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Ryu DW, Lee C, Lee HJ, Shim YS, Hong YJ, Cho JH, Kim S, Lee JM, Yang DW. Assessing the Impact of Defacing Algorithms on Brain Volumetry Accuracy in MRI Analyses. Dement Neurocogn Disord 2024; 23:127-135. [PMID: 39113754 PMCID: PMC11300685 DOI: 10.12779/dnd.2024.23.3.127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 04/26/2024] [Accepted: 04/26/2024] [Indexed: 08/10/2024] Open
Abstract
Background and Purpose To ensure data privacy, the development of defacing processes, which anonymize brain images by obscuring facial features, is crucial. However, the impact of these defacing methods on brain imaging analysis poses significant concern. This study aimed to evaluate the reliability of three different defacing methods in automated brain volumetry. Methods Magnetic resonance imaging with three-dimensional T1 sequences was performed on ten patients diagnosed with subjective cognitive decline. Defacing was executed using mri_deface, BioImage Suite Web-based defacing, and Defacer. Brain volumes were measured employing the QBraVo program and FreeSurfer, assessing intraclass correlation coefficient (ICC) and the mean differences in brain volume measurements between the original and defaced images. Results The mean age of the patients was 71.10±6.17 years, with 4 (40.0%) being male. The total intracranial volume, total brain volume, and ventricle volume exhibited high ICCs across the three defacing methods and 2 volumetry analyses. All regional brain volumes showed high ICCs with all three defacing methods. Despite variations among some brain regions, no significant mean differences in regional brain volume were observed between the original and defaced images across all regions. Conclusions The three defacing algorithms evaluated did not significantly affect the results of image analysis for the entire brain or specific cerebral regions. These findings suggest that these algorithms can serve as robust methods for defacing in neuroimaging analysis, thereby supporting data anonymization without compromising the integrity of brain volume measurements.
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Affiliation(s)
- Dong-Woo Ryu
- Department of Neurology, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - ChungHwee Lee
- Department of Neurology, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Hyuk-je Lee
- Department of Neurology, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yong S Shim
- Department of Neurology, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yun Jeong Hong
- Department of Neurology, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jung Hee Cho
- Department of Neurology, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Seonggyu Kim
- Department of Electronic Engineering, Hanyang University, Seoul, Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
| | - Dong Won Yang
- Department of Neurology, College of Medicine, The Catholic University of Korea, Seoul, Korea
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Zhang Q, Fotaki A, Ghadimi S, Wang Y, Doneva M, Wetzl J, Delfino JG, O'Regan DP, Prieto C, Epstein FH. Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence-review of evidence and proposition of a roadmap to clinical translation. J Cardiovasc Magn Reson 2024; 26:101051. [PMID: 38909656 PMCID: PMC11331970 DOI: 10.1016/j.jocmr.2024.101051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 06/09/2024] [Accepted: 06/18/2024] [Indexed: 06/25/2024] Open
Abstract
BACKGROUND Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment of heart disease; however, limitations of CMR include long exam times and high complexity compared to other cardiac imaging modalities. Recently advancements in artificial intelligence (AI) technology have shown great potential to address many CMR limitations. While the developments are remarkable, translation of AI-based methods into real-world CMR clinical practice remains at a nascent stage and much work lies ahead to realize the full potential of AI for CMR. METHODS Herein we review recent cutting-edge and representative examples demonstrating how AI can advance CMR in areas such as exam planning, accelerated image reconstruction, post-processing, quality control, classification and diagnosis. RESULTS These advances can be applied to speed up and simplify essentially every application including cine, strain, late gadolinium enhancement, parametric mapping, 3D whole heart, flow, perfusion and others. AI is a unique technology based on training models using data. Beyond reviewing the literature, this paper discusses important AI-specific issues in the context of CMR, including (1) properties and characteristics of datasets for training and validation, (2) previously published guidelines for reporting CMR AI research, (3) considerations around clinical deployment, (4) responsibilities of clinicians and the need for multi-disciplinary teams in the development and deployment of AI in CMR, (5) industry considerations, and (6) regulatory perspectives. CONCLUSIONS Understanding and consideration of all these factors will contribute to the effective and ethical deployment of AI to improve clinical CMR.
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Affiliation(s)
- Qiang Zhang
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Big Data Institute, University of Oxford, Oxford, UK.
| | - Anastasia Fotaki
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Royal Brompton Hospital, Guy's and St Thomas' NHS Foundation Trust, London, UK.
| | - Sona Ghadimi
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | - Yu Wang
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | | | - Jens Wetzl
- Siemens Healthineers AG, Erlangen, Germany.
| | - Jana G Delfino
- US Food and Drug Administration, Center for Devices and Radiological Health (CDRH), Office of Science and Engineering Laboratories (OSEL), Silver Spring, MD, USA.
| | - Declan P O'Regan
- MRC Laboratory of Medical Sciences, Imperial College London, London, UK.
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.
| | - Frederick H Epstein
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
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Molchanova N, Maréchal B, Thiran J, Kober T, Huelnhagen T, Richiardi J. Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency. Hum Brain Mapp 2024; 45:e26721. [PMID: 38899549 PMCID: PMC11187735 DOI: 10.1002/hbm.26721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 04/09/2024] [Accepted: 05/06/2024] [Indexed: 06/21/2024] Open
Abstract
With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a growing privacy concern. To protect subject privacy, several algorithms have been developed to de-identify imaging data using blurring, defacing or refacing. Completely removing facial structures provides the best re-identification protection but can significantly impact post-processing steps, like brain morphometry. As an alternative, refacing methods that replace individual facial structures with generic templates have a lower effect on the geometry and intensity distribution of original scans, and are able to provide more consistent post-processing results by the price of higher re-identification risk and computational complexity. In the current study, we propose a novel method for anonymized face generation for defaced 3D T1-weighted scans based on a 3D conditional generative adversarial network. To evaluate the performance of the proposed de-identification tool, a comparative study was conducted between several existing defacing and refacing tools, with two different segmentation algorithms (FAST and Morphobox). The aim was to evaluate (i) impact on brain morphometry reproducibility, (ii) re-identification risk, (iii) balance between (i) and (ii), and (iv) the processing time. The proposed method takes 9 s for face generation and is suitable for recovering consistent post-processing results after defacing.
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Affiliation(s)
- Nataliia Molchanova
- Department of RadiologyLausanne University Hospital (CHUV)LausanneSwitzerland
- Institute of InformaticsUniversity of Applied Sciences and Arts of Western Switzerland (HES‐SO)SierreSwitzerland
- Faculty of Biology and MedicineUniversity of Lausanne (UNIL)LausanneSwitzerland
- Advanced Clinical Imaging TechnologySiemens Healthineers International AGLausanneSwitzerland
- Laboratory of Signal Processing 5Ecole Polytechnique Fédérale de Lausanne, (EPFL)LausanneSwitzerland
| | - Bénédicte Maréchal
- Department of RadiologyLausanne University Hospital (CHUV)LausanneSwitzerland
- Advanced Clinical Imaging TechnologySiemens Healthineers International AGLausanneSwitzerland
- Laboratory of Signal Processing 5Ecole Polytechnique Fédérale de Lausanne, (EPFL)LausanneSwitzerland
| | - Jean‐Philippe Thiran
- Department of RadiologyLausanne University Hospital (CHUV)LausanneSwitzerland
- Laboratory of Signal Processing 5Ecole Polytechnique Fédérale de Lausanne, (EPFL)LausanneSwitzerland
| | - Tobias Kober
- Department of RadiologyLausanne University Hospital (CHUV)LausanneSwitzerland
- Advanced Clinical Imaging TechnologySiemens Healthineers International AGLausanneSwitzerland
- Laboratory of Signal Processing 5Ecole Polytechnique Fédérale de Lausanne, (EPFL)LausanneSwitzerland
| | - Till Huelnhagen
- Department of RadiologyLausanne University Hospital (CHUV)LausanneSwitzerland
- Advanced Clinical Imaging TechnologySiemens Healthineers International AGLausanneSwitzerland
- Laboratory of Signal Processing 5Ecole Polytechnique Fédérale de Lausanne, (EPFL)LausanneSwitzerland
| | - Jonas Richiardi
- Department of RadiologyLausanne University Hospital (CHUV)LausanneSwitzerland
- Faculty of Biology and MedicineUniversity of Lausanne (UNIL)LausanneSwitzerland
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4
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Gao C, Landman BA, Prince JL, Carass A. Reproducibility evaluation of the effects of MRI defacing on brain segmentation. J Med Imaging (Bellingham) 2023; 10:064001. [PMID: 38074632 PMCID: PMC10704191 DOI: 10.1117/1.jmi.10.6.064001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 09/22/2023] [Accepted: 10/24/2023] [Indexed: 12/20/2023] Open
Abstract
Purpose Recent advances in magnetic resonance (MR) scanner quality and the rapidly improving nature of facial recognition software have necessitated the introduction of MR defacing algorithms to protect patient privacy. As a result, there are a number of MR defacing algorithms available to the neuroimaging community, with several appearing in just the last 5 years. While some qualities of these defacing algorithms, such as patient identifiability, have been explored in the previous works, the potential impact of defacing on neuroimage processing has yet to be explored. Approach We qualitatively evaluate eight MR defacing algorithms on 179 subjects from the OASIS-3 cohort and 21 subjects from the Kirby-21 dataset. We also evaluate the effects of defacing on two neuroimaging pipelines-SLANT and FreeSurfer-by comparing the segmentation consistency between the original and defaced images. Results Defacing can alter brain segmentation and even lead to catastrophic failures, which are more frequent with some algorithms, such as Quickshear, MRI_Deface, and FSL_deface. Compared to FreeSurfer, SLANT is less affected by defacing. On outputs that pass the quality check, the effects of defacing are less pronounced than those of rescanning, as measured by the Dice similarity coefficient. Conclusions The effects of defacing are noticeable and should not be disregarded. Extra attention, in particular, should be paid to the possibility of catastrophic failures. It is crucial to adopt a robust defacing algorithm and perform a thorough quality check before releasing defaced datasets. To improve the reliability of analysis in scenarios involving defaced MRIs, it is encouraged to include multiple brain segmentation pipelines.
