maisi Model by NVIDIA

Follow the steps below to download and run the NVIDIA NIM inference microservice for this model on your infrastructure of choice.

Step 1
Generate API Key

Step 2
Pull and Run the NIM

Export NGC_API_KEY variable.

Bash

export NGC_API_KEY=<your personal NGC key>

The NIM container automatically downloads any required models. To save time and bandwidth it is recommended to provide a local cache directory. This way the NIM will be able to reuse any already downloaded models. Execute the following command to setup the cache directory:

Bash

export LOCAL_NIM_CACHE=~/.cache/nim
mkdir -p $LOCAL_NIM_CACHE

Note that you may need to run (sudo) chmod -R 777 $LOCAL_NIM_CACHE after the MAISI model is downloaded to avoid permission issues.

Run the NIM container with the following commands:

Bash

$ docker login nvcr.io
Username: $oauthtoken
Password: <PASTE_API_KEY_HERE>

Bash

docker run --rm -it --name maisi \
   --runtime=nvidia -e CUDA_VISIBLE_DEVICES=0 \
   -p 8000:8000 \
   -e NGC_API_KEY=$NGC_API_KEY \
   nvcr.io/nim/nvidia/maisi:latest

This command will start the NIM container and expose port 8000 for the user to interact with the NIM.

Open a new terminal, leaving the terminal open with the just launched service. In the new terminal, wait until the health check end point returns {"status":"ready"} before proceeding. This may take a couple of minutes. You can use the following command to query the health check.

Bash

curl -X 'GET' \
    'http://localhost:8000/v1/health/ready' \
    -H 'accept: application/json'

Step 3
Test the NIM

Python Client Example: Generate CT volumes

Save following Python example to a file named nim_client.py. Here's a script to generate synthetic images, this script will POST a request to /v1/maisi/run with image generation parameters. It then handles the response, saving ZIP files or displaying JSON messages as appropriate.

Python

import requests
from datetime import datetime

base_url = "http://localhost:8000"

# Generate synthetic image
payload = {
    "num_output_samples": 1,
    "body_region": ["abdomen"],
    "anatomy_list": ["liver", "spleen"],
    "output_size": [512, 512, 512],
    "spacing": [1.0, 1.0, 1.0],
    "image_output_ext": ".nii.gz",
    "label_output_ext": ".nii.gz",
}

generation_response = requests.post(f"{base_url}/v1/maisi/run", json=payload)

if generation_response.status_code == 200:
    print("Image generation request successful")

    if generation_response.headers.get('Content-Type') == 'application/zip':
        # Save ZIP file with timestamp
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        zip_filename = f"output_{timestamp}.zip"
        with open(zip_filename, "wb") as f:
            f.write(generation_response.content)
        print(f"Output saved as {zip_filename}")

    elif 'application/json' in generation_response.headers.get('Content-Type', ''):
        response_json = generation_response.json()
        print("Response:", response_json.get('message') or response_json.get('error'))

    else:
        print("Unexpected response format")
else:
    print(f"Error {generation_response.status_code}: {generation_response.text}")

Execute the example.

Bash

python nim_client.py

The resulting CT volume and label files will be returned and written to output.zip.

For more details on getting started with this NIM, visit the NVIDIA NIM Docs.

maisi

Step 1Generate API Key

Step 2Pull and Run the NIM

Step 3Test the NIM

Python Client Example: Generate CT volumes

Step 1
Generate API Key

Step 2
Pull and Run the NIM

Step 3
Test the NIM