J. H., Lin, F. & Fujita, H. (eds.). SPIE, 110500Y. (Proceedings of SPIE - The International Society for Optical Engineering; vol. 11050).

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

Computed Tomography
Lung
Gross
lungs
learning

Comparison of rigid and deformable image registration for nasopharyngeal carcinoma radiotherapy planning with diagnostic position PET/CT

Kai, Y., Arimura, H., Toya, R., Saito, T., Matsuyama, T., Fukugawa, Y., Shiraishi, S., Shimohigashi, Y., Maruyama, M. & Oya, N., Jan 1 2019, (Accepted/In press) In : Japanese Journal of Radiology.

Research output: Contribution to journalArticle

Tumor Burden
Radiotherapy
Neck
Observer Variation
Positron Emission Tomography Computed Tomography