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Affiliation(s)
- Chenyu Gao
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
| | - Bennett A. Landman
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
| | - Jerry L. Prince
- The Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, Maryland, United States
| | - Aaron Carass
- The Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, Maryland, United States
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5
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Schwarz CG, Kremers WK, Weigand SD, Prakaashana CM, Senjem ML, Przybelski SA, Lowe VJ, Gunter JL, Kantarci K, Vemuri P, Graff-Radford J, Petersen RC, Knopman DS, Jack CR. Effects of de-facing software mri_reface on utility of imaging biomarkers used in Alzheimer's disease research. Neuroimage Clin 2023; 40:103507. [PMID: 37703605 PMCID: PMC10502400 DOI: 10.1016/j.nicl.2023.103507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 08/07/2023] [Accepted: 09/05/2023] [Indexed: 09/15/2023]
Abstract
Brain imaging research studies increasingly use "de-facing" software to remove or replace facial imagery before public data sharing. Several works have studied the effects of de-facing software on brain imaging biomarkers by directly comparing automated measurements from unmodified vs de-faced images, but most research brain images are used in analyses of correlations with cognitive measurements or clinical statuses, and the effects of de-facing on these types of imaging-to-cognition correlations has not been measured. In this work, we focused on brain imaging measures of amyloid (A), tau (T), neurodegeneration (N), and vascular (V) measures used in Alzheimer's Disease (AD) research. We created a retrospective sample of participants from three age- and sex-matched clinical groups (cognitively unimpaired, mild cognitive impairment, and AD dementia, and we performed region- and voxel-wise analyses of: hippocampal volume (N), white matter hyperintensity volume (V), amyloid PET (A), and tau PET (T) measures, each from multiple software pipelines, on their ability to separate cognitively defined groups and their degrees of correlation with age and Clinical Dementia Rating (CDR)-Sum of Boxes (CDR-SB). We performed each of these analyses twice: once with unmodified images and once with images de-faced with leading de-facing software mri_reface, and we directly compared the findings and their statistical strengths between the original vs. the de-faced images. Analyses with original and with de-faced images had very high agreement. There were no significant differences between any voxel-wise comparisons. Among region-wise comparisons, only three out of 55 correlations were significantly different between original and de-faced images, and these were not significant after correction for multiple comparisons. Overall, the statistical power of the imaging data for AD biomarkers was almost identical between unmodified and de-faced images, and their analyses results were extremely consistent.
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Affiliation(s)
| | - Walter K Kremers
- Department of Quantitative Health Sciences, Division of Clinical Trials & Biostatistics, Mayo Clinic, Rochester, MN, USA
| | - Stephen D Weigand
- Department of Quantitative Health Sciences, Division of Clinical Trials & Biostatistics, Mayo Clinic, Rochester, MN, USA
| | | | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, Rochester, MN, USA; Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Scott A Przybelski
- Department of Quantitative Health Sciences, Division of Clinical Trials & Biostatistics, Mayo Clinic, Rochester, MN, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
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6
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Schwarz CG, Kremers WK, Arani A, Savvides M, Reid RI, Gunter JL, Senjem ML, Cogswell PM, Vemuri P, Kantarci K, Knopman DS, Petersen RC, Jack CR. A face-off of MRI research sequences by their need for de-facing. Neuroimage 2023; 276:120199. [PMID: 37269958 PMCID: PMC10389782 DOI: 10.1016/j.neuroimage.2023.120199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/19/2023] [Accepted: 05/25/2023] [Indexed: 06/05/2023] Open
Abstract
It is now widely known that research brain MRI, CT, and PET images may potentially be re-identified using face recognition, and this potential can be reduced by applying face-deidentification ("de-facing") software. However, for research MRI sequences beyond T1-weighted (T1-w) and T2-FLAIR structural images, the potential for re-identification and quantitative effects of de-facing are both unknown, and the effects of de-facing T2-FLAIR are also unknown. In this work we examine these questions (where applicable) for T1-w, T2-w, T2*-w, T2-FLAIR, diffusion MRI (dMRI), functional MRI (fMRI), and arterial spin labelling (ASL) sequences. Among current-generation, vendor-product research-grade sequences, we found that 3D T1-w, T2-w, and T2-FLAIR were highly re-identifiable (96-98%). 2D T2-FLAIR and 3D multi-echo GRE (ME-GRE) were also moderately re-identifiable (44-45%), and our derived T2* from ME-GRE (comparable to a typical 2D T2*) matched at only 10%. Finally, diffusion, functional and ASL images were each minimally re-identifiable (0-8%). Applying de-facing with mri_reface version 0.3 reduced successful re-identification to ≤8%, while differential effects on popular quantitative pipelines for cortical volumes and thickness, white matter hyperintensities (WMH), and quantitative susceptibility mapping (QSM) measurements were all either comparable with or smaller than scan-rescan estimates. Consequently, high-quality de-facing software can greatly reduce the risk of re-identification for identifiable MRI sequences with only negligible effects on automated intracranial measurements. The current-generation echo-planar and spiral sequences (dMRI, fMRI, and ASL) each had minimal match rates, suggesting that they have a low risk of re-identification and can be shared without de-facing, but this conclusion should be re-evaluated if they are acquired without fat suppression, with a full-face scan coverage, or if newer developments reduce the current levels of artifacts and distortion around the face.
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Affiliation(s)
- Christopher G Schwarz
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
| | - Walter K Kremers
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Arvin Arani
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Marios Savvides
- CyLab Biometrics Center and Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Robert I Reid
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Jeffrey L Gunter
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Matthew L Senjem
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Petrice M Cogswell
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | | | | | - Clifford R Jack
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
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7
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Gao C, Landman BA, Prince JL, Carass A. A reproducibility evaluation of the effects of MRI defacing on brain segmentation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.15.23289995. [PMID: 37293070 PMCID: PMC10246049 DOI: 10.1101/2023.05.15.23289995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Purpose Recent advances in magnetic resonance (MR) scanner quality and the rapidly improving nature of facial recognition software have necessitated the introduction of MR defacing algorithms to protect patient privacy. As a result, there are a number of MR defacing algorithms available to the neuroimaging community, with several appearing in just the last five years. While some qualities of these defacing algorithms, such as patient identifiability, have been explored in previous works, the potential impact of defacing on neuroimage processing has yet to be explored. Approach We qualitatively evaluate eight MR defacing algorithms on 179 subjects from the OASIS-3 cohort and the 21 subjects from the Kirby-21 dataset. We also evaluate the effects of defacing on two neuroimaging pipelines-SLANT and FreeSurfer-by comparing the segmentation consistency between the original and defaced images. Results Defacing can alter brain segmentation and even lead to catastrophic failures, which are more frequent with some algorithms such as Quickshear, MRI_Deface, and FSL_deface. Compared to FreeSurfer, SLANT is less affected by defacing. On outputs that pass the quality check, the effects of defacing are less pronounced than those of rescanning, as measured by the Dice similarity coefficient. Conclusions The effects of defacing are noticeable and should not be disregarded. Extra attention, in particular, should be paid to the possibility of catastrophic failures. It is crucial to adopt a robust defacing algorithm and perform a thorough quality check before releasing defaced datasets. To improve the reliability of analysis in scenarios involving defaced MRIs, it's encouraged to include multiple brain segmentation pipelines.
