Full Guide on Developing Locally

Installation

What you need:

1. DICOM server (dcm4che or Orthnac)
2. Meteor to run the app

How to start the app:

1. In the app directory:

   • to install npm packages run: meteor npm install

2. In the app directory:

   • for orthanc run: METEOR_PACKAGE_DIRS="packages" meteor
   • for dcm4chee run: METEOR_PACKAGE_DIRS="packages" meteor --settings config/dcm4cheeDICOMWeb.json

For Developers

Technologies:

• Docker
• Meteor
• MongoDB
• BlazeJs/Spacebars
• Node.js
• JavaScript
• HTML
• CSS/Stylus
• VPS

Main app components:

tesseract-ai: Components and functionality for AI.

tesseract-fiducial: Similar to cornerstone tools probe with customizable information.

tesseract-report: Reporting area for any predictions and calculations, also contains the settings for AI models.

tesseract-server-probe: A cornerstone tool probe like tool that displays findings on the DICOM images. The probe cannot be deleted or manipulated by user.

tesseract-sync-scroll: A toll similar to crosshair tool from cornerstone tools to sync the scrolling on view ports.

tesseract-cancer-study: A replaceable package to add different cancer studies to the app.

tesseract-sync-tools: A tool to sync tools like probe or any other drawing tool.

AI model Deployment:

Models folder include different models and their codes. “pcad.py” is the Flask app which serves predictions with the selected Keras model. By default, one of the available models is loaded and ready to use by the user.

Specification for each model: Each AI model folder needs to have a python class called deploy which is consisted of a build and run methods. By decoupling the model build and model run, fast prediction time is guaranteed.

1. Build: once called, the model architecture is built and the saved weights will be loaded.
2. Run(model, info): contains the code and the designed algorithm, including preprocessing, prediction and post processing. Info is json file containing the case_number(id) and the desired location specified by the user in the Viewer which was sent to the Flask app (in LPS)

Deploying to Production VPS

You need app specific files on App's root directory for deploying to server:

1. Orthanc configuration file orthanc.json, generate this file by following this instruction
2. App configuration production.env which is similar to development.env file
3. In models directory run docker-compose up -d
4. In main directory run docker-compose up -d

These files contain all the confirmation and important information like password and server IP for orthanc and MongoDB to connect.

Orthanc username and password can be changed in orthanc.json file.

Orthanc Installation

Docker usage

Following the instructions below, the docker image will listen for DICOM connections on port 4242, and for web traffic on port 8042. The default username for the web interface is orthanc, and the password is orthanc.

Temporary data storage

docker run --rm -p 4242:4242 -p 8042:8042 jodogne/orthanc-plugins

Persistent data storage

1. Create a persistant data volume for Orthanc to use

   docker create --name sampledata -v /sampledata jodogne/orthanc-plugins
   

   Note: On Windows, you need to use an absolute path for the data volume, like so:

   docker create --name sampledata -v '//C/Users/erik/sampledata' jodogne/orthanc-plugins
   

2. Run Orthanc from Docker with the data volume attached

   docker run --volumes-from sampledata -p 4242:4242 -p 8042:8042 jodogne/orthanc-plugins
   

3. Upload your data and it will be persisted

dcm4che Installation

How to install dcm4che:

1. Install docker-compose https://docs.docker.com/compose/install/
2. Clone dcm4che from https://github.com/dcm4che-dockerfiles/dcm4chee-arc-psql
3. Run docker-compose up in dcm4che directory