Semi-automated prediction approach of target shifts using machine learning with anatomical features between planning and pretreatment CT images in prostate radiotherapy

The goal of this study was to develop a semi-automated prediction approach of target shifts using machine learning architecture (MLA) with anatomical features for prostate radiotherapy. Our hypothesis was that anatomical features between planning computed tomography (pCT) and pretreatment cone-beam...

Täydet tiedot

Tallennettuna:
Bibliografiset tiedot
Julkaisussa:J Radiat Res
Päätekijät: Kai, Yudai, Arimura, Hidetaka, Ninomiya, Kenta, Saito, Tetsuo, Shimohigashi, Yoshinobu, Kuraoka, Akiko, Maruyama, Masato, Toya, Ryo, Oya, Natsuo
Aineistotyyppi: Artigo
Kieli:Inglês
Julkaistu: Oxford University Press 2020
Aiheet:
Regular Paper
Linkit:https://ncbi.nlm.nih.gov/pmc/articles/PMC7246080/
https://ncbi.nlm.nih.gov/pubmed/31994702
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/jrr/rrz105
Tagit: Lisää tagi
Ei tageja, Lisää ensimmäinen tagi!

Samankaltaisia teoksia