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Affiliation(s)
- Chenyu Gao
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, 37235
| | - Bennett A. Landman
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, 37235
| | - Jerry L. Prince
- The Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, Maryland, 21218
| | - Aaron Carass
- The Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, Maryland, 21218
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Clunie DA, Flanders A, Taylor A, Erickson B, Bialecki B, Brundage D, Gutman D, Prior F, Seibert JA, Perry J, Gichoya JW, Kirby J, Andriole K, Geneslaw L, Moore S, Fitzgerald TJ, Tellis W, Xiao Y, Farahani K, Luo J, Rosenthal A, Kandarpa K, Rosen R, Goetz K, Babcock D, Xu B, Hsiao J. Report of the Medical Image De-Identification (MIDI) Task Group - Best Practices and Recommendations. ARXIV 2023:arXiv:2303.10473v2. [PMID: 37033463 PMCID: PMC10081345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Affiliation(s)
| | | | | | | | | | | | | | - Fred Prior
- University of Arkansas for Medical Sciences
| | | | | | | | - Justin Kirby
- Frederick National Laboratory for Cancer Research
| | | | | | | | | | | | - Ying Xiao
- University of Pennsylvania Health System
| | | | - James Luo
- National Heart, Lung, and Blood Institute (NHLBI)
| | - Alex Rosenthal
- National Institute of Allergy and Infectious Diseases (NIAID)
| | - Kris Kandarpa
- National Institute of Biomedical Imaging and Bioengineering (NIBIB)
| | - Rebecca Rosen
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
| | | | - Debra Babcock
- National Institute of Neurological Disorders and Stroke (NINDS)
| | - Ben Xu
- National Institute on Alcohol Abuse and Alcoholism (NIAAA)
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9
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Sahlsten J, Wahid KA, Glerean E, Jaskari J, Naser MA, He R, Kann BH, Mäkitie A, Fuller CD, Kaski K. Segmentation stability of human head and neck cancer medical images for radiotherapy applications under de-identification conditions: Benchmarking data sharing and artificial intelligence use-cases. Front Oncol 2023; 13:1120392. [PMID: 36925936 PMCID: PMC10011442 DOI: 10.3389/fonc.2023.1120392] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 02/13/2023] [Indexed: 03/08/2023] Open
Abstract
Background Demand for head and neck cancer (HNC) radiotherapy data in algorithmic development has prompted increased image dataset sharing. Medical images must comply with data protection requirements so that re-use is enabled without disclosing patient identifiers. Defacing, i.e., the removal of facial features from images, is often considered a reasonable compromise between data protection and re-usability for neuroimaging data. While defacing tools have been developed by the neuroimaging community, their acceptability for radiotherapy applications have not been explored. Therefore, this study systematically investigated the impact of available defacing algorithms on HNC organs at risk (OARs). Methods A publicly available dataset of magnetic resonance imaging scans for 55 HNC patients with eight segmented OARs (bilateral submandibular glands, parotid glands, level II neck lymph nodes, level III neck lymph nodes) was utilized. Eight publicly available defacing algorithms were investigated: afni_refacer, DeepDefacer, defacer, fsl_deface, mask_face, mri_deface, pydeface, and quickshear. Using a subset of scans where defacing succeeded (N=29), a 5-fold cross-validation 3D U-net based OAR auto-segmentation model was utilized to perform two main experiments: 1.) comparing original and defaced data for training when evaluated on original data; 2.) using original data for training and comparing the model evaluation on original and defaced data. Models were primarily assessed using the Dice similarity coefficient (DSC). Results Most defacing methods were unable to produce any usable images for evaluation, while mask_face, fsl_deface, and pydeface were unable to remove the face for 29%, 18%, and 24% of subjects, respectively. When using the original data for evaluation, the composite OAR DSC was statistically higher (p ≤ 0.05) for the model trained with the original data with a DSC of 0.760 compared to the mask_face, fsl_deface, and pydeface models with DSCs of 0.742, 0.736, and 0.449, respectively. Moreover, the model trained with original data had decreased performance (p ≤ 0.05) when evaluated on the defaced data with DSCs of 0.673, 0.693, and 0.406 for mask_face, fsl_deface, and pydeface, respectively. Conclusion Defacing algorithms may have a significant impact on HNC OAR auto-segmentation model training and testing. This work highlights the need for further development of HNC-specific image anonymization methods.
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Affiliation(s)
- Jaakko Sahlsten
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Kareem A. Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Enrico Glerean
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Joel Jaskari
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Mohamed A. Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Renjie He
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Benjamin H. Kann
- Artificial Intelligence in Medicine Program, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Antti Mäkitie
- Department of Otorhinolaryngology, Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- *Correspondence: Clifton D. Fuller, ; Kimmo Kaski,
| | - Kimmo Kaski
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
- *Correspondence: Clifton D. Fuller, ; Kimmo Kaski,
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10
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Bruña R, Vaghari D, Greve A, Cooper E, Mada MO, Henson RN. Modified MRI Anonymization (De-Facing) for Improved MEG Coregistration. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9100591. [PMID: 36290559 PMCID: PMC9598466 DOI: 10.3390/bioengineering9100591] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 10/02/2022] [Accepted: 10/17/2022] [Indexed: 01/28/2023]
Abstract
Localising the sources of MEG/EEG signals often requires a structural MRI to create a head model, while ensuring reproducible scientific results requires sharing data and code. However, sharing structural MRI data often requires the face go be hidden to help protect the identity of the individuals concerned. While automated de-facing methods exist, they tend to remove the whole face, which can impair methods for coregistering the MRI data with the EEG/MEG data. We show that a new, automated de-facing method that retains the nose maintains good MRI-MEG/EEG coregistration. Importantly, behavioural data show that this "face-trimming" method does not increase levels of identification relative to a standard de-facing approach and has less effect on the automated segmentation and surface extraction sometimes used to create head models for MEG/EEG localisation. We suggest that this trimming approach could be employed for future sharing of structural MRI data, at least for those to be used in forward modelling (source reconstruction) of EEG/MEG data.
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Affiliation(s)
- Ricardo Bruña
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, 28040 Madrid, Spain
- Department of Radiology, Rehabilitation and Physical Therapy, Universidad Complutense de Madrid, IdISSC, 28040 Madrid, Spain
- Correspondence:
| | - Delshad Vaghari
- Department of Electrical & Computer Engineering, Tarbiat Modares University, Tehran P.O. Box 14115-111, Iran
| | - Andrea Greve
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Elisa Cooper
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Marius O. Mada
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Richard N. Henson
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
- Department of Psychiatry, University of Cambridge, Cambridge CB2 OSZ, UK
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11
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Mercier MR, Dubarry AS, Tadel F, Avanzini P, Axmacher N, Cellier D, Vecchio MD, Hamilton LS, Hermes D, Kahana MJ, Knight RT, Llorens A, Megevand P, Melloni L, Miller KJ, Piai V, Puce A, Ramsey NF, Schwiedrzik CM, Smith SE, Stolk A, Swann NC, Vansteensel MJ, Voytek B, Wang L, Lachaux JP, Oostenveld R. Advances in human intracranial electroencephalography research, guidelines and good practices. Neuroimage 2022; 260:119438. [PMID: 35792291 DOI: 10.1016/j.neuroimage.2022.119438] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/23/2022] [Accepted: 06/30/2022] [Indexed: 12/11/2022] Open
Abstract
Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.
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12
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Schwarz CG, Kremers WK, Lowe VJ, Savvides M, Gunter JL, Senjem ML, Vemuri P, Kantarci K, Knopman DS, Petersen RC, Jack CR. Face recognition from research brain PET: An unexpected PET problem. Neuroimage 2022; 258:119357. [PMID: 35660089 PMCID: PMC9358410 DOI: 10.1016/j.neuroimage.2022.119357] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/31/2022] [Accepted: 06/02/2022] [Indexed: 11/04/2022] Open
Abstract
It is well known that de-identified research brain images from MRI and CT can potentially be re-identified using face recognition; however, this has not been examined for PET images. We generated face reconstruction images of 182 volunteers using amyloid, tau, and FDG PET scans, and we measured how accurately commercial face recognition software (Microsoft Azure’s Face API) automatically matched them with the individual participants’ face photographs. We then compared this accuracy with the same experiments using participants’ CT and MRI. Face reconstructions from PET images from PET/CT scanners were correctly matched at rates of 42% (FDG), 35% (tau), and 32% (amyloid), while CT were matched at 78% and MRI at 97–98%. We propose that these recognition rates are high enough that research studies should consider using face de-identification (“de-facing”) software on PET images, in addition to CT and structural MRI, before data sharing. We also updated our mri_reface de-identification software with extended functionality to replace face imagery in PET and CT images. Rates of face recognition on de-faced images were reduced to 0–4% for PET, 5% for CT, and 8% for MRI. We measured the effects of de-facing on regional amyloid PET measurements from two different measurement pipelines (PETSurfer/FreeSurfer 6.0, and one in-house method based on SPM12 and ANTs), and these effects were small: ICC values between de-faced and original images were > 0.98, biases were <2%, and median relative errors were <2%. Effects on global amyloid PET SUVR measurements were even smaller: ICC values were 1.00, biases were <0.5%, and median relative errors were also <0.5%.
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Affiliation(s)
| | - Walter K Kremers
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Marios Savvides
- CyLab Biometrics Center and Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | | | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, Rochester, MN, USA; Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
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13
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De Stefano N, Battaglini M, Pareto D, Cortese R, Zhang J, Oesingmann N, Prados F, Rocca MA, Valsasina P, Vrenken H, Gandini Wheeler-Kingshott CAM, Filippi M, Barkhof F, Rovira À. MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies. Neuroimage Clin 2022; 34:102972. [PMID: 35245791 PMCID: PMC8892169 DOI: 10.1016/j.nicl.2022.102972] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 02/22/2022] [Accepted: 02/23/2022] [Indexed: 11/24/2022]
Abstract
Sharing data from cooperative studies is essential to develop new biomarkers in MS. Differences in MRI acquisition, analysis, storage represent a substantial constraint. We review the state of the art and developments in the harmonization of MRI. We provide recommendations to harmonize large MRI datasets in the MS field.
There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resources.
