Dataset Information

A DICOM dataset for evaluation of medical image de-identification.

ABSTRACT: We developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM objects (a total of 1,693 CT, MRI, PET, and digital X-ray images) were selected from datasets published in the Cancer Imaging Archive (TCIA). Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM Attributes to mimic typical clinical imaging exams. The DICOM Standard and TCIA curation audit logs guided the insertion of synthetic PHI into standard and non-standard DICOM data elements. A TCIA curation team tested the utility of the evaluation dataset. With this publication, the evaluation dataset (containing synthetic PHI) and de-identified evaluation dataset (the result of TCIA curation) are released on TCIA in advance of a competition, sponsored by the National Cancer Institute (NCI), for algorithmic de-identification of medical image datasets. The competition will use a much larger evaluation dataset constructed in the same manner. This paper describes the creation of the evaluation datasets and guidelines for their use.

SUBMITTER: Rutherford M

PROVIDER: S-EPMC8285420 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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A DICOM dataset for evaluation of medical image de-identification.

Rutherford Michael M Mun Seong K SK Levine Betty B Bennett William W Smith Kirk K Farmer Phil P Jarosz Quasar Q Wagner Ulrike U Freyman John J Blake Geri G Tarbox Lawrence L Farahani Keyvan K Prior Fred F

Scientific data 20210716 1

We developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM objects (a total of 1,693 CT, MRI, PET, and digital X-ray images) were selected from datasets published in the Cancer Imaging Archive (TCIA). Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM Attributes to mimic typical clinical imaging exams. The DICOM Standard and TCIA curation audit logs guided the insertion of synthetic PHI into standard a ...[more]

PMID: 34272388

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Project description:BackgroundText-based patient medical records are a vital resource in medical research. In order to preserve patient confidentiality, however, the U.S. Health Insurance Portability and Accountability Act (HIPAA) requires that protected health information (PHI) be removed from medical records before they can be disseminated. Manual de-identification of large medical record databases is prohibitively expensive, time-consuming and prone to error, necessitating automatic methods for large-scale, automated de-identification.MethodsWe describe an automated Perl-based de-identification software package that is generally usable on most free-text medical records, e.g., nursing notes, discharge summaries, X-ray reports, etc. The software uses lexical look-up tables, regular expressions, and simple heuristics to locate both HIPAA PHI, and an extended PHI set that includes doctors' names and years of dates. To develop the de-identification approach, we assembled a gold standard corpus of re-identified nursing notes with real PHI replaced by realistic surrogate information. This corpus consists of 2,434 nursing notes containing 334,000 words and a total of 1,779 instances of PHI taken from 163 randomly selected patient records. This gold standard corpus was used to refine the algorithm and measure its sensitivity. To test the algorithm on data not used in its development, we constructed a second test corpus of 1,836 nursing notes containing 296,400 words. The algorithm's false negative rate was evaluated using this test corpus.ResultsPerformance evaluation of the de-identification software on the development corpus yielded an overall recall of 0.967, precision value of 0.749, and fallout value of approximately 0.002. On the test corpus, a total of 90 instances of false negatives were found, or 27 per 100,000 word count, with an estimated recall of 0.943. Only one full date and one age over 89 were missed. No patient names were missed in either corpus.ConclusionWe have developed a pattern-matching de-identification system based on dictionary look-ups, regular expressions, and heuristics. Evaluation based on two different sets of nursing notes collected from a U.S. hospital suggests that, in terms of recall, the software out-performs a single human de-identifier (0.81) and performs at least as well as a consensus of two human de-identifiers (0.94). The system is currently tuned to de-identify PHI in nursing notes and discharge summaries but is sufficiently generalized and can be customized to handle text files of any format. Although the accuracy of the algorithm is high, it is probably insufficient to be used to publicly disseminate medical data. The open-source de-identification software and the gold standard re-identified corpus of medical records have therefore been made available to researchers via the PhysioNet website to encourage improvements in the algorithm.

Project description:Over 200 georeferenced registered rephotographic compilations of the Faroe Islands are provided in this dataset. The position of each compilation is georeferenced and thus locatable on a map. Each compilation consists of a historical and a corresponding contemporary image showing the same scene. With steady object features, these two images of the same geolocation are aligned pixel accurately. In the summer of 2022, all contemporary images were photographed by A. Schaffland, while historical images were retrieved from the National Museum of Denmark collections. Images show Faroese landscape and cultural heritage sites, focusing on relevant areas when the historical images were taken, e.g., Kirkjubøur, Tórshavn, and Saksun. Historic images date from the end of the 19th century to the middle of the 20th century. The historical images were taken by scientists, surveyors, archaeologists, and painters. All historical images are in the public domain, have no known rights, or are shared under a CC license. The contemporary images by A. Schaffland are released under CC BY-NC-SA 4.0. The dataset is organized as a GIS project. Historic images, not already georeferenced, were referenced with street view services. All historical images were added to the GIS database, containing camera position, viewing direction, etc. Each compilation can be displayed as an arrow from the camera position along the view direction on a map. Contemporary images were registered to historical images using a specialized tool. None or only a suboptimal rephotograph could be taken for some historical images. These historical images are still added to the database together with all other original images, providing additional data for improvements in rephotography methods in the upcoming years. The resulting image pairs can be used in image registration, landscape change, urban development, and cultural heritage research. Further, the database can be used for public engagement in heritage and as a benchmark for further rephotography and time-series projects.

Dataset Information

A DICOM dataset for evaluation of medical image de-identification.

Publications

A DICOM dataset for evaluation of medical image de-identification.

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