1
|
Theyers AE, Zamyadi M, O'Reilly M, Bartha R, Symons S, MacQueen GM, Hassel S, Lerch JP, Anagnostou E, Lam RW, Frey BN, Milev R, Müller DJ, Kennedy SH, Scott CJM, Strother SC, Arnott SR. Multisite Comparison of MRI Defacing Software Across Multiple Cohorts. Front Psychiatry 2021; 12:617997. [PMID: 33716819 PMCID: PMC7943842 DOI: 10.3389/fpsyt.2021.617997] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 02/03/2021] [Indexed: 01/26/2023] Open
Abstract
With improvements to both scan quality and facial recognition software, there is an increased risk of participants being identified by a 3D render of their structural neuroimaging scans, even when all other personal information has been removed. To prevent this, facial features should be removed before data are shared or openly released, but while there are several publicly available software algorithms to do this, there has been no comprehensive review of their accuracy within the general population. To address this, we tested multiple algorithms on 300 scans from three neuroscience research projects, funded in part by the Ontario Brain Institute, to cover a wide range of ages (3-85 years) and multiple patient cohorts. While skull stripping is more thorough at removing identifiable features, we focused mainly on defacing software, as skull stripping also removes potentially useful information, which may be required for future analyses. We tested six publicly available algorithms (afni_refacer, deepdefacer, mri_deface, mridefacer, pydeface, quickshear), with one skull stripper (FreeSurfer) included for comparison. Accuracy was measured through a pass/fail system with two criteria; one, that all facial features had been removed and two, that no brain tissue was removed in the process. A subset of defaced scans were also run through several preprocessing pipelines to ensure that none of the algorithms would alter the resulting outputs. We found that the success rates varied strongly between defacers, with afni_refacer (89%) and pydeface (83%) having the highest rates, overall. In both cases, the primary source of failure came from a single dataset that the defacer appeared to struggle with - the youngest cohort (3-20 years) for afni_refacer and the oldest (44-85 years) for pydeface, demonstrating that defacer performance not only depends on the data provided, but that this effect varies between algorithms. While there were some very minor differences between the preprocessing results for defaced and original scans, none of these were significant and were within the range of variation between using different NIfTI converters, or using raw DICOM files.
Collapse
Affiliation(s)
- Athena E Theyers
- Rotman Research Institute, Baycrest Health Sciences Centre, Toronto, ON, Canada
| | - Mojdeh Zamyadi
- Rotman Research Institute, Baycrest Health Sciences Centre, Toronto, ON, Canada
| | | | - Robert Bartha
- Department of Medical Biophysics, Robarts Research Institute, Western University, London, ON, Canada
| | - Sean Symons
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Glenda M MacQueen
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Stefanie Hassel
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Jason P Lerch
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, ON, Canada
| | - Evdokia Anagnostou
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.,Mood Disorders Program, St. Joseph's Healthcare, Hamilton, ON, Canada
| | - Roumen Milev
- Departments of Psychiatry and Psychology, Queen's University, Providence Care Hospital, Kingston, ON, Canada
| | - Daniel J Müller
- Molecular Brain Science, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Sidney H Kennedy
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Department of Psychiatry, Krembil Research Centre, University Health Network, Toronto, ON, Canada.,Department of Psychiatry, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada.,Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
| | - Christopher J M Scott
- LC Campbell Cognitive Neurology Research Unit, Toronto, ON, Canada.,Heart & Stroke Foundation Centre for Stroke Recovery, Toronto, ON, Canada.,Sunnybrook Health Sciences Centre, Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest Health Sciences Centre, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Stephen R Arnott
- Rotman Research Institute, Baycrest Health Sciences Centre, Toronto, ON, Canada
| |
Collapse
|
3
|
Das S, Glatard T, MacIntyre LC, Madjar C, Rogers C, Rousseau ME, Rioux P, MacFarlane D, Mohades Z, Gnanasekaran R, Makowski C, Kostopoulos P, Adalat R, Khalili-Mahani N, Niso G, Moreau JT, Evans AC. The MNI data-sharing and processing ecosystem. Neuroimage 2015; 124:1188-1195. [PMID: 26364860 DOI: 10.1016/j.neuroimage.2015.08.076] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2015] [Revised: 08/22/2015] [Accepted: 08/24/2015] [Indexed: 11/29/2022] Open
Abstract
Neuroimaging has been facing a data deluge characterized by the exponential growth of both raw and processed data. As a result, mining the massive quantities of digital data collected in these studies offers unprecedented opportunities and has become paramount for today's research. As the neuroimaging community enters the world of "Big Data", there has been a concerted push for enhanced sharing initiatives, whether within a multisite study, across studies, or federated and shared publicly. This article will focus on the database and processing ecosystem developed at the Montreal Neurological Institute (MNI) to support multicenter data acquisition both nationally and internationally, create database repositories, facilitate data-sharing initiatives, and leverage existing software toolkits for large-scale data processing.
Collapse
Affiliation(s)
- Samir Das
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada.
| | - Tristan Glatard
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada; Université de Lyon, CREATIS ; CNRS UMR5220 ; Inserm U1044 ; INSA-Lyon ; Université Claude Bernard Lyon 1, France
| | - Leigh C MacIntyre
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Cecile Madjar
- Douglas Mental Health University Institute, Montreal, Canada
| | - Christine Rogers
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Marc-Etienne Rousseau
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Pierre Rioux
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Dave MacFarlane
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Zia Mohades
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Rathi Gnanasekaran
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Carolina Makowski
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Penelope Kostopoulos
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Reza Adalat
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Najmeh Khalili-Mahani
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Guiomar Niso
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Jeremy T Moreau
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| |
Collapse
|