Meta-learned SIREN implicit neural network for 5-class brain MRI segmentation on neurite-OASIS coronal slices. Trained with the MetaSeg recipe (Vyas et al., MICCAI 2025): 5,000 outer-loop MAML iterations + 4,001 classifier-only finetune epochs.

Performance

Metric	Value	Source
Mean Dice (OASIS test split, n=80)	0.925 ± 0.013	Reproduction of paper Table 1 row 1 (paper: 0.93 ± 0.012, within 1σ)
INR parameters	83 K	–
Seg head parameters	645	–
Inference time (per slice, A100 BF16)	~0.3 s	inner_steps=100

Output classes:

ID	Class
0	background
1	CSF / ventricles
2	cortex
3	white matter
4	deep grey matter (thalamus, caudate, putamen, hippocampus, etc.)

Usage

python

from inr_brain_seg import InrBrainSeg

model = InrBrainSeg.from_pretrained("basimazam/metaseg-siren-2d-5cls")
mask  = model.segment("path/to/T1.nii.gz")
# mask is a numpy.ndarray of integer class labels, same spatial shape as the input.

The model expects a mid-coronal slice at 192² resolution, percentile-normalised to [0, 1]. If you pass a full 3D NIfTI volume, the wrapper extracts the mid-coronal slice automatically; see preprocessing.json for the canonical recipe.

CLI:

inr-seg-single \
    --model basimazam/metaseg-siren-2d-5cls \
    --input  /data/T1.nii.gz \
    --output /data/T1_seg.nii.gz

Training details

Backbone: SIREN with omega_0=30, 3 hidden layers of width 128.
Outer loop: 5,000 MAML iterations, lr 1e-4, inner_steps=2 at lr 1e-4.
Classifier finetune: 4,001 epochs, lr 5e-5, BCE loss.
Validation budget: 50 inner-loop steps.
Test-time inner loop: K=100 steps at lr 1e-4 (paper default).

Training data: 314 neurite-OASIS subjects (train split). Validation: 21 subjects. Held-out test: 80 subjects.

Intended use

Research only. The model is not validated for clinical use. Best for in-domain neurite-OASIS-style coronal T1 slices.

For cross-site scans (different scanner / vendor / field strength), the zero-shot Dice drops to ~0.30. Use the adapter variant basimazam/metaseg-siren-adapter-v2-ixi which lifts cross-site Dice to 0.69 with a 65,920-parameter MLP adapter.

Reproducibility

The full reproduction log lives at results/RESULTS.md in the accompanying repository, including hardware, wall-clock, seed values, and paper-deviation discussion.

Citation

bibtex

@inproceedings{azam2026inrbrainseg,
  title     = {Coordinate-Field Implicit Networks for Cross-Site Brain MRI Segmentation},
  author    = {Azam, Basim},
  booktitle = {Asian Conference on Computer Vision (ACCV)},
  year      = {2026}
}

@inproceedings{vyas2025metaseg,
  title     = {Fit Pixels, Get Labels: Meta-Learned Implicit Networks for Image Segmentation},
  author    = {Vyas, Kushal and others},
  booktitle = {MICCAI},
  year      = {2025}
}

@inproceedings{sitzmann2020siren,
  title     = {Implicit Neural Representations with Periodic Activation Functions},
  author    = {Sitzmann, Vincent and others},
  booktitle = {NeurIPS},
  year      = {2020}
}

License

Apache License 2.0.