README.md

metadata

title: SHIA - Brain MRI Segmentation
emoji: 🧠
colorFrom: blue
colorTo: purple
sdk: docker
app_port: 7860
pinned: false
license: mit

SHIA - Structured Health Intelligence for Alzheimer's

Fast GPU-accelerated brain MRI segmentation API.

Setup Instructions

1. Convert the model (run locally first)

# Install conversion dependencies
pip install tensorflowjs tensorflow

# Convert tfjs model to H5 format
cd huggingface
python convert_model.py ../public/models/model18cls ./model18cls

2. Upload to Hugging Face Space

The model18cls/ folder should now contain model.h5 and saved_model/.

API Endpoints

POST /segment/tensor (Recommended)

Upload pre-processed tensor data (256³ uint8, gzipped) for GPU inference. This is the recommended endpoint as it ensures identical preprocessing to local inference.

# Frontend sends the conformed tensor from NiiVue directly
# See atrophy/main.js for implementation

POST /segment

Upload a NIfTI file (.nii or .nii.gz) for segmentation. Note: Server-side preprocessing may differ slightly from local NiiVue preprocessing.

curl -X POST -F "file=@brain.nii.gz" https://YOUR-SPACE.hf.space/segment

POST /segment/compact

Same as /segment but returns base64-gzipped results.

GET /health

Check API status and GPU availability.

Credits

Based on BrainChop by the Neuroneural Lab.