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...

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Detaylı Bibliyografya
Yayımlandı:J Radiat Res
Asıl Yazarlar: Kai, Yudai, Arimura, Hidetaka, Ninomiya, Kenta, Saito, Tetsuo, Shimohigashi, Yoshinobu, Kuraoka, Akiko, Maruyama, Masato, Toya, Ryo, Oya, Natsuo
Materyal Türü: Artigo
Dil:Inglês
Baskı/Yayın Bilgisi: Oxford University Press 2020
Konular:
Regular Paper
Online Erişim: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
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