A Flask-based web application for extracting, storing, browsing, editing, and downloading metadata from DICOM images generated by the stimulated Raman histology (SRH) system, InveNIO.

The application reads DICOM files from a configured image directory, extracts selected metadata using pydicom, stores the metadata in a SQLite database, generates thumbnails and TIFF derivatives for generated SRH images, and provides a browser-based interface for reviewing metadata and opening the corresponding images.

• Extracts DICOM metadata from folders of InveNIO/stimulated Raman histology images.
• Stores image/session metadata in a SQLite database.
• Displays studies, image entries, specimen metadata, and custom user-defined fields in a Flask web interface.
• Supports metadata search.
• Provides an editor for generated metadata and custom data fields.
• Generates JPEG thumbnails for generated SRH images.
• Generates TIFF derivatives for generated SRH images.
• Allows DICOM and TIFF downloads, with optional DICOM anonymiziation.
• Can be run locally with a Python virtual environment or in Docker with Gunicorn.

.
├── extractor_app/
│   ├── __init__.py              # Flask application factory and route registration
│   ├── commands.py              # Flask CLI commands: init-db, update-db
│   ├── config_handler.py        # config.ini reader
│   ├── db.py                    # SQLite connection and schema initialization
│   ├── file_exporter.py         # DICOM/TIFF export and anonymization
│   ├── image_generator.py       # Thumbnail and TIFF generation
│   ├── metadata_extractor.py    # DICOM metadata extraction and database writing
│   ├── schema.sql               # SQLite schema
│   ├── static/                  # JavaScript, generated Resources, OpenSeadragon assets
│   ├── templates/               # Flask/Jinja templates
│   └── views/                   # Flask blueprints
├── config.ini                   # Runtime configuration
├── Dockerfile                   # Docker image definition
├── gunicorn_config.py           # Gunicorn configuration
├── requirements.txt             # Runtime Python dependencies
├── setup.sh / setup.bat         # Local setup helpers
├── run.sh / run.bat             # Local development run helpers
├── update_db.sh / update_db.bat # Manual database update helpers
└── reset_db.sh / reset_db.bat   # Database reset helpers

Make sure to edit the paths inside the config.ini file!

Expected image layout:

instance/images/
├── <study-or-session-folder>/
│   ├── <image_1>.dcm
│   ├── <image_2>.dcm
│   └── ...
└── <another-study-or-session-folder>/
    └── ...

Clone the repository:

git clone https://github.com/vojdam/invenio_extractor.git
cd invenio_extractor

Edit the config.ini file and specify paths! Also change the secret key in __init__.py!

Run the setup.sh or setup.bat helper script to create a virtual environment, initialize and update the database.

Run the run.sh or run.bat helper script to run the web app.

Build the image:

docker build -t invenio_extractor:latest .

Run the container:

docker run -d \
  --name invenio_extractor \
  -p 8080:8080 \
  -v /path/to/local/instance:/invenio_extractor/instance \
  invenio_extractor:latest

Then open:

http://localhost:8080/

Mounting the whole instance/ directory is recommended so that both the input images and the SQLite database persist outside the container.

If you only mount the image folder, for example:

-v /path/to/images:/invenio_extractor/instance/images

the database remains inside the container filesystem and will be lost when the container is removed.

Initialize or reset the SQLite database:

python -m flask --app extractor_app init-db

Update the database from the configured image folder:

python -m flask --app extractor_app update-db

Renew extraction from all folders:

python -m flask --app extractor_app update-db --renew

This project is distributed under the MIT License. See LICENSE for details.