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Affiliation(s)
- Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy.
| | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Rosa Cortese
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Jian Zhang
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | | | - Ferran Prados
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, UCL, London, WC1V 6LJ, United Kingdom; e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Hugo Vrenken
- Amsterdam Neuroscience, MS Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Brain MRI 3T Research Center, C. Mondino National Neurological Institute, Pavia, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy; Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Frederik Barkhof
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, UCL, London, WC1V 6LJ, United Kingdom; Amsterdam Neuroscience, MS Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
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14
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Rubbert C, Wolf L, Turowski B, Hedderich DM, Gaser C, Dahnke R, Caspers J. Impact of defacing on automated brain atrophy estimation. Insights Imaging 2022; 13:54. [PMID: 35348936 PMCID: PMC8964867 DOI: 10.1186/s13244-022-01195-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 02/19/2022] [Indexed: 11/10/2022] Open
Abstract
Background Defacing has become mandatory for anonymization of brain MRI scans; however, concerns regarding data integrity were raised. Thus, we systematically evaluated the effect of different defacing procedures on automated brain atrophy estimation. Methods In total, 268 Alzheimer’s disease patients were included from ADNI, which included unaccelerated (n = 154), within-session unaccelerated repeat (n = 67) and accelerated 3D T1 imaging (n = 114). Atrophy maps were computed using the open-source software veganbagel for every original, unmodified scan and after defacing using afni_refacer, fsl_deface, mri_deface, mri_reface, PyDeface or spm_deface, and the root-mean-square error (RMSE) between z-scores was calculated. RMSE values derived from unaccelerated and unaccelerated repeat imaging served as a benchmark. Outliers were defined as RMSE > 75th percentile and by using Grubbs’s test. Results Benchmark RMSE was 0.28 ± 0.1 (range 0.12–0.58, 75th percentile 0.33). Outliers were found for unaccelerated and accelerated T1 imaging using the 75th percentile cutoff: afni_refacer (unaccelerated: 18, accelerated: 16), fsl_deface (unaccelerated: 4, accelerated: 18), mri_deface (unaccelerated: 0, accelerated: 15), mri_reface (unaccelerated: 0, accelerated: 2) and spm_deface (unaccelerated: 0, accelerated: 7). PyDeface performed best with no outliers (unaccelerated mean RMSE 0.08 ± 0.05, accelerated mean RMSE 0.07 ± 0.05). The following outliers were found according to Grubbs’s test: afni_refacer (unaccelerated: 16, accelerated: 13), fsl_deface (unaccelerated: 10, accelerated: 21), mri_deface (unaccelerated: 7, accelerated: 20), mri_reface (unaccelerated: 7, accelerated: 6), PyDeface (unaccelerated: 5, accelerated: 8) and spm_deface (unaccelerated: 10, accelerated: 12). Conclusion Most defacing approaches have an impact on atrophy estimation, especially in accelerated 3D T1 imaging. Only PyDeface showed good results with negligible impact on atrophy estimation.
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15
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Artificial Intelligence in Medicine and Privacy Preservation. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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16
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White T, Blok E, Calhoun VD. Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed. Hum Brain Mapp 2022; 43:278-291. [PMID: 32621651 PMCID: PMC8675413 DOI: 10.1002/hbm.25120] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 05/12/2020] [Accepted: 06/22/2020] [Indexed: 12/19/2022] Open
Abstract
Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. These initiatives offer greater transparency in science, with the opportunity for external research groups to reproduce, replicate, and extend research findings. Further, larger datasets offer the opportunity to identify homogeneous patterns within subgroups of individuals, where these patterns may be obscured by the heterogeneity of the neurobiological measure in smaller samples. However, data sharing and data pooling initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. Interestingly, what is key to some of these new laws (i.e, the European Union's general data protection regulation) is that they provide greater control of data to those who "give" their data for research purposes. Thus, the most important element in data sharing is allowing the participants to make informed decisions about how they want their data to be used, and, within the law of the specific country, to follow the participants' wishes. This framework encompasses obtaining thorough informed consent and allowing the participant to determine the extent that they want their data shared, many of the ethical and legal obstacles are reduced to just monsters under the bed. In this manuscript we discuss the many options and obstacles for data sharing, from fully open, to federated learning, to fully closed. Importantly, we highlight the intersection of data sharing, privacy, and data ownership and highlight specific examples that we believe are informative to the neuroimaging community.
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Affiliation(s)
- Tonya White
- Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical CenterRotterdamThe Netherlands
- Department of RadiologyErasmus University Medical CenterRotterdamThe Netherlands
| | - Elisabet Blok
- Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical CenterRotterdamThe Netherlands
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
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17
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Zugman A, Harrewijn A, Cardinale EM, Zwiebel H, Freitag GF, Werwath KE, Bas‐Hoogendam JM, Groenewold NA, Aghajani M, Hilbert K, Cardoner N, Porta‐Casteràs D, Gosnell S, Salas R, Blair KS, Blair JR, Hammoud MZ, Milad M, Burkhouse K, Phan KL, Schroeder HK, Strawn JR, Beesdo‐Baum K, Thomopoulos SI, Grabe HJ, Van der Auwera S, Wittfeld K, Nielsen JA, Buckner R, Smoller JW, Mwangi B, Soares JC, Wu M, Zunta‐Soares GB, Jackowski AP, Pan PM, Salum GA, Assaf M, Diefenbach GJ, Brambilla P, Maggioni E, Hofmann D, Straube T, Andreescu C, Berta R, Tamburo E, Price R, Manfro GG, Critchley HD, Makovac E, Mancini M, Meeten F, Ottaviani C, Agosta F, Canu E, Cividini C, Filippi M, Kostić M, Munjiza A, Filippi CA, Leibenluft E, Alberton BAV, Balderston NL, Ernst M, Grillon C, Mujica‐Parodi LR, van Nieuwenhuizen H, Fonzo GA, Paulus MP, Stein MB, Gur RE, Gur RC, Kaczkurkin AN, Larsen B, Satterthwaite TD, Harper J, Myers M, Perino MT, Yu Q, Sylvester CM, Veltman DJ, Lueken U, Van der Wee NJA, Stein DJ, Jahanshad N, Thompson PM, Pine DS, Winkler AM. Mega-analysis methods in ENIGMA: The experience of the generalized anxiety disorder working group. Hum Brain Mapp 2022; 43:255-277. [PMID: 32596977 PMCID: PMC8675407 DOI: 10.1002/hbm.25096] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 05/26/2020] [Accepted: 05/31/2020] [Indexed: 12/15/2022] Open
Abstract
The ENIGMA group on Generalized Anxiety Disorder (ENIGMA-Anxiety/GAD) is part of a broader effort to investigate anxiety disorders using imaging and genetic data across multiple sites worldwide. The group is actively conducting a mega-analysis of a large number of brain structural scans. In this process, the group was confronted with many methodological challenges related to study planning and implementation, between-country transfer of subject-level data, quality control of a considerable amount of imaging data, and choices related to statistical methods and efficient use of resources. This report summarizes the background information and rationale for the various methodological decisions, as well as the approach taken to implement them. The goal is to document the approach and help guide other research groups working with large brain imaging data sets as they develop their own analytic pipelines for mega-analyses.
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Affiliation(s)
- André Zugman
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Anita Harrewijn
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Elise M. Cardinale
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Hannah Zwiebel
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Gabrielle F. Freitag
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Katy E. Werwath
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Janna M. Bas‐Hoogendam
- Leiden University Medical Center, Department of PsychiatryLeidenThe Netherlands
- Leiden Institute for Brain and Cognition (LIBC)LeidenThe Netherlands
- Leiden University, Institute of Psychology, Developmental and Educational PsychologyLeidenThe Netherlands
| | - Nynke A. Groenewold
- Department of Psychiatry & Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | - Moji Aghajani
- Department. of PsychiatryAmsterdam UMC/VUMCAmsterdamThe Netherlands
- GGZ InGeestDepartment of Research & InnovationAmsterdamThe Netherlands
| | - Kevin Hilbert
- Department of PsychologyHumboldt‐Universität zu BerlinBerlinGermany
| | - Narcis Cardoner
- Department of Mental HealthUniversity Hospital Parc Taulí‐I3PTBarcelonaSpain
- Department of Psychiatry and Forensic MedicineUniversitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red de Salud MentalCarlos III Health InstituteMadridSpain
| | - Daniel Porta‐Casteràs
- Department of Mental HealthUniversity Hospital Parc Taulí‐I3PTBarcelonaSpain
- Department of Psychiatry and Forensic MedicineUniversitat Autònoma de BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red de Salud MentalCarlos III Health InstituteMadridSpain
| | - Savannah Gosnell
- Menninger Department of Psychiatry and Behavioral SciencesBaylor College of MedicineHoustonTexasUSA
| | - Ramiro Salas
- Menninger Department of Psychiatry and Behavioral SciencesBaylor College of MedicineHoustonTexasUSA
| | - Karina S. Blair
- Center for Neurobehavioral ResearchBoys Town National Research HospitalBoys TownNebraskaUSA
| | - James R. Blair
- Center for Neurobehavioral ResearchBoys Town National Research HospitalBoys TownNebraskaUSA
| | - Mira Z. Hammoud
- Department of PsychiatryNew York UniversityNew YorkNew YorkUSA
| | - Mohammed Milad
- Department of PsychiatryNew York UniversityNew YorkNew YorkUSA
| | - Katie Burkhouse
- Department of PsychiatryUniversity of Illinois at ChicagoChicagoIllinoisUSA
| | - K. Luan Phan
- Department of Psychiatry and Behavioral HealthThe Ohio State UniversityColumbusOhioUSA
| | - Heidi K. Schroeder
- Department of Psychiatry & Behavioral NeuroscienceUniversity of CincinnatiCincinnatiOhioUSA
| | - Jeffrey R. Strawn
- Department of Psychiatry & Behavioral NeuroscienceUniversity of CincinnatiCincinnatiOhioUSA
| | - Katja Beesdo‐Baum
- Behavioral EpidemiologyInstitute of Clinical Psychology and Psychotherapy, Technische Universität DresdenDresdenGermany
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Hans J. Grabe
- Department of Psychiatry and PsychotherapyUniversity Medicine GreifswaldGreifswaldGermany
- German Center for Neurodegenerative Diseases (DZNE)Site Rostock/GreifswaldGreifswaldGermany
| | - Sandra Van der Auwera
- Department of Psychiatry and PsychotherapyUniversity Medicine GreifswaldGreifswaldGermany
