AUCseg: An Automatically Unsupervised Clustering Toolbox for 3D-Segmentation of High-Grade Gliomas in Multi-Parametric MR Images

The segmentation of high-grade gliomas (HGG) using magnetic resonance imaging (MRI) data is clinically meaningful in neurosurgical practice, but a challenging task. Currently, most segmentation methods are supervised learning with labeled training sets. Although these methods work well in most cases...

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Detalles Bibliográficos
Autores principales: Zhao, Botao, Ren, Yan, Yu, Ziqi, Yu, Jinhua, Peng, Tingying, Zhang, Xiao-Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236895/
https://www.ncbi.nlm.nih.gov/pubmed/34195080
http://dx.doi.org/10.3389/fonc.2021.679952

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236895/
https://www.ncbi.nlm.nih.gov/pubmed/34195080
http://dx.doi.org/10.3389/fonc.2021.679952

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