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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.


nii to hdr/img conversion.
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Figure 12: nii to hdr/img conversion.

Mentions: Windows has almost 90% of the operating system market share and for that reason it was essential to design a Windows compatible version of DeID. One difference in the development of DeID for Windows compared to Linux and Mac versions is that DeID for Windows calls BET using the MRICron implementation of BET because it is the only implementation of BET for Windows. Importantly, this Windows implementation of BET only supports the hdr/img file bundle. DeID therefore converts nii files to hdr/img pairs to in order to perform skull-stripping. Figure 12 presents the key procedures of this conversion function.


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)

nii to hdr/img conversion.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 12: nii to hdr/img conversion.
Mentions: Windows has almost 90% of the operating system market share and for that reason it was essential to design a Windows compatible version of DeID. One difference in the development of DeID for Windows compared to Linux and Mac versions is that DeID for Windows calls BET using the MRICron implementation of BET because it is the only implementation of BET for Windows. Importantly, this Windows implementation of BET only supports the hdr/img file bundle. DeID therefore converts nii files to hdr/img pairs to in order to perform skull-stripping. Figure 12 presents the key procedures of this conversion function.

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.