De-identification of Medical Images with Retention of Scientific Research Value

Moore, Stephen; Maffitt, David R.; Smith, Kirk E.; Kirby, Justin; Clark, Kenneth W.; Freymann, John; Vendt, Bruce A.; Tarbox, Lawrence; Prior, Fred

doi:10.1148/rg.2015140244

Cited by 58 publications

(42 citation statements)

References 4 publications

(3 reference statements)

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“…TCIA staff then provided customized versions of the Clinical Trial Processor software to deidentify and transfer the challenge dataset to TCIA servers. TCIA curators performed extensive review of both DICOM headers and pixel data to ensure full removal of patient identifiers in compliance with HIPAA regulations, while also taking care not to remove critical information necessary for analysis by challenge participants . The resulting dataset with detailed description can be obtained at http://doi.org/10.7937/K9/TCIA.2017.3r3fvz08…”

Section: Methodsmentioning

confidence: 99%

Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017

et al. 2018

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Section: Methodsmentioning

confidence: 99%

Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017

et al. 2018

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“…TCIA redacts PHI while retaining as much data as possible to ensure scientific usability of the data. 73 TCIA and other research projects 74 use the Posda open source framework 75,76 to implement curation workflows for all DICOM defined objects. Posda incorporates DICOM validation rules and maintains an up-to-date DICOM private tag dictionary.…”

Section: Careful Image Curation Permits Data Aggregationmentioning

confidence: 99%

Open access image repositories: high-quality data to enable machine learning research

et al. 2020

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Originally motivated by the need for research reproducibility and data reuse, large-scale, open access information repositories have become key resources for training and testing of advanced machine learning applications in biomedical and clinical research. To be of value, such repositories must provide large, high-quality data sets, where quality is defined as minimising variance due to data collection protocols and data misrepresentations. Curation is the key to quality. We have constructed a large public access image repository, The Cancer Imaging Archive, dedicated to the promotion of open science to advance the global effort to diagnose and treat cancer. Drawing on this experience and our experience in applying machine learning techniques to the analysis of radiology and pathology image data, we will review the requirements placed on such information repositories by state-of-the-art machine learning applications and how these requirements can be met.

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“…In addition to maintaining the elapsed time, users may want the ability to integrate these imaging studies with other non-imaging data. Therefore, enough metadata must be preserved to ensure compliance with appropriate rules and regulations, while ensuring that researchers can unambiguously locate imaging and associated non-imaging data [100].…”

Section: De-identificationmentioning

confidence: 99%

An Assessment of Imaging Informatics for Precision Medicine in Cancer

Chennubhotla¹,

Clarke²,

Fedorov³

et al. 2017

Yearb Med Inform

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SummaryObjectives: Precision medicine requires the measurement, quantification, and cataloging of medical characteristics to identify the most effective medical intervention. However, the amount of available data exceeds our current capacity to extract meaningful information. We examine the informatics needs to achieve precision medicine from the perspective of quantitative imaging and oncology. Methods: The National Cancer Institute (NCI) organized several workshops on the topic of medical imaging and precision medicine. The observations and recommendations are summarized herein. Results: Recommendations include: use of standards in data collection and clinical correlates to promote interoperability; data sharing and validation of imaging tools; clinician's feedback in all phases of research and development; use of open-source architecture to encourage reproducibility and reusability; use of challenges which simulate real-world situations to incentivize

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De-identification of Medical Images with Retention of Scientific Research Value

Cited by 58 publications

References 4 publications

Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017

Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017

Open access image repositories: high-quality data to enable machine learning research

An Assessment of Imaging Informatics for Precision Medicine in Cancer

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