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DeID - a data sharing tool for neuroimaging studies.

Song X, Wang J, Wang A, Meng Q, Prescott C, Tsu L, Eckert MA - Front Neurosci (2015)

Bottom Line: We have developed a Java program that users can use to remove identifying information in neuroimaging datasets, while still maintaining the association among different data types from the same subject for further studies.This software provides a series of user interaction wizards to allow users to select data variables to be de-identified, implements functions for auditing and validation of de-identified data, and enables the user to share the de-identified data in a single compressed package through various communication protocols, such as FTPS and SFTP.DeID runs with Windows, Linux, and Mac operating systems and its open architecture allows it to be easily adapted to support a broader array of data types, with the goal of facilitating data sharing.

View Article: PubMed Central - PubMed

Affiliation: School of Computing, Clemson University Clemson, SC, USA.

ABSTRACT
Funding institutions and researchers increasingly expect that data will be shared to increase scientific integrity and provide other scientists with the opportunity to use the data with novel methods that may advance understanding in a particular field of study. In practice, sharing human subject data can be complicated because data must be de-identified prior to sharing. Moreover, integrating varied data types collected in a study can be challenging and time consuming. For example, sharing data from structural imaging studies of a complex disorder requires the integration of imaging, demographic and/or behavioral data in a way that no subject identifiers are included in the de-identified dataset and with new subject labels or identification values that cannot be tracked back to the original ones. We have developed a Java program that users can use to remove identifying information in neuroimaging datasets, while still maintaining the association among different data types from the same subject for further studies. This software provides a series of user interaction wizards to allow users to select data variables to be de-identified, implements functions for auditing and validation of de-identified data, and enables the user to share the de-identified data in a single compressed package through various communication protocols, such as FTPS and SFTP. DeID runs with Windows, Linux, and Mac operating systems and its open architecture allows it to be easily adapted to support a broader array of data types, with the goal of facilitating data sharing. DeID can be obtained at http://www.nitrc.org/projects/deid.

No MeSH data available.


The architecture of the DeID system.
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Figure 1: The architecture of the DeID system.

Mentions: DeID makes it possible to share demographic and/or behavioral data, T1-weighted brain images, and can be extended to other datasets. Figure 1 presents a design overview of the tool. These data are first sent through the index engine so that a new and unique ID or label can be assigned to the image file. This value is used by the matching engine to associate the T1-weighed images with an ID variable in a corresponding demographic and/or behavioral data file. The original IDs, which might be tracked back to identifiers, are then removed from the image and data files. The demographic and/or behavioral data then goes through the anonymization engine to be de-identified. The brain images are subsequently skull-stripped using the defacing engine that is essentially a call to BET. The de-identified demographic and/or behavioral data along with the skull-stripped images are then either saved to the user's local disk or sent to the remote data repository. These functions and the data processing flow are also summarized in Figure 1.


DeID - a data sharing tool for neuroimaging studies.

Song X, Wang J, Wang A, Meng Q, Prescott C, Tsu L, Eckert MA - Front Neurosci (2015)

The architecture of the DeID system.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4585207&req=5

Figure 1: The architecture of the DeID system.
Mentions: DeID makes it possible to share demographic and/or behavioral data, T1-weighted brain images, and can be extended to other datasets. Figure 1 presents a design overview of the tool. These data are first sent through the index engine so that a new and unique ID or label can be assigned to the image file. This value is used by the matching engine to associate the T1-weighed images with an ID variable in a corresponding demographic and/or behavioral data file. The original IDs, which might be tracked back to identifiers, are then removed from the image and data files. The demographic and/or behavioral data then goes through the anonymization engine to be de-identified. The brain images are subsequently skull-stripped using the defacing engine that is essentially a call to BET. The de-identified demographic and/or behavioral data along with the skull-stripped images are then either saved to the user's local disk or sent to the remote data repository. These functions and the data processing flow are also summarized in Figure 1.

Bottom Line: We have developed a Java program that users can use to remove identifying information in neuroimaging datasets, while still maintaining the association among different data types from the same subject for further studies.This software provides a series of user interaction wizards to allow users to select data variables to be de-identified, implements functions for auditing and validation of de-identified data, and enables the user to share the de-identified data in a single compressed package through various communication protocols, such as FTPS and SFTP.DeID runs with Windows, Linux, and Mac operating systems and its open architecture allows it to be easily adapted to support a broader array of data types, with the goal of facilitating data sharing.

View Article: PubMed Central - PubMed

Affiliation: School of Computing, Clemson University Clemson, SC, USA.

ABSTRACT
Funding institutions and researchers increasingly expect that data will be shared to increase scientific integrity and provide other scientists with the opportunity to use the data with novel methods that may advance understanding in a particular field of study. In practice, sharing human subject data can be complicated because data must be de-identified prior to sharing. Moreover, integrating varied data types collected in a study can be challenging and time consuming. For example, sharing data from structural imaging studies of a complex disorder requires the integration of imaging, demographic and/or behavioral data in a way that no subject identifiers are included in the de-identified dataset and with new subject labels or identification values that cannot be tracked back to the original ones. We have developed a Java program that users can use to remove identifying information in neuroimaging datasets, while still maintaining the association among different data types from the same subject for further studies. This software provides a series of user interaction wizards to allow users to select data variables to be de-identified, implements functions for auditing and validation of de-identified data, and enables the user to share the de-identified data in a single compressed package through various communication protocols, such as FTPS and SFTP. DeID runs with Windows, Linux, and Mac operating systems and its open architecture allows it to be easily adapted to support a broader array of data types, with the goal of facilitating data sharing. DeID can be obtained at http://www.nitrc.org/projects/deid.

No MeSH data available.