Clinical Evaluation of a Multiparametric Deep Learning Model for Glioblastoma Segmentation Using Heterogeneous Magnetic Resonance Imaging Data From Clinical Routine

OBJECTIVES: The aims of this study were, first, to evaluate a deep learning–based, automatic glioblastoma (GB) tumor segmentation algorithm on clinical routine data from multiple centers and compare the results to a ground truth, manual expert segmentation, and second, to evaluate the quality of the...

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Detalles Bibliográficos
Autores principales: Perkuhn, Michael, Stavrinou, Pantelis, Thiele, Frank, Shakirin, Georgy, Mohan, Manoj, Garmpis, Dionysios, Kabbasch, Christoph, Borggrefe, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2018
Materias:
Original Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7598095/
https://www.ncbi.nlm.nih.gov/pubmed/29863600
http://dx.doi.org/10.1097/RLI.0000000000000484

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7598095/
https://www.ncbi.nlm.nih.gov/pubmed/29863600
http://dx.doi.org/10.1097/RLI.0000000000000484

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