- German Center for Neurodegenerative Diseases (DZNE)Site Rostock/GreifswaldGreifswaldGermany
| | - Katharina Wittfeld
- Department of Psychiatry and PsychotherapyUniversity Medicine GreifswaldGreifswaldGermany
- German Center for Neurodegenerative Diseases (DZNE)Site Rostock/GreifswaldGreifswaldGermany
| | - Jared A. Nielsen
- Department of PsychologyHarvard UniversityCambridgeMassachusettsUSA
- Center for Brain ScienceHarvard UniversityCambridgeMassachusettsUSA
| | - Randy Buckner
- Department of PsychologyHarvard UniversityCambridgeMassachusettsUSA
- Center for Brain ScienceHarvard UniversityCambridgeMassachusettsUSA
- Department of PsychiatryMassachusetts General HospitalBostonMassachusettsUSA
| | - Jordan W. Smoller
- Department of PsychiatryMassachusetts General HospitalBostonMassachusettsUSA
| | - Benson Mwangi
- Center Of Excellence On Mood Disorders, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Jair C. Soares
- Center Of Excellence On Mood Disorders, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Mon‐Ju Wu
- Center Of Excellence On Mood Disorders, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Giovana B. Zunta‐Soares
- Center Of Excellence On Mood Disorders, Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Andrea P. Jackowski
- LiNC, Department of PsychiatryFederal University of São PauloSão PauloSão PauloBrazil
| | - Pedro M. Pan
- LiNC, Department of PsychiatryFederal University of São PauloSão PauloSão PauloBrazil
| | - Giovanni A. Salum
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do SulPorto AlegreRio Grande do SulBrazil
| | - Michal Assaf
- Olin Neuropsychiatry Research CenterInstitute of Living, Hartford HospitalHartfordConnecticutUSA
- Department of PsychiatryYale School of MedicineNew HavenConnecticutUSA
| | - Gretchen J. Diefenbach
- Anxiety Disorders CenterInstitute of Living, Hartford HospitalHartfordConnecticutUSA
- Yale School of MedicineNew HavenConnecticutUSA
| | - Paolo Brambilla
- Department of Neurosciences and Mental HealthFondazione IRCCS Ca' Granda Ospedale Maggiore PoliclinicoMilanItaly
| | - Eleonora Maggioni
- Department of Neurosciences and Mental HealthFondazione IRCCS Ca' Granda Ospedale Maggiore PoliclinicoMilanItaly
| | - David Hofmann
- Institute of Medical Psychology and Systems Neuroscience, University of MuensterMuensterGermany
| | - Thomas Straube
- Institute of Medical Psychology and Systems Neuroscience, University of MuensterMuensterGermany
| | - Carmen Andreescu
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Rachel Berta
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Erica Tamburo
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Rebecca Price
- Department of Psychiatry & PsychologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Gisele G. Manfro
- Anxiety Disorder ProgramHospital de Clínicas de Porto AlegrePorto AlegreRio Grande do SulBrazil
- Department of PsychiatryFederal University of Rio Grande do SulPorto AlegreRio Grande do SulBrazil
| | - Hugo D. Critchley
- Department of NeuroscienceBrighton and Sussex Medical School, University of SussexBrightonUK
| | - Elena Makovac
- Centre for Neuroimaging ScienceKings College LondonLondonUK
| | - Matteo Mancini
- Department of NeuroscienceBrighton and Sussex Medical School, University of SussexBrightonUK
| | | | | | - Federica Agosta
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
- Vita‐Salute San Raffaele UniversityMilanItaly
| | - Elisa Canu
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Camilla Cividini
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
- Vita‐Salute San Raffaele UniversityMilanItaly
- Neurology and Neurophysiology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Milutin Kostić
- Institute of Mental Health, University of BelgradeBelgradeSerbia
- Department of Psychiatry, School of MedicineUniversity of BelgradeBelgradeSerbia
| | - Ana Munjiza
- Institute of Mental Health, University of BelgradeBelgradeSerbia
| | - Courtney A. Filippi
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Ellen Leibenluft
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Bianca A. V. Alberton
- Graduate Program in Electrical and Computer Engineering, Universidade Tecnológica Federal do ParanáCuritibaPuerto RicoBrazil
| | - Nicholas L. Balderston
- Center for Neuromodulation in Depression and StressUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Monique Ernst
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Christian Grillon
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | | | | | - Gregory A. Fonzo
- Department of PsychiatryThe University of Texas at Austin Dell Medical SchoolAustinTexasUSA
| | | | - Murray B. Stein
- Department of Psychiatry & Family Medicine and Public HealthUniversity of CaliforniaLa JollaCaliforniaUSA
| | - Raquel E. Gur
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ruben C. Gur
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Bart Larsen
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Jennifer Harper
- Department of PsychiatryWashington UniversitySt. LouisMissouriUSA
| | - Michael Myers
- Department of PsychiatryWashington UniversitySt. LouisMissouriUSA
| | | | - Qiongru Yu
- Department of PsychiatryWashington UniversitySt. LouisMissouriUSA
| | | | - Dick J. Veltman
- Department. of PsychiatryAmsterdam UMC/VUMCAmsterdamThe Netherlands
| | - Ulrike Lueken
- Department of PsychologyHumboldt‐Universität zu BerlinBerlinGermany
| | - Nic J. A. Van der Wee
- Leiden University Medical Center, Department of PsychiatryLeidenThe Netherlands
- Leiden Institute for Brain and Cognition (LIBC)LeidenThe Netherlands
| | - Dan J. Stein
- Department of Psychiatry & Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- SAMRC Unite on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Daniel S. Pine
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
| | - Anderson M. Winkler
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH)BethesdaMarylandUSA
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18
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Mikulan E, Russo S, Zauli FM, d'Orio P, Parmigiani S, Favaro J, Knight W, Squarza S, Perri P, Cardinale F, Avanzini P, Pigorini A. A comparative study between state-of-the-art MRI deidentification and AnonyMI, a new method combining re-identification risk reduction and geometrical preservation. Hum Brain Mapp 2021; 42:5523-5534. [PMID: 34520074 PMCID: PMC8559469 DOI: 10.1002/hbm.25639] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 08/11/2021] [Accepted: 08/12/2021] [Indexed: 01/03/2023] Open
Abstract
Deidentifying MRIs constitutes an imperative challenge, as it aims at precluding the possibility of re-identification of a research subject or patient, but at the same time it should preserve as much geometrical information as possible, in order to maximize data reusability and to facilitate interoperability. Although several deidentification methods exist, no comprehensive and comparative evaluation of deidentification performance has been carried out across them. Moreover, the possible ways these methods can compromise subsequent analysis has not been exhaustively tested. To tackle these issues, we developed AnonyMI, a novel MRI deidentification method, implemented as a user-friendly 3D Slicer plugin-in, which aims at providing a balance between identity protection and geometrical preservation. To test these features, we performed two series of analyses on which we compared AnonyMI to other two state-of-the-art methods, to evaluate, at the same time, how efficient they are at deidentifying MRIs and how much they affect subsequent analyses, with particular emphasis on source localization procedures. Our results show that all three methods significantly reduce the re-identification risk but AnonyMI provides the best geometrical conservation. Notably, it also offers several technical advantages such as a user-friendly interface, multiple input-output capabilities, the possibility of being tailored to specific needs, batch processing and efficient visualization for quality assurance.
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Affiliation(s)
- Ezequiel Mikulan
- Dipartimento di Scienze Biomediche e Cliniche "L.Sacco", Università degli Studi di Milano, Milano, Italy
| | - Simone Russo
- Dipartimento di Scienze Biomediche e Cliniche "L.Sacco", Università degli Studi di Milano, Milano, Italy
| | - Flavia Maria Zauli
- Dipartimento di Scienze Biomediche e Cliniche "L.Sacco", Università degli Studi di Milano, Milano, Italy
| | - Piergiorgio d'Orio
- "Claudio Munari" Epilepsy and Parkinson Surgery Center, ASST Grande Ospedale Metropolitano Niguarda, Milano, Italy
| | - Sara Parmigiani
- Dipartimento di Scienze Biomediche e Cliniche "L.Sacco", Università degli Studi di Milano, Milano, Italy
| | - Jacopo Favaro
- Department of Women's and Children's Health, Pediatric Neurology and Neurophysiology Unit, University of Padua, Padua, Italy
| | - William Knight
- Centre for Computing and Social Responsibility, De Montfort University, Leicester, UK
| | - Silvia Squarza
- Department of Neuroradiology, "Claudio Munari" Epilepsy and Parkinson Surgery Center, ASST Grande Ospedale Metropolitano Niguarda, Milano, Italy
| | - Pierluigi Perri
- Dipartimento di Scienze Giuridiche Cesare Beccaria, Università degli Studi di Milano, Milano, Italy
| | - Francesco Cardinale
- "Claudio Munari" Epilepsy and Parkinson Surgery Center, ASST Grande Ospedale Metropolitano Niguarda, Milano, Italy
| | - Pietro Avanzini
- Istituto di Neuroscienze, Consiglio Nazionale delle Ricerche, Parma, Italy
| | - Andrea Pigorini
- Dipartimento di Scienze Biomediche e Cliniche "L.Sacco", Università degli Studi di Milano, Milano, Italy
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19
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Vrenken H, Jenkinson M, Pham DL, Guttmann CRG, Pareto D, Paardekooper M, de Sitter A, Rocca MA, Wottschel V, Cardoso MJ, Barkhof F. Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence. Neurology 2021; 97:989-999. [PMID: 34607924 PMCID: PMC8610621 DOI: 10.1212/wnl.0000000000012884] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 09/09/2021] [Indexed: 11/15/2022] Open
Abstract
Patients with multiple sclerosis (MS) have heterogeneous clinical presentations, symptoms, and progression over time, making MS difficult to assess and comprehend in vivo. The combination of large-scale data sharing and artificial intelligence creates new opportunities for monitoring and understanding MS using MRI. First, development of validated MS-specific image analysis methods can be boosted by verified reference, test, and benchmark imaging data. Using detailed expert annotations, artificial intelligence algorithms can be trained on such MS-specific data. Second, understanding disease processes could be greatly advanced through shared data of large MS cohorts with clinical, demographic, and treatment information. Relevant patterns in such data that may be imperceptible to a human observer could be detected through artificial intelligence techniques. This applies from image analysis (lesions, atrophy, or functional network changes) to large multidomain datasets (imaging, cognition, clinical disability, genetics). After reviewing data sharing and artificial intelligence, we highlight 3 areas that offer strong opportunities for making advances in the next few years: crowdsourcing, personal data protection, and organized analysis challenges. Difficulties as well as specific recommendations to overcome them are discussed, in order to best leverage data sharing and artificial intelligence to improve image analysis, imaging, and the understanding of MS.
