Metaseg Siren 2d 5cls
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...
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- vRevision 59a9a5c
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- 16. Sept. 2026
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Quellenbeschreibung
MetaSeg-SIREN 2D 5-class (OASIS)
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
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
@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.
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Quellenauszüge
2 Auszüge--- license: apache-2.0 language: en library_name: pytorch tags: - medical-imaging - brain-mri - segmentation - implicit-neural-representation - meta-learning - siren - parameter-efficient datasets: - oasis metrics: - dice pipeline_tag: image-segmentation --- # MetaSeg-SIREN 2D 5-class (OASIS) 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: ```sh 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 dr...
Source context: 15 downloads · 0 likes · Pipeline image-segmentation · Library pytorch · Repo basimazam/metaseg-siren-2d-5cls
