Face De-Identification Service for Neuroimaging Volumes

Silva, Jorge Miguel; Guerra, Antonio; Silva, João Figueira; Pinho, Eduardo; Costa, Carlos

doi:10.1109/cbms.2018.00032

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…In order to learn and consolidate these concepts, students have to execute a three-month long development projects related to the PACS/DICOM universe, for which most students have used Dicoogle and the Learning Pack. For instance, the projects "Workflow Management in Distributed Medical Imaging Environments" and "3D Defacing -Visual Anatomic Anonymization", which culminated into the production of scientific publications [32,33]. At the end of the academic year, it was sent an anonymous form to students to evaluate these tools as learning resources.…”

Section: Teaching Network and Services In Imagingmentioning

confidence: 99%

Dicoogle Framework for Medical Imaging Teaching and Research

Lebre

Pinho

Silva

et al. 2020

2020 IEEE Symposium on Computers and Communications (ISCC)

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One of the most noticeable trends in healthcare over the last years is the continuous growth of data volume produced and its heterogeneity. In the medical imaging field, the evolution of digital systems is supported by the PACS concept and the DICOM standard. These technologies are deeply grounded in medical laboratories, supporting the production and providing healthcare practitioners with the ability to set up collaborative work environments with researchers and academia to study and improve healthcare practice. However, the complexity of those systems and protocols makes difficult and time-consuming to prototype new ideas or develop applied research, even for skilled users with training in those environments. Dicoogle emerges as a reference tool to achieve those objectives through a set of resources aggregated in the form of a learning pack. It is an open-source PACS archive that, on the one hand, provides a comprehensive view of the PACS and DICOM technologies and, on the other hand, provides the user with tools to easily expand its core functionalities. This paper describes the Dicoogle framework, with particular emphasis in its Learning Pack package, the resources available and the impact of the platform in research and academia. It starts by presenting an overview of its architectural concept, the most recent research backed up by Dicoogle, some remarks obtained from its use in teaching, and worldwide usage statistics of the software. Moreover, a comparison between the Dicoogle platform and the most popular open-source PACS in the market is presented. Xebra: sourceforge.net/projects/xebra 4 CDMedic: sourceforge.net/projects/ cdmedicpacsweb

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Section: Teaching Network and Services In Imagingmentioning

confidence: 99%

Dicoogle Framework for Medical Imaging Teaching and Research

Lebre

Pinho

Silva

et al. 2020

2020 IEEE Symposium on Computers and Communications (ISCC)

Self Cite

View full text Add to dashboard Cite

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“…Cloud providers are commonly seen as "honest-butcurious" [26]. To protect data in those environments, some common methods include, searchable encryption [27]- [29] and pseudonymity [30], [31] schemes. Nevertheless, high availability through fault tolerance is only within the reach of some distributed technologies.…”

Section: Introductionmentioning

confidence: 99%

A Performant Protocol for Distributed Health Records Databases

2021

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Electronic Health Records (EHR) have a distributed nature and can be managed by distinct affinity domains. Sharing patient health information across distinct organisations helps to deliver a wellinformed diagnosis, improving the quality of healthcare service. The federation of those information systems can take the form of a distributed database where data are partitioned and possibly replicated across distinct computational systems. However, the benefits of having a distributed system, such as consistency, availability, and data protection, are mostly absent. This article proposes a distributed database consensus protocol designed to improve the performance of EHR insertion operations, a particularly critical issue in medical imaging cases due to the data volume. It explores the personal and non-transferable nature of EHR and the proposed methodology reduces the data contention through data isolation, improving the overall retrieval performance and detection of misbehaving parties. Furthermore, the proposal follows the recent European General Data Protection Regulation (GDPR), which states that appropriate mechanisms should be used in order to protect data against accidental loss, destruction, or damage, using appropriate technical or organisational measures.INDEX TERMS blockchain, consensus protocol, distributed ledger, distributed databases, electronic health records

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Implementation and validation of face de‐identification (de‐facing) in ADNI4

Schwarz,

Choe,

Rossi

et al. 2024

Alzheimer's & Dementia

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INTRODUCTIONRecent technological advances have increased the risk that de‐identified brain images could be re‐identified from face imagery. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a leading source of publicly available de‐identified brain imaging, who quickly acted to protect participants’ privacy.METHODSAn independent expert committee evaluated 11 face‐deidentification (“de‐facing”) methods and selected four for formal testing.RESULTSEffects of de‐facing on brain measurements were comparable across methods and sufficiently small to recommend de‐facing in ADNI. The committee ultimately recommended mri_reface for advantages in reliability, and for some practical considerations. ADNI leadership approved the committee's recommendation, beginning in ADNI4.DISCUSSIONADNI4 de‐faces all applicable brain images before subsequent pre‐processing, analyses, and public release. Trained analysts inspect de‐faced images to confirm complete face removal and complete non‐modification of brain. This paper details the history of the algorithm selection process and extensive validation, then describes the production workflows for de‐facing in ADNI.Highlights ADNI is implementing “de‐facing” of MRI and PET beginning in ADNI4. “De‐facing” alters face imagery in brain images to help protect privacy. Four algorithms were extensively compared for ADNI and mri_reface was chosen. Validation confirms mri_reface is robust and effective for ADNI sequences. Validation confirms mri_reface negligibly affects ADNI brain measurements.

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Face De-Identification Service for Neuroimaging Volumes

Cited by 3 publications

References 16 publications

Dicoogle Framework for Medical Imaging Teaching and Research

Dicoogle Framework for Medical Imaging Teaching and Research

A Performant Protocol for Distributed Health Records Databases

Implementation and validation of face de‐identification (de‐facing) in ADNI4

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