Metaseg Siren 3d 5cls

--- license: apache-2.0 language: en library_name: pytorch tags: - medical-imaging - brain-mri - segmentation - implicit-neural-representation - meta-learning - siren - 3d - parameter-efficient datasets: - oasis metrics: - dice pipeline_tag: image-segmentation --- # MetaSeg-SIREN 3D 5-class (OASIS) Meta-learned **3D SIREN implicit neural network** for 5-class brain MRI volume segmentation on neurite-OASIS at 80×80×100 resolution. Trained with the MetaSeg recipe (Vyas et al., MICCAI 2025). ## Performance | Metric | Value | Source | |---|---:|---| | Mean Dice (OASIS test split, n=80) | **0.915 ± 0.008** | Reproduction of paper Table 1 row 3 (paper: 0.91 ± 0.011, within 1σ) | | INR parameters | 330 K | – | | Inference time (per volume, A100 BF16) | ~5 s | inner_steps=100 | **Parameter efficiency context**: SegResNet at 4.7 M params hits 0.963 on this task (the empirical ceiling); SwinUNETR at 62 M and 248 M both plateau at 0.965. This 330 K-parameter MetaSeg model is **only 0.05 Dice below the ceiling at 750× fewer parameters than SwinUNETR**. Output classes: same five as `metaseg-siren-2d-5cls` (bg / CSF / cortex / WM / deep grey). ## Usage ```python from inr_brain_seg import InrBrainSeg model = InrBrainSeg.from_pretrained("basimazam/metaseg-siren-3d-5cls") mask = model.segment("path/to/T1_volume.nii.gz") # Returns a 3D numpy.ndarray, shape (80, 80, 100). ``` ## Training details * **Backbone**: 3D SIREN with `omega_0=30`, 3 hidden layers of width 256. * **Volume shape**: 80×80×100 (resampled from neurite-OASIS's native grid). * **Outer loop**: 5,000 MAML iterations, lr 1e-4, inner_steps=2. * **Classifier finetune**: 4,001 epochs, lr 5e-5. ## Citation See repository [`results/RESULTS.md`](https://github.com/basim-azam/inr-brain-seg/blob/main/results/RESULTS.md). ## License Apache License 2.0.

Source context: 4 downloads · 1 likes · Pipeline image-segmentation · Library pytorch · Repo basimazam/metaseg-siren-3d-5cls