• Corpus ID: 239024361

Conditional De-Identification of 3D Magnetic Resonance Images

  title={Conditional De-Identification of 3D Magnetic Resonance Images},
  author={Lennart Alexander Van der Goten and Tobias Hepp and Zeynep Akata and Kevin Smith},
Privacy protection of medical image data is challenging. Even if metadata is removed, brain scans are vulnerable to attacks that match renderings of the face to facial image databases. Solutions have been developed to de-identify diagnostic scans by obfuscating or removing parts of the face. However, these solutions either fail to reliably hide the patient’s identity or are so aggressive that they impair further analyses. We propose a new class of de-identification techniques that, instead of… 

Figures and Tables from this paper


Facing privacy in neuroimaging: removing facial features degrades performance of image analysis methods
All three outcome measures were affected differently by FFR, including failure of analysis methods and both “random” variation and systematic differences, to ensure high-quality neuroimaging research while protecting participants’ privacy.
Obscuring Surface Anatomy in Volumetric Imaging Data
Improved the understanding of the effect of surface deformation approaches on the quality of de-identified data and to provide a useful de-identification tool for MR and CT acquisitions is hoped for.
A technique for the deidentification of structural brain MR images
Results suggested that the automatic defacing algorithm is robust, efficiently removes nonbrain tissue, and does not unduly influence the outcome of the processing methods utilized; in some cases, skull‐stripping was improved.
Facial Recognition Software Success Rates for the Identification of 3D Surface Reconstructed Facial Images: Implications for Patient Privacy and Security
Assessment of the ability of a computer application to match research subjects’ 3D facial reconstructions with conventional photographs of their face found facial recognition software has the potential to recognize features on 3D CT surface reconstructions and match these with photographs, with implications for PHI.
Preserving Privacy in Structural Neuroimages
The Quickshear Defacing method uses a convex hull to identify a plane that divides the volume into two parts, one containing facial features and another the brain volume, and removes the voxels on the facial features side and can provide reductions in running time.
Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks
This work proposes a method to generate synthetic abnormal MRI images with brain tumors by training a generative adversarial network using two publicly available data sets of brain MRI, and demonstrates the value of generative models as an anonymization tool.
DeepPrivacy: A Generative Adversarial Network for Face Anonymization
A novel architecture which is able to automatically anonymize faces in images while retaining the original data distribution is proposed, based on a conditional generative adversarial network, which generates highly realistic faces with a seamless transition between the generated face and the existing background.
Attribute preserved face de-identification
This paper recognizes the need of de-identifying a face image while preserving a large set of facial attributes, which has not been explicitly studied before and forms an objective function and uses gradient descent to learn the optimal weights for fusing k images.
Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods
This paper introduces a robust, learning-based brain extraction system (ROBEX), which combines a discriminative and a generative model to achieve the final result and shows that ROBEX provides significantly improved performance measures for almost every method/dataset combination.
A hybrid approach to the Skull Stripping problem in MRI
A novel skull-stripping algorithm based on a hybrid approach that combines watershed algorithms and deformable surface models is presented, resulting in a robust and automated procedure.