Skip to main content

De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient Privacy

Project description

De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient Privacy

Python 3.11.2 Code style: black License Open Source Love Docker PyPI - Version

[!IMPORTANT]
The package is now available on PyPI: pip install mede

This repository contains the De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient Privacy, which enables the user to anonymize a wide variety of medical imaging types, including Magnetic Resonance Imaging (MRI), Computer Tomography (CT), Ultrasound (US), Whole Slide Images (WSI) or MRI raw data (twix).

Overview

This tool combines multiple anonymization steps, including metadata deidentification, defacing and skull-stripping while being faster than current state-of-the-art deidentification tools.

Computationtimes

Getting started

You can install the anonymization tool directly via pip or Docker.

Installation via pip

Our tool is available via pip. You can install it with the following command:

pip install mede

Additional dependencies for text removal

If you want to use the text removal feature, you also need to install Google's Tesseract OCR engine. You can find the installation instructions for your operating system here. On Ubuntu, you can install it via

sudo apt install tesseract-ocr
sudo apt install libtesseract-dev

On MacOS, you can install it via Homebrew:

brew install tesseract

Installation via Docker

Alternatively this tool is distributed via docker. You can find the docker images here. The docker image is available for Linux-based (including Mac) amd64 and arm64 platforms.

For the installation and execution of the docker image, you must have Docker installed on your system.

  1. Pull the docker image

    docker pull morrempe/mede:[tag]   (either arm64 or amd64)
    
  2. Run the docker container with attached volume. Your data will be mounted in the data folder:

    docker run --rm -it -v [Path/to/your/data]:/data morrempe/mede:[tag]
    
  3. Run the script with the corresponding cli parameter, e.g.:

    mede-deidentify [your flags]
    

Usage

De-Identification CLI

usage: mede-deidentify [-h] [-v | --verbose | --no-verbose] [-t | --text-removal | --no-text-removal] [-i INPUT]
                                    [-o OUTPUT] [--gpu GPU] [-s | --skull_strip | --no-skull_strip] [-de | --deface | --no-deface]
                                    [-tw | --twix | --no-twix] [-p PROCESSES]
                                    [-d {basicProfile,cleanDescOpt,cleanGraphOpt,cleanStructContOpt,rtnDevIdOpt,rtnInstIdOpt,rtnLongFullDatesOpt,rtnLongModifDatesOpt,rtnPatCharsOpt,rtnSafePrivOpt,rtnUIDsOpt} [{basicProfile,cleanDescOpt,cleanGraphOpt,cleanStructContOpt,rtnDevIdOpt,rtnInstIdOpt,rtnLongFullDatesOpt,rtnLongModifDatesOpt,rtnPatCharsOpt,rtnSafePrivOpt,rtnUIDsOpt} ...]]

options:
  -h, --help            show this help message and exit
  -v, --verbose, --no-verbose
  -t, --text-removal, --no-text-removal
  -i INPUT, --input INPUT
                        Path to the input data.
  -o OUTPUT, --output OUTPUT
                        Path to save the output data.
  --gpu GPU             GPU device number. (default 0)
  -s, --skull_strip, --no-skull_strip
  -de, --deface, --no-deface
  -tw, --twix, --no-twix
  -w, --wsi, --no-wsi
  -p PROCESSES, --processes PROCESSES
                        Number of processes to use for multiprocessing.
  -d {basicProfile,cleanDescOpt,cleanGraphOpt,cleanStructContOpt,rtnDevIdOpt,rtnInstIdOpt,rtnLongFullDatesOpt,rtnLongModifDatesOpt,rtnPatCharsOpt,rtnSafePrivOpt,rtnUIDsOpt} [{basicProfile,cleanDescOpt,cleanGraphOpt,cleanStructContOpt,rtnDevIdOpt,rtnInstIdOpt,rtnLongFullDatesOpt,rtnLongModifDatesOpt,rtnPatCharsOpt,rtnSafePrivOpt,rtnUIDsOpt} ...], --deidentification-profile {basicProfile,cleanDescOpt,cleanGraphOpt,cleanStructContOpt,rtnDevIdOpt,rtnInstIdOpt,rtnLongFullDatesOpt,rtnLongModifDatesOpt,rtnPatCharsOpt,rtnSafePrivOpt,rtnUIDsOpt} [{basicProfile,cleanDescOpt,cleanGraphOpt,cleanStructContOpt,rtnDevIdOpt,rtnInstIdOpt,rtnLongFullDatesOpt,rtnLongModifDatesOpt,rtnPatCharsOpt,rtnSafePrivOpt,rtnUIDsOpt} ...]
                        Which DICOM deidentification profile(s) to apply. (default None)

Citation

If you use our tool in your work, please cite us with the following BibTeX entry.

@article{rempe2025identification,
  title={De-identification of medical imaging data: a comprehensive tool for ensuring patient privacy},
  author={Rempe, Moritz and Heine, Lukas and Seibold, Constantin and H{\"o}rst, Fabian and Kleesiek, Jens},
  journal={European Radiology},
  pages={1--10},
  year={2025},
  publisher={Springer}
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mede-0.0.4.tar.gz (44.3 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

mede-0.0.4-py3-none-any.whl (41.3 MB view details)

Uploaded Python 3

File details

Details for the file mede-0.0.4.tar.gz.

File metadata

  • Download URL: mede-0.0.4.tar.gz
  • Upload date:
  • Size: 44.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.8

File hashes

Hashes for mede-0.0.4.tar.gz
Algorithm Hash digest
SHA256 3e1ca40db5a2f6710f9b1eca18b18073eff3c143c34d1c4f27b75f4f69c26da6
MD5 0dcb484c124d193f2e15c1cb089f2a4e
BLAKE2b-256 d0ed71629808393027d4222d8bdc33ffd9a9f9004b7fe125c6e35db06f3c595c

See more details on using hashes here.

File details

Details for the file mede-0.0.4-py3-none-any.whl.

File metadata

  • Download URL: mede-0.0.4-py3-none-any.whl
  • Upload date:
  • Size: 41.3 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.8

File hashes

Hashes for mede-0.0.4-py3-none-any.whl
Algorithm Hash digest
SHA256 ca685fed0c626c6dc94ee17fa3f29b8e749b5239702407600e95560c90047d41
MD5 85cf1468904090e303d56bdeeb1f2b8a
BLAKE2b-256 038e79783aa5e0e3b51cbbf31914ba37a87c3d224e83a63dcd35c6e1a41204c4

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page