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Affiliation(s)
- Hugo Vrenken
- From the MS Center Amsterdam (H.V., A.d.S., V.W.), Amsterdam Neuroscience, Department of Radiology and Nuclear Medicine, Amsterdam UMC (M.P.), the Netherlands; Wellcome Centre for Integrative Neuroimaging (WIN), FMRIB (M.J.), Nuffield Department of Clinical Neurosciences (NDCN), University of Oxford, UK; Human Imaging and Image Processing Core (D.L.P.), Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation, Bethesda, MD; Center for Neurological Imaging (C.R.G.G.), Department of Radiology, Brigham and Women's Hospital, Boston, MA; Section of Neuroradiology (Department of Radiology) (D.P.), Vall d'Hebron University Hospital and Research Institute (VHIR), Autonomous University Barcelona, Spain; Neuroimaging Research Unit (M.A.R.), Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; AMIGO (M.J.C.), School of Biomedical Engineering and Imaging Sciences, King's College London; and Institutes of Neurology & Healthcare Engineering (F.B.), UCL London, UK.
| | - Mark Jenkinson
- From the MS Center Amsterdam (H.V., A.d.S., V.W.), Amsterdam Neuroscience, Department of Radiology and Nuclear Medicine, Amsterdam UMC (M.P.), the Netherlands; Wellcome Centre for Integrative Neuroimaging (WIN), FMRIB (M.J.), Nuffield Department of Clinical Neurosciences (NDCN), University of Oxford, UK; Human Imaging and Image Processing Core (D.L.P.), Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation, Bethesda, MD; Center for Neurological Imaging (C.R.G.G.), Department of Radiology, Brigham and Women's Hospital, Boston, MA; Section of Neuroradiology (Department of Radiology) (D.P.), Vall d'Hebron University Hospital and Research Institute (VHIR), Autonomous University Barcelona, Spain; Neuroimaging Research Unit (M.A.R.), Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; AMIGO (M.J.C.), School of Biomedical Engineering and Imaging Sciences, King's College London; and Institutes of Neurology & Healthcare Engineering (F.B.), UCL London, UK
| | - Dzung L Pham
- From the MS Center Amsterdam (H.V., A.d.S., V.W.), Amsterdam Neuroscience, Department of Radiology and Nuclear Medicine, Amsterdam UMC (M.P.), the Netherlands; Wellcome Centre for Integrative Neuroimaging (WIN), FMRIB (M.J.), Nuffield Department of Clinical Neurosciences (NDCN), University of Oxford, UK; Human Imaging and Image Processing Core (D.L.P.), Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation, Bethesda, MD; Center for Neurological Imaging (C.R.G.G.), Department of Radiology, Brigham and Women's Hospital, Boston, MA; Section of Neuroradiology (Department of Radiology) (D.P.), Vall d'Hebron University Hospital and Research Institute (VHIR), Autonomous University Barcelona, Spain; Neuroimaging Research Unit (M.A.R.), Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; AMIGO (M.J.C.), School of Biomedical Engineering and Imaging Sciences, King's College London; and Institutes of Neurology & Healthcare Engineering (F.B.), UCL London, UK
| | - Charles R G Guttmann
- From the MS Center Amsterdam (H.V., A.d.S., V.W.), Amsterdam Neuroscience, Department of Radiology and Nuclear Medicine, Amsterdam UMC (M.P.), the Netherlands; Wellcome Centre for Integrative Neuroimaging (WIN), FMRIB (M.J.), Nuffield Department of Clinical Neurosciences (NDCN), University of Oxford, UK; Human Imaging and Image Processing Core (D.L.P.), Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation, Bethesda, MD; Center for Neurological Imaging (C.R.G.G.), Department of Radiology, Brigham and Women's Hospital, Boston, MA; Section of Neuroradiology (Department of Radiology) (D.P.), Vall d'Hebron University Hospital and Research Institute (VHIR), Autonomous University Barcelona, Spain; Neuroimaging Research Unit (M.A.R.), Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; AMIGO (M.J.C.), School of Biomedical Engineering and Imaging Sciences, King's College London; and Institutes of Neurology & Healthcare Engineering (F.B.), UCL London, UK
| | - Deborah Pareto
- From the MS Center Amsterdam (H.V., A.d.S., V.W.), Amsterdam Neuroscience, Department of Radiology and Nuclear Medicine, Amsterdam UMC (M.P.), the Netherlands; Wellcome Centre for Integrative Neuroimaging (WIN), FMRIB (M.J.), Nuffield Department of Clinical Neurosciences (NDCN), University of Oxford, UK; Human Imaging and Image Processing Core (D.L.P.), Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation, Bethesda, MD; Center for Neurological Imaging (C.R.G.G.), Department of Radiology, Brigham and Women's Hospital, Boston, MA; Section of Neuroradiology (Department of Radiology) (D.P.), Vall d'Hebron University Hospital and Research Institute (VHIR), Autonomous University Barcelona, Spain; Neuroimaging Research Unit (M.A.R.), Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; AMIGO (M.J.C.), School of Biomedical Engineering and Imaging Sciences, King's College London; and Institutes of Neurology & Healthcare Engineering (F.B.), UCL London, UK
| | - Michel Paardekooper
- From the MS Center Amsterdam (H.V., A.d.S., V.W.), Amsterdam Neuroscience, Department of Radiology and Nuclear Medicine, Amsterdam UMC (M.P.), the Netherlands; Wellcome Centre for Integrative Neuroimaging (WIN), FMRIB (M.J.), Nuffield Department of Clinical Neurosciences (NDCN), University of Oxford, UK; Human Imaging and Image Processing Core (D.L.P.), Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation, Bethesda, MD; Center for Neurological Imaging (C.R.G.G.), Department of Radiology, Brigham and Women's Hospital, Boston, MA; Section of Neuroradiology (Department of Radiology) (D.P.), Vall d'Hebron University Hospital and Research Institute (VHIR), Autonomous University Barcelona, Spain; Neuroimaging Research Unit (M.A.R.), Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; AMIGO (M.J.C.), School of Biomedical Engineering and Imaging Sciences, King's College London; and Institutes of Neurology & Healthcare Engineering (F.B.), UCL London, UK
| | - Alexandra de Sitter
- From the MS Center Amsterdam (H.V., A.d.S., V.W.), Amsterdam Neuroscience, Department of Radiology and Nuclear Medicine, Amsterdam UMC (M.P.), the Netherlands; Wellcome Centre for Integrative Neuroimaging (WIN), FMRIB (M.J.), Nuffield Department of Clinical Neurosciences (NDCN), University of Oxford, UK; Human Imaging and Image Processing Core (D.L.P.), Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation, Bethesda, MD; Center for Neurological Imaging (C.R.G.G.), Department of Radiology, Brigham and Women's Hospital, Boston, MA; Section of Neuroradiology (Department of Radiology) (D.P.), Vall d'Hebron University Hospital and Research Institute (VHIR), Autonomous University Barcelona, Spain; Neuroimaging Research Unit (M.A.R.), Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; AMIGO (M.J.C.), School of Biomedical Engineering and Imaging Sciences, King's College London; and Institutes of Neurology & Healthcare Engineering (F.B.), UCL London, UK
| | - Maria A Rocca
- From the MS Center Amsterdam (H.V., A.d.S., V.W.), Amsterdam Neuroscience, Department of Radiology and Nuclear Medicine, Amsterdam UMC (M.P.), the Netherlands; Wellcome Centre for Integrative Neuroimaging (WIN), FMRIB (M.J.), Nuffield Department of Clinical Neurosciences (NDCN), University of Oxford, UK; Human Imaging and Image Processing Core (D.L.P.), Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation, Bethesda, MD; Center for Neurological Imaging (C.R.G.G.), Department of Radiology, Brigham and Women's Hospital, Boston, MA; Section of Neuroradiology (Department of Radiology) (D.P.), Vall d'Hebron University Hospital and Research Institute (VHIR), Autonomous University Barcelona, Spain; Neuroimaging Research Unit (M.A.R.), Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; AMIGO (M.J.C.), School of Biomedical Engineering and Imaging Sciences, King's College London; and Institutes of Neurology & Healthcare Engineering (F.B.), UCL London, UK
| | - Viktor Wottschel
- From the MS Center Amsterdam (H.V., A.d.S., V.W.), Amsterdam Neuroscience, Department of Radiology and Nuclear Medicine, Amsterdam UMC (M.P.), the Netherlands; Wellcome Centre for Integrative Neuroimaging (WIN), FMRIB (M.J.), Nuffield Department of Clinical Neurosciences (NDCN), University of Oxford, UK; Human Imaging and Image Processing Core (D.L.P.), Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation, Bethesda, MD; Center for Neurological Imaging (C.R.G.G.), Department of Radiology, Brigham and Women's Hospital, Boston, MA; Section of Neuroradiology (Department of Radiology) (D.P.), Vall d'Hebron University Hospital and Research Institute (VHIR), Autonomous University Barcelona, Spain; Neuroimaging Research Unit (M.A.R.), Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; AMIGO (M.J.C.), School of Biomedical Engineering and Imaging Sciences, King's College London; and Institutes of Neurology & Healthcare Engineering (F.B.), UCL London, UK
| | - M Jorge Cardoso
- From the MS Center Amsterdam (H.V., A.d.S., V.W.), Amsterdam Neuroscience, Department of Radiology and Nuclear Medicine, Amsterdam UMC (M.P.), the Netherlands; Wellcome Centre for Integrative Neuroimaging (WIN), FMRIB (M.J.), Nuffield Department of Clinical Neurosciences (NDCN), University of Oxford, UK; Human Imaging and Image Processing Core (D.L.P.), Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation, Bethesda, MD; Center for Neurological Imaging (C.R.G.G.), Department of Radiology, Brigham and Women's Hospital, Boston, MA; Section of Neuroradiology (Department of Radiology) (D.P.), Vall d'Hebron University Hospital and Research Institute (VHIR), Autonomous University Barcelona, Spain; Neuroimaging Research Unit (M.A.R.), Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; AMIGO (M.J.C.), School of Biomedical Engineering and Imaging Sciences, King's College London; and Institutes of Neurology & Healthcare Engineering (F.B.), UCL London, UK
| | - Frederik Barkhof
