AutoMICE — Swin UNETR weights + Docker image (mouse micro-CT, 8-class)

This repository hosts both the pretrained checkpoint and the ready-to-run Docker image for AutoMICE (Automated Micro-CT Imaging Contouring Engine): a Swin UNETR model for fully automatic multi-organ segmentation of mouse micro-CT volumes (7 organs + background).

⚡ One-line install (Docker)

git clone https://github.com/namijiang/AutoMICE.git
cd AutoMICE && ./scripts/install_from_hf.sh

docker run --gpus all --rm \
    -v /your/inputs:/data -v /your/outputs:/results \
    automice:latest --data /data --results /results

install_from_hf.sh downloads automice-image.tar.gz from this repo and runs docker load, leaving you with automice:latest. No Docker Hub account is required.

📦 What is in this repo

File	Size	Description
model.pt	~150 MB	Swin UNETR checkpoint (training config test4 in the original code).
automice-image.tar.gz	~3.7 GB	Pre-built Docker image (automice:latest) with model.pt baked in.

The Docker image is the fastest path for users who just want to run inference; the standalone model.pt is for users who prefer the Python CLI or want to load the weights into their own pipeline.

🧠 Associated publication

Robust Automated Mouse Micro-CT Segmentation Using Swin UNEt TRansformers
Lu Jiang, Di Xu, Qifan Xu, Arion Chatziioannou, Keisuke S. Iwamoto, Susanta Hui, Ke Sheng.
Bioengineering (MDPI), 2024. DOI: https://doi.org/10.3390/bioengineering11121255

Please cite the paper when you use these weights or the Docker image.

🏷 Label map (8 classes)

Index	Structure
0	background
1	bladder
2	lung
3	heart
4	liver
5	intestine
6	kidney
7	spleen

🧪 Model summary

• Architecture: Swin UNETR (MONAI implementation).
• Task: 3D semantic segmentation of mouse trunk CT.
• Input: single-channel CT in NIfTI (.nii / .nii.gz).
• Output: 8-class label volume (uint8 NIfTI), same grid as input.

✅ Intended use

• Research and non-clinical workflows (e.g. radiation biology, dosimetry research, image analysis pipelines).
• Users who already have mouse micro-CT in Hounsfield-like intensity units.

Not for: human clinical diagnosis, species or modalities outside the training distribution, or any safety-critical decision without independent validation.

⚙️ Preprocessing (must match training)

Fixed for this checkpoint (original test4 configuration):

Setting	Value
Resampling	isotropic 0.2 mm spacing
Intensity clip	[-1000, 5000] HU → linear to [0, 1]
Sliding window ROI	128 × 128 × 128
Overlap	0.8 (Gaussian blending)
feature_size	36

If your CT is not in Hounsfield Units (e.g. raw scanner counts), apply the scanner-specific calibration before inference, otherwise predictions will be poor.

🐍 Use the bare weights from Python

pip install -U "huggingface_hub[cli]"
hf download namijiang98/AutoMICE model.pt --local-dir ./weights

# from the AutoMICE source tree (https://github.com/namijiang/AutoMICE):
pip install -e .
automice --data ./inputs --results ./outputs

🐳 Use the prebuilt Docker image (manual)

curl -L -o automice-image.tar.gz \
    https://huggingface.co/namijiang98/AutoMICE/resolve/main/automice-image.tar.gz
gunzip -c automice-image.tar.gz | docker load   # creates `automice:latest`

docker run --gpus all --rm \
    -v /your/inputs:/data -v /your/outputs:/results \
    automice:latest --data /data --results /results

⚠️ Limitations

• Trained on mouse micro-CT; transfer to other species or modalities is not guaranteed.
• Performance depends on FOV, resolution, contrast, and HU scaling.
• The Hugging Face online demo Space runs on CPU with downsampling — for publication-quality runs, use the Docker image or the Python CLI.

🔒 License

Apache-2.0. See the LICENSE file in the GitHub repository for the full text.

📚 Citation

@article{jiang2024automice,
  title   = {Robust Automated Mouse Micro-CT Segmentation Using Swin UNEt TRansformers},
  author  = {Jiang, Lu and Xu, Di and Xu, Qifan and Chatziioannou, Arion and
             Iwamoto, Keisuke S. and Hui, Susanta and Sheng, Ke},
  journal = {Bioengineering},
  year    = {2024},
  doi     = {10.3390/bioengineering11121255},
  url     = {https://doi.org/10.3390/bioengineering11121255}
}

Please also cite MONAI and the Swin UNETR papers as appropriate for your manuscript.