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REFACING: RECONSTRUCTING ANONYMIZED FACIAL FEATURES USING GANS
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
2019 (English)In: 2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019), IEEE , 2019, p. 1104-1108Conference paper, Published paper (Refereed)
Abstract [en]

Anonymization of medical images is necessary for protecting the identity of the test subjects, and is therefore an essential step in data sharing. However, recent developments in deep learning may raise the bar on the amount of distortion that needs to be applied to guarantee anonymity. To test such possibilities, we have applied the novel CycleGAN unsupervised image-to-image translation framework on sagittal slices of T1 MR images, in order to reconstruct, facial features from anonymized data. We applied the CycleGAN framework on both face-blurred and face-removed images. Our results show that face blurring may not provide adequate protection against malicious attempts at identifying the subjects, while face removal provides more robust anonymization, but is still partially reversible.

Place, publisher, year, edition, pages
IEEE , 2019. p. 1104-1108
Series
IEEE International Symposium on Biomedical Imaging, ISSN 1945-7928, E-ISSN 1945-8452
Keywords [en]
MRI; anonymization; GANs; image-to-image translation
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-160633DOI: 10.1109/ISBI.2019.8759515ISI: 000485040000234ISBN: 978-1-5386-3641-1 (print)OAI: oai:DiVA.org:liu-160633DiVA, id: diva2:1359959
Conference
16th IEEE International Symposium on Biomedical Imaging (ISBI)
Note

Funding Agencies|Swedish research councilSwedish Research Council [201704889]; Center for Industrial Information Technology (CENIIT) at Linkoping University; Knut and Alice Wallenberg foundationKnut & Alice Wallenberg Foundation

Available from: 2019-10-10 Created: 2019-10-10 Last updated: 2023-03-31
In thesis
1. Modern multimodal methods in brain MRI
Open this publication in new window or tab >>Modern multimodal methods in brain MRI
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Magnetic resonance imaging (MRI) is one of the pillars of modern medical imaging, providing a non-invasive means to generate 3D images of the body with high soft-tissue contrast. Furthermore, the possibilities afforded by the design of MRI sequences enable the signal to be sensitized to a multitude of physiological tissue properties, resulting in a wide variety of distinct MRI modalities for clinical and research use. 

This thesis presents a number of advanced brain MRI applications, which fulfill, to differing extents, two complementary aims. On the one hand, they explore the benefits of a multimodal approach to MRI, combining structural, functional and diffusion MRI, in a variety of contexts. On the other, they emphasize the use of advanced mathematical and computational tools in the analysis of MRI data, such as deep learning, Bayesian statistics, and graph signal processing. 

Paper I introduces an anatomically-adapted extension to previous work in Bayesian spatial priors for functional MRI data, where anatomical information is introduced from a T1-weighted image to compensate for the low anatomical contrast of functional MRI data. 

It has been observed that the spatial correlation structure of the BOLD signal in brain white matter follows the orientation of the underlying axonal fibers. Paper II argues about the implications of this fact on the ideal shape of spatial filters for the analysis of white matter functional MRI data. By using axonal orientation information extracted from diffusion MRI, and leveraging the possibilities afforded by graph signal processing, a graph-based description of the white matter structure is introduced, which, in turn, enables the definition of spatial filters whose shape is adapted to the underlying axonal structure, and demonstrates the increased detection power resulting from their use. 

One of the main clinical applications of functional MRI is functional localization of the eloquent areas of the brain prior to brain surgery. This practice is widespread for various invasive surgeries, but is less common for stereotactic radiosurgery (SRS), a non-invasive surgical procedure wherein tissue is ablated by concentrating several beams of high-energy radiation. Paper III describes an analysis and processing pipeline for functional MRI data that enables its use for functional localization and delineation of organs-at-risk for Elekta GammaKnife SRS procedures. 

Paper IV presents a deep learning model for super-resolution of diffusion MRI fiber ODFs, which outperforms standard interpolation methods in estimating local axonal fiber orientations in white matter. Finally, Paper V demonstrates that some popular methods for anonymizing facial data in structural MRI volumes can be partially reversed by applying generative deep learning models, highlighting one way in which the enormous power of deep learning models can potentially be put to use for harmful purposes. 

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2023. p. 63
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2307
Keywords
MRI, Functional MRI, Diffusion MRI, Graph signal processing, Deep learning
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-192793 (URN)10.3384/9789180751360 (DOI)9789180751353 (ISBN)9789180751360 (ISBN)
Public defence
2023-05-05, Hugo Theorell, building, Campus US, Linköping, 13:15 (English)
Opponent
Supervisors
Note

Funding agencies: CENIIT (Center for industrial information technology) and LiU Cancer, as well as the ITEA/VINNOVA-funded projects IMPACT and ASSIST. Center for Medical Image Science and Visualization (CMIV) at Linköping University.

Available from: 2023-03-31 Created: 2023-03-31 Last updated: 2023-04-06Bibliographically approved

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Abramian, David

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