- From the MS Center Amsterdam (H.V., A.d.S., V.W.), Amsterdam Neuroscience, Department of Radiology and Nuclear Medicine, Amsterdam UMC (M.P.), the Netherlands; Wellcome Centre for Integrative Neuroimaging (WIN), FMRIB (M.J.), Nuffield Department of Clinical Neurosciences (NDCN), University of Oxford, UK; Human Imaging and Image Processing Core (D.L.P.), Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation, Bethesda, MD; Center for Neurological Imaging (C.R.G.G.), Department of Radiology, Brigham and Women's Hospital, Boston, MA; Section of Neuroradiology (Department of Radiology) (D.P.), Vall d'Hebron University Hospital and Research Institute (VHIR), Autonomous University Barcelona, Spain; Neuroimaging Research Unit (M.A.R.), Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; AMIGO (M.J.C.), School of Biomedical Engineering and Imaging Sciences, King's College London; and Institutes of Neurology & Healthcare Engineering (F.B.), UCL London, UK
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20
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Forte C, Voinea A, Chichirau M, Yeshmagambetova G, Albrecht LM, Erfurt C, Freundt LA, Carmo LOE, Henning RH, van der Horst ICC, Sundelin T, Wiering MA, Axelsson J, Epema AH. Deep Learning for Identification of Acute Illness and Facial Cues of Illness. Front Med (Lausanne) 2021; 8:661309. [PMID: 34381793 PMCID: PMC8350122 DOI: 10.3389/fmed.2021.661309] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 06/30/2021] [Indexed: 12/26/2022] Open
Abstract
Background: The inclusion of facial and bodily cues (clinical gestalt) in machine learning (ML) models improves the assessment of patients' health status, as shown in genetic syndromes and acute coronary syndrome. It is unknown if the inclusion of clinical gestalt improves ML-based classification of acutely ill patients. As in previous research in ML analysis of medical images, simulated or augmented data may be used to assess the usability of clinical gestalt. Objective: To assess whether a deep learning algorithm trained on a dataset of simulated and augmented facial photographs reflecting acutely ill patients can distinguish between healthy and LPS-infused, acutely ill individuals. Methods: Photographs from twenty-six volunteers whose facial features were manipulated to resemble a state of acute illness were used to extract features of illness and generate a synthetic dataset of acutely ill photographs, using a neural transfer convolutional neural network (NT-CNN) for data augmentation. Then, four distinct CNNs were trained on different parts of the facial photographs and concatenated into one final, stacked CNN which classified individuals as healthy or acutely ill. Finally, the stacked CNN was validated in an external dataset of volunteers injected with lipopolysaccharide (LPS). Results: In the external validation set, the four individual feature models distinguished acutely ill patients with sensitivities ranging from 10.5% (95% CI, 1.3-33.1% for the skin model) to 89.4% (66.9-98.7%, for the nose model). Specificity ranged from 42.1% (20.3-66.5%) for the nose model and 94.7% (73.9-99.9%) for skin. The stacked model combining all four facial features achieved an area under the receiver characteristic operating curve (AUROC) of 0.67 (0.62-0.71) and distinguished acutely ill patients with a sensitivity of 100% (82.35-100.00%) and specificity of 42.11% (20.25-66.50%). Conclusion: A deep learning algorithm trained on a synthetic, augmented dataset of facial photographs distinguished between healthy and simulated acutely ill individuals, demonstrating that synthetically generated data can be used to develop algorithms for health conditions in which large datasets are difficult to obtain. These results support the potential of facial feature analysis algorithms to support the diagnosis of acute illness.
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Affiliation(s)
- Castela Forte
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, Netherlands
| | - Andrei Voinea
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, Netherlands
| | - Malina Chichirau
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, Netherlands
| | - Galiya Yeshmagambetova
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, Netherlands
| | - Lea M. Albrecht
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Chiara Erfurt
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Liliane A. Freundt
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Luisa Oliveira e Carmo
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Robert H. Henning
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Iwan C. C. van der Horst
- Department of Intensive Care Medicine, Maastricht University Medical Centre+, University Maastricht, Maastricht, Netherlands
| | - Tina Sundelin
- Department of Psychology, Stress Research Institute, Stockholm University, Stockholm, Sweden
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Marco A. Wiering
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, Netherlands
| | - John Axelsson
- Department of Psychology, Stress Research Institute, Stockholm University, Stockholm, Sweden
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Anne H. Epema
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
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21
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Buimer EEL, Schnack HG, Caspi Y, van Haren NEM, Milchenko M, Pas P, Hulshoff Pol HE, Brouwer RM. De-identification procedures for magnetic resonance images and the impact on structural brain measures at different ages. Hum Brain Mapp 2021; 42:3643-3655. [PMID: 33973694 PMCID: PMC8249889 DOI: 10.1002/hbm.25459] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 02/26/2021] [Accepted: 04/05/2021] [Indexed: 11/12/2022] Open
Abstract
Surface rendering of MRI brain scans may lead to identification of the participant through facial characteristics. In this study, we evaluate three methods that overwrite voxels containing privacy‐sensitive information: Face Masking, FreeSurfer defacing, and FSL defacing. We included structural T1‐weighted MRI scans of children, young adults and older adults. For the young adults, test–retest data were included with a 1‐week interval. The effects of the de‐identification methods were quantified using different statistics to capture random variation and systematic noise in measures obtained through the FreeSurfer processing pipeline. Face Masking and FSL defacing impacted brain voxels in some scans especially in younger participants. FreeSurfer defacing left brain tissue intact in all cases. FSL defacing and FreeSurfer defacing preserved identifiable characteristics around the eyes or mouth in some scans. For all de‐identification methods regional brain measures of subcortical volume, cortical volume, cortical surface area, and cortical thickness were on average highly replicable when derived from original versus de‐identified scans with average regional correlations >.90 for children, young adults, and older adults. Small systematic biases were found that incidentally resulted in significantly different brain measures after de‐identification, depending on the studied subsample, de‐identification method, and brain metric. In young adults, test–retest intraclass correlation coefficients (ICCs) were comparable for original scans and de‐identified scans with average regional ICCs >.90 for (sub)cortical volume and cortical surface area and ICCs >.80 for cortical thickness. We conclude that apparent visual differences between de‐identification methods minimally impact reliability of brain measures, although small systematic biases can occur.
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Affiliation(s)
- Elizabeth E L Buimer
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Hugo G Schnack
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Yaron Caspi
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Neeltje E M van Haren
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.,Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, Rotterdam, Netherlands
| | - Mikhail Milchenko
- Department of Radiology, Washington University School of Medicine, Mallinckrodt Institute of Radiology, Saint Louis, Missouri, USA
| | - Pascal Pas
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | | | - Hilleke E Hulshoff Pol
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Rachel M Brouwer
- UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
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22
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Schwarz CG, Kremers WK, Wiste HJ, Gunter JL, Vemuri P, Spychalla AJ, Kantarci K, Schultz AP, Sperling RA, Knopman DS, Petersen RC, Jack CR. Changing the face of neuroimaging research: Comparing a new MRI de-facing technique with popular alternatives. Neuroimage 2021; 231:117845. [PMID: 33582276 PMCID: PMC8154695 DOI: 10.1016/j.neuroimage.2021.117845] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 12/01/2020] [Accepted: 02/05/2021] [Indexed: 11/18/2022] Open
Abstract
Recent advances in automated face recognition algorithms have increased the risk that de-identified research MRI scans may be re-identifiable by matching them to identified photographs using face recognition. A variety of software exist to de-face (remove faces from) MRI, but their ability to prevent face recognition has never been measured and their image modifications can alter automated brain measurements. In this study, we compared three popular de-facing techniques and introduce our mri_reface technique designed to minimize effects on brain measurements by replacing the face with a population average, rather than removing it. For each technique, we measured 1) how well it prevented automated face recognition (i.e. effects on exceptionally-motivated individuals) and 2) how it altered brain measurements from SPM12, FreeSurfer, and FSL (i.e. effects on the average user of de-identified data). Before de-facing, 97% of scans from a sample of 157 volunteers were correctly matched to photographs using automated face recognition. After de-facing with popular software, 28-38% of scans still retained enough data for successful automated face matching. Our proposed mri_reface had similar performance with the best existing method (fsl_deface) at preventing face recognition (28-30%) and it had the smallest effects on brain measurements in more pipelines than any other, but these differences were modest.
