{
  "_ilex": {
    "architecture": "ilex.models.synthstrip.model.SynthStrip",
    "constructor_kwargs": {
      "feat_mult": 2,
      "inshape": null,
      "max_features": 64,
      "max_pool": 2,
      "nb_conv_per_level": 2,
      "nb_features": 16,
      "nb_levels": 7,
      "return_mask": false
    },
    "format": "ilex",
    "framework_version": {
      "equinox": "0.13.8",
      "ilex": "0.0.0.dev0",
      "jax": "0.10.0",
      "jaxlib": "0.10.0",
      "numpy": "2.4.4",
      "safetensors": "0.7.0"
    },
    "has_state": false,
    "origin": "ilex-native"
  },
  "authors": "Hoopes A., Mora J. S., Dalca A. V., Fischl B., Hoffmann M.",
  "copyright": "Network architecture and training code: copyright (c) 2022 The General Hospital Corporation, distributed as part of FreeSurfer (GPL-2.0). Pretrained weights: dual-licensed by upstream as MIT or CC-BY-4.0 (see https://surfer.nmr.mgh.harvard.edu/docs/synthstrip/). JAX / Equinox port: copyright (c) the ilex authors, released under the Apache-2.0 / GPL-3.0 dual license used by ilex itself.",
  "data_type": "nibabel",
  "description": "SynthStrip skull-stripping network ported to JAX / Equinox from the FreeSurfer mri_synthstrip reference implementation. The network is trained with synthesis-based domain randomisation and is contrast-agnostic across modalities (T1w, T2w, FLAIR, DWI, etc.). It outputs a signed distance transform (SDT) of the brain surface; downstream tooling thresholds the SDT to obtain a binary brain mask.",
  "equinox_version": "0.13.8",
  "ilex_version": "0.0.0.dev0",
  "image_classes": "Single-channel 3D structural MRI volume, contrast-agnostic. Inputs at 1 mm isotropic voxel size and conformed to LIA orientation are recommended for parity with the published checkpoint behaviour.",
  "intended_use": "Research only. Skull stripping of 3D structural MRI volumes that have been conformed to 1 mm isotropic voxels, intensity-normalised (subtract minimum, divide by 99th percentile, clip to [0, 1]), and padded to a multiple of 64 along each spatial axis -- see the FreeSurfer mri_synthstrip preprocessing pipeline.",
  "jax_version": "0.10.0",
  "network_data_format": {
    "inputs": {},
    "outputs": {}
  },
  "numpy_version": "2.4.4",
  "pred_classes": "Single-channel signed distance transform of the brain surface (positive outside, negative inside). Threshold at 1 mm to obtain the binary brain mask used by mri_synthstrip's CLI.",
  "references": [
    "Hoopes A., Mora J. S., Dalca A. V., Fischl B., Hoffmann M. (2022). SynthStrip: Skull-Stripping for Any Brain Image. NeuroImage, 260:119474. doi:10.1016/j.neuroimage.2022.119474",
    "Kelley W. et al. (2024). Boosting Skull-Stripping Performance for Pediatric Brain Images. IEEE International Symposium on Biomedical Imaging (ISBI).",
    "Hoffmann M. (2025). Domain-Randomized Deep Learning for Neuroimage Analysis. IEEE Signal Processing Magazine."
  ],
  "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
  "task": "Skull stripping (brain extraction) of structural MRI volumes",
  "version": "0.1.0"
}