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

Internet

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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