Deep learning segmentation of paediatric brain tumours using diffusion-weighted MRI: towards an early, in vivo classification pipeline.

Griffiths-King DJ, Mulvany T, Rose HEL, Peet AC, Apps JR, Arvanitis TN, Novak J

DOI: 10.1093/braincomms/fcag317
Published: 2026
Container: Brain communications
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1093/braincomms/fcag317,
  title = {Deep learning segmentation of paediatric brain tumours using diffusion-weighted MRI: towards an early, in vivo classification pipeline.},
  author = {Griffiths-King DJ and Mulvany T and Rose HEL and Peet AC and Apps JR and Arvanitis TN and Novak J},
  year = {2026},
  journal = {Brain communications},
  doi = {10.1093/braincomms/fcag317},
  url = {https://doi.org/10.1093/braincomms/fcag317}
}

RIS

TY  - JOUR
TI  - Deep learning segmentation of paediatric brain tumours using diffusion-weighted MRI: towards an early, in vivo classification pipeline.
AU  - Griffiths-King DJ
AU  - Mulvany T
AU  - Rose HEL
AU  - Peet AC
AU  - Apps JR
AU  - Arvanitis TN
AU  - Novak J
PY  - 2026
JO  - Brain communications
DO  - 10.1093/braincomms/fcag317
UR  - https://doi.org/10.1093/braincomms/fcag317
ER  -

APA

DJ, G., T, M., HEL, R., AC, P., JR, A., TN, A., & J, N. (2026). Deep learning segmentation of paediatric brain tumours using diffusion-weighted MRI: towards an early, in vivo classification pipeline.. Brain communications. https://doi.org/10.1093/braincomms/fcag317

Source records

pubmed · retrieved 2026-09-25T09:17:26.575Z