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Robust Deep Learning–based Segmentation of Glioblastoma on Routine Clinical MRI Scans Using Sparsified Training

PURPOSE: To improve the robustness of deep learning–based glioblastoma segmentation in a clinical setting with sparsified datasets. MATERIALS AND METHODS: In this retrospective study, preoperative T1-weighted, T2-weighted, T2-weighted fluid-attenuated inversion recovery, and postcontrast T1-weighted...

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Detalles Bibliográficos
Autores principales:	Eijgelaar, Roelant S., Visser, Martin, Müller, Domenique M. J., Barkhof, Frederik, Vrenken, Hugo, van Herk, Marcel, Bello, Lorenzo, Conti Nibali, Marco, Rossi, Marco, Sciortino, Tommaso, Berger, Mitchel S., Hervey-Jumper, Shawn, Kiesel, Barbara, Widhalm, Georg, Furtner, Julia, Robe, Pierre A. J. T., Mandonnet, Emmanuel, De Witt Hamer, Philip C., de Munck, Jan C., Witte, Marnix G.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Radiological Society of North America 2020
Materias:	Original Research
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8082349/ https://www.ncbi.nlm.nih.gov/pubmed/33937837 http://dx.doi.org/10.1148/ryai.2020190103

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