Quality control strategies for brain MRI segmentation and parcellation: Practical approaches and recommendations - insights from the Maastricht study
Quality control of brain segmentation is a fundamental step to ensure data quality. Manual quality control strategies are the current gold standard, although these may be unfeasible for large neuroimaging samples. Several options for automated quality control have been proposed, providing potential...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-08-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811921004511 |
| _version_ | 1819148543607177216 |
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| author | Jennifer Monereo-Sánchez Joost J.A. de Jong Gerhard S. Drenthen Magdalena Beran Walter H. Backes Coen D.A. Stehouwer Miranda T. Schram David E.J. Linden Jacobus F.A. Jansen |
| author_facet | Jennifer Monereo-Sánchez Joost J.A. de Jong Gerhard S. Drenthen Magdalena Beran Walter H. Backes Coen D.A. Stehouwer Miranda T. Schram David E.J. Linden Jacobus F.A. Jansen |
| author_sort | Jennifer Monereo-Sánchez |
| collection | DOAJ |
| description | Quality control of brain segmentation is a fundamental step to ensure data quality. Manual quality control strategies are the current gold standard, although these may be unfeasible for large neuroimaging samples. Several options for automated quality control have been proposed, providing potential time efficient and reproducible alternatives. However, those have never been compared side to side, which prevents consensus in the appropriate quality control strategy to use. This study aimed to elucidate the changes manual editing of brain segmentations produce in morphological estimates, and to analyze and compare the effects of different quality control strategies on the reduction of the measurement error.Structural brain MRI from 259 participants of The Maastricht Study were used. Morphological estimates were automatically extracted using FreeSurfer 6.0. Segmentations with inaccuracies were manually edited, and morphological estimates were compared before and after editing. In parallel, 12 quality control strategies were applied to the full sample. Those included: two manual strategies, in which images were visually inspected and either excluded or manually edited; five automated strategies, where outliers were excluded based on the tools “MRIQC” and “Qoala-T”, and the metrics “morphological global measures”, “Euler numbers” and “Contrast-to-Noise ratio”; and five semi-automated strategies, where the outliers detected through the mentioned tools and metrics were not excluded, but visually inspected and manually edited. In order to quantify the effects of each quality control strategy, the proportion of unexplained variance relative to the total variance was extracted after the application of each strategy, and the resulting differences compared.Manually editing brain surfaces produced particularly large changes in subcortical brain volumes and moderate changes in cortical surface area, thickness and hippocampal volumes. The performance of the quality control strategies depended on the morphological measure of interest. Overall, manual quality control strategies yielded the largest reduction in relative unexplained variance. The best performing automated alternatives were those based on Euler numbers and MRIQC scores. The exclusion of outliers based on global morphological measures produced an increase of relative unexplained variance.Manual quality control strategies are the most reliable solution for quality control of brain segmentation and parcellation. However, measures must be taken to prevent the subjectivity associated with these strategies. The detection of inaccurate segmentations based on Euler numbers or MRIQC provides a time efficient and reproducible alternative. The exclusion of outliers based on global morphological estimates must be avoided. |
| first_indexed | 2024-12-22T13:47:23Z |
| format | Article |
| id | doaj.art-78496511c05f4fb0adbc54624b2a9572 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-22T13:47:23Z |
| publishDate | 2021-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-78496511c05f4fb0adbc54624b2a95722022-12-21T18:23:45ZengElsevierNeuroImage1095-95722021-08-01237118174Quality control strategies for brain MRI segmentation and parcellation: Practical approaches and recommendations - insights from the Maastricht studyJennifer Monereo-Sánchez0Joost J.A. de Jong1Gerhard S. Drenthen2Magdalena Beran3Walter H. Backes4Coen D.A. Stehouwer5Miranda T. Schram6David E.J. Linden7Jacobus F.A. Jansen8School for Mental Health & Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands; Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands; Corresponding authors.School for Mental Health & Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands; Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, Maastricht, the NetherlandsSchool for Mental Health & Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands; Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, Maastricht, the NetherlandsSchool for Mental Health & Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands; School for Cardiovascular Diseases, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands; Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the NetherlandsSchool for Mental Health & Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands; Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands; School for Cardiovascular