Multi-class glioma segmentation on real-world data with missing MRI sequences: comparison of three deep learning algorithms

This study tests the generalisability of three Brain Tumor Segmentation (BraTS) challenge models using a multi-center dataset of varying image quality and incomplete MRI datasets. In this retrospective study, DeepMedic, no-new-Unet (nn-Unet), and NVIDIA-net (nv-Net) were trained and tested using man...

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Detalles Bibliográficos
Autores principales: Pemberton, Hugh G., Wu, Jiaming, Kommers, Ivar, Müller, Domenique M. J., Hu, Yipeng, Goodkin, Olivia, Vos, Sjoerd B., Bisdas, Sotirios, Robe, Pierre A., Ardon, Hilko, Bello, Lorenzo, Rossi, Marco, Sciortino, Tommaso, Nibali, Marco Conti, Berger, Mitchel S., Hervey-Jumper, Shawn L., Bouwknegt, Wim, Van den Brink, Wimar A., Furtner, Julia, Han, Seunggu J., Idema, Albert J. S., Kiesel, Barbara, Widhalm, Georg, Kloet, Alfred, Wagemakers, Michiel, Zwinderman, Aeilko H., Krieg, Sandro M., Mandonnet, Emmanuel, Prados, Ferran, de Witt Hamer, Philip, Barkhof, Frederik, Eijgelaar, Roelant S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10622563/
https://www.ncbi.nlm.nih.gov/pubmed/37919354
http://dx.doi.org/10.1038/s41598-023-44794-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10622563/
https://www.ncbi.nlm.nih.gov/pubmed/37919354
http://dx.doi.org/10.1038/s41598-023-44794-0

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