fidoptMONAI BraTS SegResNet
MONAI's brats_mri_segmentation bundle, a SegResNet trained on BraTS 2018: the tumour core, whole tumour and enhancing tumour of a glioma from T1, T1c, T2 and FLAIR, as BraTS labels, run as the bundle's inference.json runs it.
SegmentationGPUversion bundle 0.5.4, models/model.pt (sha256 860ccb3f), MONAI 1.5.1brain · MRApache-2.0
Run it
With its KonfAI app, at the benchmark's settings (the fidopt app repositories are private for now: a Hugging Face token with access opens them):
pip install konfai-apps konfai-apps infer fideus-labs/fidopt-segmentation:MONAI_BraTS -i image.nii.gz -o out --gpu 0
The app on Hugging Face: its model, its settings and how it is scored.
Where it works
Its place on each kind of problem it has results on, among the methods ranked there. A method that leads one problem can trail another: read the one closest to yours.
| Problem | Place | Ranked | Datasets |
|---|---|---|---|
| Brain tumours, four MR sequences, segmentation | not ranked yet | 6 of 8 |
Results
| Dataset | # | Read by | Score | Failed | Time s |
|---|---|---|---|---|---|
| BraTS-Africa adult glioma | 1 | Dice | 0.655 | 0 | 18 |
| BraTS-PEDs paediatric high-grade glioma | 1 | Dice | 0.039 | 0 | |
| BraTS 2023 meningioma | 1 | Dice | 0.362 | 0 | 14 |
| Pretreat-MetsToBrain-Masks | 1 | Dice | 0.493 | 0 | |
| UTSW-Glioma, adult glioma before surgery | 1 | Dice | 0.615 | 0 | 14 |
| MU-Glioma-Post, glioma after treatment | 1 | Dice | 0.555 | 0 | 14 |
| UCSD-PTGBM, glioblastoma after treatment | 1 | Dice | 0.493 | 0 | 18 |
- Reference
- Myronenko, BrainLes 2018 (MICCAI)