deid

Name	deid JSON
Version	0.3.24 JSON
	download
home_page	https://github.com/pydicom/deid
Summary	best effort deidentify dicom with python and pydicom
upload_time	2024-09-05 18:30:31
maintainer	None
docs_url	None
author	Vanessa Sochat
requires_python	>=3.7
license	LICENSE
keywords	open source python anonymize dicom
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI
coveralls test coverage	No coveralls.

            # Deidentify (deid)

Best effort anonymization for medical images in Python.

[![DOI](https://zenodo.org/badge/94163984.svg)](https://zenodo.org/badge/latestdoi/94163984)
[![Build Status](https://travis-ci.org/pydicom/deid.svg?branch=master)](https://travis-ci.org/pydicom/deid)

Please see our [Documentation](https://pydicom.github.io/deid/).

These are basic Python based tools for working with medical images and text, specifically for de-identification.
The cleaning method used here mirrors the one by CTP in that we can identify images based on known
locations. We are looking for collaborators to develop and validate an OCR cleaning method! Please reach out if you would like to help work on this.


## Installation

### Local
For the stable release, install via pip:

```bash
pip install deid
```

For the development version, install from Github:

```bash
pip install git+git://github.com/pydicom/deid
```

### Docker

```bash
docker build -t pydicom/deid .
docker run pydicom/deid --help
```

## Issues
If you have an issue, or want to request a feature, please do so on our [issues board](https://www.github.com/pydicom/deid/issues).

            

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