Published June 13, 2022
| Version 1.0
Software
Open
Brain tumour segmentation with incomplete imaging data
Authors/Creators
- 1. UCL Queen Square Institute of Neurology
Description
This repository contains all weights for segmentation models reported within the article:
James K Ruffle, Samia Mohinta, Robert Gray, Harpreet Hyare, Parashkev Nachev. Brain tumour segmentation with incomplete imaging data. Brain Communications. 2023, Volume 5, Issue 2. DOI 10.1093/braincomms/fcad118
If using these works, please cite the above paper. Full article available at https://bit.ly/tumour-seg
For detailed instructions on usage, please refer to https://github.com/high-dimensional/tumour-seg
high-dimensional/tumour-seg is licensed under the GNU General Public License v3.0
For any usage questions, please address them to j.ruffle@ucl.ac.uk
Files
nnUNet_trained_models.zip
Files
(13.8 GB)
| Name | Size | Download all |
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md5:c84c6f4be5ea771589959a738568e6b5
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13.8 GB | Preview Download |
Additional details
Related works
- Is cited by
- Journal article: 10.1093/braincomms/fcad118 (DOI)
- Preprint: arXiv:2206.06120 (arXiv)
Funding
- Wellcome Trust
- Programme for High Dimensional Translation in Neurology 213038
References
- James K Ruffle, Samia Mohinta, Robert Gray, Harpreet Hyare, Parashkev Nachev. Brain tumour segmentation with incomplete imaging data. Brain Communications. 2023. DOI 10.1093/braincomms/fcad118