Automated segmentation of craniopharyngioma on MR images using U-Net-based deep convolutional neural network

OBJECTIVES: To develop a U-Net-based deep learning model for automated segmentation of craniopharyngioma. METHODS: A total number of 264 patients diagnosed with craniopharyngiomas were included in this research. Pre-treatment MRIs were collected, annotated, and used as ground truth to learn and eval...

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Detalles Bibliográficos
Autores principales: Chen, Chaoyue, Zhang, Ting, Teng, Yuen, Yu, Yijie, Shu, Xin, Zhang, Lei, Zhao, Fumin, Xu, Jianguo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Imaging Informatics and Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10017618/
https://www.ncbi.nlm.nih.gov/pubmed/36396792
http://dx.doi.org/10.1007/s00330-022-09216-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10017618/
https://www.ncbi.nlm.nih.gov/pubmed/36396792
http://dx.doi.org/10.1007/s00330-022-09216-1

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