Face De-Identification Service for Neuroimaging Volumes

Digital medical imaging is a fundamental tool for improving medical practice workflows and supporting clinical diagnosis. Nowadays, healthcare institutions are usually very supported by information and communication systems that meet regular practice requirements. However, the usage of those platforms in collaborative, research and educational scenarios faces several problems. One of the key issues is related with patient data privacy, namely with concerns related with the visual anonymization of studies. In the neuroimaging field, this subject is more complex since, even after removing the patient's information from the images meta-data or burned in the pixel data, it is still possible to identify the patients through 3D reconstruction of the volume. This article proposes and describes the implementation of an end-user service that allows neuroimages facial de-identification of CT volumes, being fully interoperable with production repositories. The solution was validated using a public dataset and made available to the community through its integration with an open source archive server.

[1]  Carlos Costa,et al.  Controlled searching in reversibly de-identified medical imaging archives , 2018, J. Biomed. Informatics.

[2]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[3]  Oleg S. Pianykh,et al.  Digital Imaging and Communications in Medicine : A Practical Introduction and Survival Guide , 2008 .

[4]  Frederico Valente,et al.  Dicoogle, a Pacs Featuring Profiled Content Based Image Retrieval , 2013, PloS one.

[5]  Randy L. Gollub,et al.  Field of View Normalization in Multi-Site Brain MRI , 2018, Neuroinformatics.

[6]  Gregory G. Brown,et al.  Quantitative evaluation of automated skull‐stripping methods applied to contemporary and legacy images: Effects of diagnosis, bias correction, and slice location , 2006, Human brain mapping.

[7]  Tiago Marques Godinho,et al.  A Routing Mechanism for Cloud Outsourcing of Medical Imaging Repositories , 2016, IEEE Journal of Biomedical and Health Informatics.

[8]  José Luís Oliveira,et al.  XDS-I Outsourcing Proxy: Ensuring Confidentiality While Preserving Interoperability , 2014, IEEE Journal of Biomedical and Health Informatics.

[9]  Tobey Clark PACS and Imaging Informatics: Basic Principles and Applications , 2006 .

[10]  D. Peck Digital Imaging and Communications in Medicine (DICOM): A Practical Introduction and Survival Guide , 2009, Journal of Nuclear Medicine.

[11]  Ciprian M. Crainiceanu,et al.  Validated automatic brain extraction of head CT images , 2015, NeuroImage.

[12]  Tiago Marques Godinho,et al.  Anatomy of an Extensible Open Source PACS , 2016, Journal of Digital Imaging.

[13]  Daniel S. Marcus,et al.  Obscuring Surface Anatomy in Volumetric Imaging Data , 2012, Neuroinformatics.

[14]  José Luís Oliveira,et al.  Indexing and retrieving DICOM data in disperse and unstructured archives , 2008, International Journal of Computer Assisted Radiology and Surgery.

[15]  Charles Hildebolt,et al.  Facial Recognition From Volume-Rendered Magnetic Resonance Imaging Data , 2009, IEEE Transactions on Information Technology in Biomedicine.

[16]  Gregory G. Brown,et al.  A technique for the deidentification of structural brain MR images , 2007, Human brain mapping.

[17]  John Hale,et al.  Quickshear Defacing for Neuroimages , 2011, HealthSec.

[18]  Amit Mehta,et al.  PACS: A Guide to the Digital Revolution , 2005 .

[19]  Blake Hannaford,et al.  Evaluation of segmentation methods on head and neck CT: Auto‐segmentation challenge 2015 , 2017, Medical physics.

[20]  Le Xuan Hung,et al.  Neuroimage Data Sets: Rethinking Privacy Policies , 2012, HealthSec.