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Affiliation(s)
- Christopher G. Schwarz
- Department of Radiology, Mayo Clinic, Rochester, MN, United States,Correspondence to: Mayo Clinic, Diagnostic Radiology, 200 First Street SW, Rochester, Minnesota 55905, United States. (C.G. Schwarz)
| | - Walter K. Kremers
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Heather J. Wiste
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Jeffrey L. Gunter
- Department of Radiology, Mayo Clinic, Rochester, MN, United States,Department of Information Technology, Mayo Clinic, Rochester, MN, United States
| | | | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Aaron P. Schultz
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Reisa A. Sperling
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - David S. Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | | | - Clifford R. Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
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Bhalerao GV, Parekh P, Saini J, Venkatasubramanian G, John JP. Systematic evaluation of the impact of defacing on quality and volumetric assessments on T1-weighted MR-images. J Neuroradiol 2021; 49:250-257. [PMID: 33727023 DOI: 10.1016/j.neurad.2021.03.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 02/20/2021] [Accepted: 03/01/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND AND PURPOSE Facial features can be potentially reconstructed from structural magnetic resonance images, thereby compromising the confidentiality of study participants. Defacing methods can be applied to MRI images to ensure privacy of study participants. These methods remove facial features, thereby rendering the image unidentifiable. It is commonly assumed that defacing would not have any impact on quantitative assessments of the brain. In this study, we have assessed the impact of different defacing methods on quality and volumetric estimates. MATERIALS AND METHODS We performed SPM-, Freesurfer-, pydeface, and FSL-based defacing on 30 T1-weighted images. We statistically compared the change in quality measurements (from MRIQC) and volumes (from SPM, CAT, and Freesurfer) between non-defaced and defaced images. We also calculated the Dice coefficient of each tissue class between non-defaced and defaced images. RESULTS Almost all quality measurements and tissue volumes changed after defacing, irrespective of the method used. All tissue volumes decreased post-defacing for CAT, but no such consistent trend was seen for SPM and Freesurfer. Dice coefficients indicated that segmentations are relatively robust; however, partial volumes might be affected leading to changed volumetric estimates. CONCLUSION In this study, we demonstrated that volumes and quality measurements get affected differently by defacing methods. It is likely that this will have a significant impact on the reproducibility of experiments. We provide suggestions on ways to minimize the impact of defacing on outcome measurements. Our results warrant the need for robust handling of defaced images at different steps of image processing.
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Affiliation(s)
- Gaurav Vivek Bhalerao
- ADBS Neuroimaging Centre, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India; Translational Psychiatry Lab, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India; Departments of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India
| | - Pravesh Parekh
- ADBS Neuroimaging Centre, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India; Multimodal Brain Image Analysis Laboratory, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India; Departments of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India
| | - Ganesan Venkatasubramanian
- ADBS Neuroimaging Centre, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India; Translational Psychiatry Lab, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India; Departments of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India.
| | - John P John
- ADBS Neuroimaging Centre, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India; Multimodal Brain Image Analysis Laboratory, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India; Departments of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India.
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24
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Bannier E, Barker G, Borghesani V, Broeckx N, Clement P, Emblem KE, Ghosh S, Glerean E, Gorgolewski KJ, Havu M, Halchenko YO, Herholz P, Hespel A, Heunis S, Hu Y, Hu CP, Huijser D, de la Iglesia Vayá M, Jancalek R, Katsaros VK, Kieseler ML, Maumet C, Moreau CA, Mutsaerts HJ, Oostenveld R, Ozturk-Isik E, Pascual Leone Espinosa N, Pellman J, Pernet CR, Pizzini FB, Trbalić AŠ, Toussaint PJ, Visconti di Oleggio Castello M, Wang F, Wang C, Zhu H. The Open Brain Consent: Informing research participants and obtaining consent to share brain imaging data. Hum Brain Mapp 2021; 42:1945-1951. [PMID: 33522661 PMCID: PMC8046140 DOI: 10.1002/hbm.25351] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 01/11/2021] [Accepted: 01/12/2021] [Indexed: 02/06/2023] Open
Abstract
Having the means to share research data openly is essential to modern science. For human research, a key aspect in this endeavor is obtaining consent from participants, not just to take part in a study, which is a basic ethical principle, but also to share their data with the scientific community. To ensure that the participants' privacy is respected, national and/or supranational regulations and laws are in place. It is, however, not always clear to researchers what the implications of those are, nor how to comply with them. The Open Brain Consent (https://open-brain-consent.readthedocs.io) is an international initiative that aims to provide researchers in the brain imaging community with information about data sharing options and tools. We present here a short history of this project and its latest developments, and share pointers to consent forms, including a template consent form that is compliant with the EU general data protection regulation. We also share pointers to an associated data user agreement that is not only useful in the EU context, but also for any researchers dealing with personal (clinical) data elsewhere.
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Affiliation(s)
- Elise Bannier
- Radiology Department, CHU Rennes, Rennes, France.,Inria, CNRS, Inserm, IRISA UMR 6074, Empenn ERL, University of Rennes, Rennes, France
| | - Gareth Barker
- Department of Neuroimaging, King's College London, London, United Kingdom
| | - Valentina Borghesani
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, California, USA
| | - Nils Broeckx
- Dewallens & partners law firm, Leuven, Belgium & Antwerp Health Law and Ethics Chair (AHLEC) and P2 research group, Faculty of law, University of Antwerp, Antwerp, Belgium
| | - Patricia Clement
- Ghent Institute for functional and Metabolic Imaging, Ghent University, Ghent, Belgium
| | | | - Satrajit Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts, USA.,Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts, USA
| | - Enrico Glerean
- Aalto University, Espoo, Finland.,International Laboratory of Social Neurobiology, Institute of Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| | | | - Marko Havu
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | | | - Peer Herholz
- NeuroDataScience - ORIGAMI laboratory, McConnell Brain Imaging Centre, The Neuro (Montreal Neurological Institute-Hospital), Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | | | - Stephan Heunis
- Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Yue Hu
- Institute for Experimental Psychology, Heinrich-Heine-University of Düsseldorf, Düsseldorf, Germany
| | - Chuan-Peng Hu
- School of Psychology, Nanjing Normal University, Nanjing, China
| | - Dorien Huijser
- Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - María de la Iglesia Vayá
- Biomedical Imaging Unit FISABIO-CIPF, Foundation for the Promotion of Health and Biomedical Research of the Valencian Community, Valencia, Spain
| | - Radim Jancalek
- Department of Neurosurgery, St. Anne's University Hospital, Masaryk University, Brno, Czech Republic
| | - Vasileios K Katsaros
- Department of Advanced Imaging Modalities, MRI Unit, General Anti-Cancer and Oncological Hospital of Athens"St. Savvas", National and Kapodistrian University of Athens, Athens, Greece.,Department of Neurosurgery and Neurology, National and Kapodistrian University of Athens, Athens, Greece
| | - Marie-Luise Kieseler
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, USA
| | - Camille Maumet
- Inria, University of Rennes, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U 1228, Rennes, France
| | | | - Henk-Jan Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands.,Department of Radiology and Nuclear Medicine, University Hospital Ghent, Ghent, Belgium
| | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behaviour; Radboud University, Nijmegen, The Netherlands
| | | | - Nicolas Pascual Leone Espinosa
- Biomedical Imaging Unit, FISABIO-CIPF, Foundation for the Promotion of Health and Biomedical Research of the Valencian Community, Valencia, Spain
| | - John Pellman
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, USA
| | - Cyril R Pernet
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | | | | | - Paule-Joanne Toussaint
- McGill University, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada
| | | | - Fengjuan Wang
- National Institute of Education, Nanyang Technological University, Singapore, Singapore
| | - Cheng Wang
- School of Health, Fujian Medical University, Fuzhou, China
| | - Hua Zhu
- Department of Biological Medicine and Engineering, BUAA, Beihang University, Beijing, China
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25
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Artificial Intelligence in Medicine and Privacy Preservation. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_261-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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26
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Kocak B, Kus EA, Kilickesmez O. How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts. Eur Radiol 2020; 31:1819-1830. [PMID: 33006018 DOI: 10.1007/s00330-020-07324-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 08/25/2020] [Accepted: 09/18/2020] [Indexed: 12/16/2022]
Abstract
In recent years, there has been a dramatic increase in research papers about machine learning (ML) and artificial intelligence in radiology. With so many papers around, it is of paramount importance to make a proper scientific quality assessment as to their validity, reliability, effectiveness, and clinical applicability. Due to methodological complexity, the papers on ML in radiology are often hard to evaluate, requiring a good understanding of key methodological issues. In this review, we aimed to guide the radiology community about key methodological aspects of ML to improve their academic reading and peer-review experience. Key aspects of ML pipeline were presented within four broad categories: study design, data handling, modelling, and reporting. Sixteen key methodological items and related common pitfalls were reviewed with a fresh perspective: database size, robustness of reference standard, information leakage, feature scaling, reliability of features, high dimensionality, perturbations in feature selection, class balance, bias-variance trade-off, hyperparameter tuning, performance metrics, generalisability, clinical utility, comparison with traditional tools, data sharing, and transparent reporting.Key Points• Machine learning is new and rather complex for the radiology community.• Validity, reliability, effectiveness, and clinical applicability of studies on machine learning can be evaluated with a proper understanding of key methodological concepts about study design, data handling, modelling, and reporting.• Understanding key methodological concepts will provide a better academic reading and peer-review experience for the radiology community.
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Affiliation(s)
- Burak Kocak
- Department of Radiology, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey.
| | - Ece Ates Kus
- Department of Radiology, Istanbul Training and Research Hospital, Samatya, 34098, Istanbul, Turkey
| | - Ozgur Kilickesmez
- Department of Radiology, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
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