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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.


Skull stripping. If the images were not appropriately skull-stripped, as shown in (A), users can choose to re-skull-strip specifying a different threshold (B).
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Figure 7: Skull stripping. If the images were not appropriately skull-stripped, as shown in (A), users can choose to re-skull-strip specifying a different threshold (B).

Mentions: Skull-stripping is performed to remove voxels representing the face. Tools such as ROBEX (Iglesias et al., 2011), mri_watershed (Ségonne et al., 2004), and mri_deface (Bischoff-Grethe et al., 2007) are excellent tools for removing voxels that represent the face, but we chose to use BET (Smith, 2002) for multiple reasons. BET is flexible in handling mulitple image orientations, easy to use for naïve users, computationally efficient, and could be implemented across operating systems. A small tradeoff for these benefits is that voxels representing much of the neck and skull might remain in the skull-stripped image, as shown in Figure 7. Skull-stripping algorithms such as BET estimate the brain outline within a range by providing a fractional intensity threshold parameter (0–1) with the default value being 0.5. This threshold can be varied if too few voxels representing the face are removed (Figure 6) or too many voxels are removed, including those representing the brain.


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)

Skull stripping. If the images were not appropriately skull-stripped, as shown in (A), users can choose to re-skull-strip specifying a different threshold (B).
© Copyright Policy
Related In: Results  -  Collection

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

Figure 7: Skull stripping. If the images were not appropriately skull-stripped, as shown in (A), users can choose to re-skull-strip specifying a different threshold (B).
Mentions: Skull-stripping is performed to remove voxels representing the face. Tools such as ROBEX (Iglesias et al., 2011), mri_watershed (Ségonne et al., 2004), and mri_deface (Bischoff-Grethe et al., 2007) are excellent tools for removing voxels that represent the face, but we chose to use BET (Smith, 2002) for multiple reasons. BET is flexible in handling mulitple image orientations, easy to use for naïve users, computationally efficient, and could be implemented across operating systems. A small tradeoff for these benefits is that voxels representing much of the neck and skull might remain in the skull-stripped image, as shown in Figure 7. Skull-stripping algorithms such as BET estimate the brain outline within a range by providing a fractional intensity threshold parameter (0–1) with the default value being 0.5. This threshold can be varied if too few voxels representing the face are removed (Figure 6) or too many voxels are removed, including those representing the brain.

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.