madhavappaneni9/pig-brain-segresnet

Pig Brain SegResNet — brain extraction for porcine T2/CISS MRI

⚠️ License is a placeholder (cc-by-nc-4.0). Confirm the final license before making this repo public / before publication.

3D SegResNet (MONAI) for binary brain extraction (brain vs. background) on sub-millimeter porcine T2-weighted / 3D-CISS MRI. This is the deep-learning component of the pipeline described in "Automated Brain Extraction Using Deep Learning and Atlas-Guided Refinement."

The released pipeline pairs this model with ANTs SyN template refinement; the model alone provides a fast, template-free brain mask and the initial mask that anchors the iterative curation workflow.

Files

• pig_brain_segresnet.pth — model state_dict (load into the architecture below).
• config.yaml — full training/inference config (architecture + post-processing).

Architecture

field	value
init_filters	32
blocks_down	(1, 2, 2, 4)
blocks_up	(1, 1, 1)
in / out channels	1 / 2
spatial_dims	3
params	~18.8 M

Preprocessing

LPS orientation → 1 mm isotropic resampling (bilinear) → foreground crop → spatial pad to 128³ → per-channel non-zero z-score normalization.

Inference

Gaussian-weighted sliding window (ROI 128³, overlap 0.75) with flip TTA, then: softmax → Gaussian smooth → threshold 0.6 → remove small objects (min 500) → keep largest connected component → fill holes. Spatial preprocessing is inverted to return the mask to native resolution.

Usage

import torch
from omegaconf import OmegaConf
from huggingface_hub import hf_hub_download
from monai.networks.nets import SegResNet

repo = "madhavappaneni9/pig-brain-segresnet"
cfg = OmegaConf.load(hf_hub_download(repo, "config.yaml"))
weights = hf_hub_download(repo, "pig_brain_segresnet.pth")

model = SegResNet(
    spatial_dims=cfg.model.spatial_dims,
    init_filters=cfg.model.init_filters,
    in_channels=cfg.model.in_channels,
    out_channels=cfg.model.out_channels,
    blocks_down=tuple(cfg.model.blocks_down),
    blocks_up=tuple(cfg.model.blocks_up),
    dropout_prob=0.0,
)
model.load_state_dict(torch.load(weights, map_location="cpu", weights_only=False))
model.eval()

A full preprocessing + sliding-window + post-processing example is in the demo Space.

Intended use & limitations

• Intended: research brain extraction for porcine T2/CISS MRI; baseline for future porcine brain MRI studies.
• Not for clinical/diagnostic use.
• Trained on a small cohort; defaults calibrated to ~1-month Yorkshire piglets. Out-of-distribution scans (other ages/cohorts/contrasts) may segment poorly and benefit from transfer-learning the checkpoint on a small new-cohort sample.

Citation

Citation to be added on publication.