Diseases, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the NetherlandsSchool for Cardiovascular Diseases, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands; Department of Internal Medicine, Maastricht University Medical Center, Maastricht, the NetherlandsSchool for Mental Health & Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands; School for Cardiovascular Diseases, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands; Department of Internal Medicine, Maastricht University Medical Center, Maastricht, the Netherlands; Maastricht Heart+Vascular Center, Maastricht University Medical Center, Maastricht, the NetherlandsSchool for Mental Health & Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the NetherlandsSchool for Mental Health & Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands; Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Corresponding authors.Quality control of brain segmentation is a fundamental step to ensure data quality. Manual quality control strategies are the current gold standard, although these may be unfeasible for large neuroimaging samples. Several options for automated quality control have been proposed, providing potential time efficient and reproducible alternatives. However, those have never been compared side to side, which prevents consensus in the appropriate quality control strategy to use. This study aimed to elucidate the changes manual editing of brain segmentations produce in morphological estimates, and to analyze and compare the effects of different quality control strategies on the reduction of the measurement error.Structural brain MRI from 259 participants of The Maastricht Study were used. Morphological estimates were automatically extracted using FreeSurfer 6.0. Segmentations with inaccuracies were manually edited, and morphological estimates were compared before and after editing. In parallel, 12 quality control strategies were applied to the full sample. Those included: two manual strategies, in which images were visually inspected and either excluded or manually edited; five automated strategies, where outliers were excluded based on the tools “MRIQC” and “Qoala-T”, and the metrics “morphological global measures”, “Euler numbers” and “Contrast-to-Noise ratio”; and five semi-automated strategies, where the outliers detected through the mentioned tools and metrics were not excluded, but visually inspected and manually edited. In order to quantify the effects of each quality control strategy, the proportion of unexplained variance relative to the total variance was extracted after the application of each strategy, and the resulting differences compared.Manually editing brain surfaces produced particularly large changes in subcortical brain volumes and moderate changes in cortical surface area, thickness and hippocampal volumes. The performance of the quality control strategies depended on the morphological measure of interest. Overall, manual quality control strategies yielded the largest reduction in relative unexplained variance. The best performing automated alternatives were those based on Euler numbers and MRIQC scores. The exclusion of outliers based on global morphological measures produced an increase of relative unexplained variance.Manual quality control strategies are the most reliable solution for quality control of brain segmentation and parcellation. However, measures must be taken to prevent the subjectivity associated with these strategies. The detection of inaccurate segmentations based on Euler numbers or MRIQC provides a time efficient and reproducible alternative. The exclusion of outliers based on global morphological estimates must be avoided.http://www.sciencedirect.com/science/article/pii/S1053811921004511Brain segmentationCortical parcellationFreeSurferQuality controlManual editingOutlier exclusion |
| spellingShingle | Jennifer Monereo-Sánchez Joost J.A. de Jong Gerhard S. Drenthen Magdalena Beran Walter H. Backes Coen D.A. Stehouwer Miranda T. Schram David E.J. Linden Jacobus F.A. Jansen Quality control strategies for brain MRI segmentation and parcellation: Practical approaches and recommendations - insights from the Maastricht study NeuroImage Brain segmentation Cortical parcellation FreeSurfer Quality control Manual editing Outlier exclusion |
| title | Quality control strategies for brain MRI segmentation and parcellation: Practical approaches and recommendations - insights from the Maastricht study |
| title_full | Quality control strategies for brain MRI segmentation and parcellation: Practical approaches and recommendations - insights from the Maastricht study |
| title_fullStr | Quality control strategies for brain MRI segmentation and parcellation: Practical approaches and recommendations - insights from the Maastricht study |
| title_full_unstemmed | Quality control strategies for brain MRI segmentation and parcellation: Practical approaches and recommendations - insights from the Maastricht study |
| title_short | Quality control strategies for brain MRI segmentation and parcellation: Practical approaches and recommendations - insights from the Maastricht study |
| title_sort | quality control strategies for brain mri segmentation and parcellation practical approaches and recommendations insights from the maastricht study |
| topic | Brain segmentation Cortical parcellation FreeSurfer Quality control Manual editing Outlier exclusion |
| url | http://www.sciencedirect.com/science/article/pii/S1053811921004511 |
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