  Objective: The exchange of health-related data is subject to regional laws
and regulations, such as the General Data Protection Regulation (GDPR) in the
EU or the Health Insurance Portability and Accountability Act (HIPAA) in the
United States, resulting in non-trivial challenges for researchers and
educators when working with these data. In pathology, the digitization of
diagnostic tissue samples inevitably generates identifying data that can
consist of sensitive but also acquisition-related information stored in
vendor-specific file formats. Distribution and off-clinical use of these Whole
Slide Images (WSI) is usually done in these formats, as an industry-wide
standardization such as DICOM is yet only tentatively adopted and slide scanner
vendors currently do not provide anonymization functionality.
  Methods: We developed a guideline for the proper handling of
histopathological image data particularly for research and education with
regard to the GDPR. In this context, we evaluated existing anonymization
methods and examined proprietary format specifications to identify all
sensitive information for the most common WSI formats. This work results in a
software library that enables GDPR-compliant anonymization of WSIs while
preserving the native formats.
  Results: Based on the analysis of proprietary formats, all occurrences of
sensitive information were identified for file formats frequently used in
clinical routine, and finally, an open-source programming library with an
executable CLI-tool and wrappers for different programming languages was
developed.
  Conclusions: Our analysis showed that there is no straightforward software
solution to anonymize WSIs in a GDPR-compliant way while maintaining the data
format. We closed this gap with our extensible open-source library that works
instantaneously and offline.
