NIMG-29. DEVELOPING AUTOMATIC SEGMENTATION METHOD FOR BRAIN TUMOR MR IMAGES THAT CAN BE USED AT MULTIPLE FACILITIES

BACKGROUND: Manual segmentation of brain tumor images from a large volume of MR images generated in clinical routines is difficult and time-consuming. Hence, it is imperative to develop a machine learning model for automated segmentation of brain tumor images. PURPOSE: Machine learning models for au...

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Bibliografiske detaljer
Udgivet i:Neuro Oncol
Main Authors: Takahashi, Satoshi, Takahashi, Masamichi, Kinoshita, Manabu, Miyake, Mototaka, Kawaguchi, Risa, Shinojima, Naoki, Mukasa, Akitake, Saito, Kuniaki, Nagane, Motoo, Otani, Ryohei, Ueki, Keisuke, Tanaka, Shota, Hata, Nobuhiro, Nishikawa, Ryo, Arita, Hideyuki, Nonaka, Masahiro, Tamura, Kaoru, Tateishi, Kensuke, Uda, Takehiro, Fukai, Junya, Okita, Yoshiko, Tsuyuguchi, Naohiro, Kanemura, Yonehiro, Kobayashi, Kazuma, Sese, Jun, Ichimura, Kouichi, Narita, Yoshitaka, Hamamoto, Ryuji
Format: Artigo
Sprog:Inglês
Udgivet: Oxford University Press 2020
Fag:
Neuroimaging
Online adgang:https://ncbi.nlm.nih.gov/pmc/articles/PMC7650924/
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/neuonc/noaa215.642
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