A comparison of Freesurfer and multi-atlas MUSE for brain anatomy segmentation: Findings about size and age bias, and inter-scanner stability in multi-site aging studies

Automatic segmentation of brain anatomy has been a key processing step in quantitative neuroimaging analyses. An extensive body of literature has relied on Freesurfer segmentations. Yet, in recent years, the multi-atlas segmentation framework has consistently obtained results with superior accuracy...

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Vydané v:	NeuroImage (Orlando, Fla.) Ročník 223; s. 117248
Hlavní autori:	Srinivasan, Dhivya, Erus, Guray, Doshi, Jimit, Wolk, David A., Shou, Haochang, Habes, Mohamad, Davatzikos, Christos
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Inc 01.12.2020 Elsevier Limited Elsevier
Predmet:	Adult Aged Aging Algorithms Alzheimer's disease Anatomy Automation Bias Brain Brain - anatomy & histology Brain - diagnostic imaging Datasets Female Freesurfer Hippocampus Hippocampus - anatomy & histology Hippocampus - diagnostic imaging Humans Image Processing, Computer-Assisted - methods Magnetic Resonance Imaging Male Medical imaging MRI MUSE Neuroimaging Organ Size Protocol Quality control Registration Reproducibility of Results ROI Segmentation Software Software packages Young Adult Brain Freesurfer Segmentation MRI ROI MUSE
ISSN:	1053-8119, 1095-9572, 1095-9572
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Abstract	Automatic segmentation of brain anatomy has been a key processing step in quantitative neuroimaging analyses. An extensive body of literature has relied on Freesurfer segmentations. Yet, in recent years, the multi-atlas segmentation framework has consistently obtained results with superior accuracy in various evaluations. We compared brain anatomy segmentations from Freesurfer, which uses a single probabilistic atlas strategy, against segmentations from Multi-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters and locally optimal atlas selection (MUSE), one of the leading ensemble-based methods that calculates a consensus segmentation through fusion of anatomical labels from multiple atlases and registrations. The focus of our evaluation was twofold. First, using manual ground-truth hippocampus segmentations, we found that Freesurfer segmentations showed a bias towards over-segmentation of larger hippocampi, and under-segmentation in older age. This bias was more pronounced in Freesurfer-v5.3, which has been used in multiple previous studies of aging, while the effect was mitigated in more recent Freesurfer-v6.0, albeit still present. Second, we evaluated inter-scanner segmentation stability using same day scan pairs from ADNI acquired on 1.5T and 3T scanners. We also found that MUSE obtains more consistent segmentations across scanners compared to Freesurfer, particularly in the deep structures.
AbstractList	Automatic segmentation of brain anatomy has been a key processing step in quantitative neuroimaging analyses. An extensive body of literature has relied on Freesurfer segmentations. Yet, in recent years, the multi-atlas segmentation framework has consistently obtained results with superior accuracy in various evaluations. We compared brain anatomy segmentations from Freesurfer, which uses a single probabilistic atlas strategy, against segmentations from Multi-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters and locally optimal atlas selection (MUSE), one of the leading ensemble-based methods that calculates a consensus segmentation through fusion of anatomical labels from multiple atlases and registrations. The focus of our evaluation was twofold. First, using manual ground-truth hippocampus segmentations, we found that Freesurfer segmentations showed a bias towards over-segmentation of larger hippocampi, and under-segmentation in older age. This bias was more pronounced in Freesurfer-v5.3, which has been used in multiple previous studies of aging, while the effect was mitigated in more recent Freesurfer-v6.0, albeit still present. Second, we evaluated inter-scanner segmentation stability using same day scan pairs from ADNI acquired on 1.5T and 3T scanners. We also found that MUSE obtains more consistent segmentations across scanners compared to Freesurfer, particularly in the deep structures. Automatic segmentation of brain anatomy has been a key processing step in quantitative neuroimaging analyses. An extensive body of literature has relied on Freesurfer segmentations. Yet, in recent years, the multi-atlas segmentation framework has consistently obtained results with superior accuracy in various evaluations. We compared brain anatomy segmentations from Freesurfer, which uses a single probabilistic atlas strategy, against segmentations from Multi-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters and locally optimal atlas selection (MUSE), one of the leading ensemble-based methods that calculates a consensus segmentation through fusion of anatomical labels from multiple atlases and registrations. The focus of our evaluation was twofold. First, using manual ground-truth hippocampus segmentations, we found that Freesurfer segmentations showed a bias towards over-segmentation of larger hippocampi, and under-segmentation in older age. This bias was more pronounced in Freesurfer-v5.3, which has been used in multiple previous studies of aging, while the effect was mitigated in more recent Freesurfer-v6.0, albeit still present. Second, we evaluated inter-scanner segmentation stability using same day scan pairs from ADNI acquired on 1.5T and 3T scanners. We also found that MUSE obtains more consistent segmentations across scanners compared to Freesurfer, particularly in the deep structures.Automatic segmentation of brain anatomy has been a key processing step in quantitative neuroimaging analyses. An extensive body of literature has relied on Freesurfer segmentations. Yet, in recent years, the multi-atlas segmentation framework has consistently obtained results with superior accuracy in various evaluations. We compared brain anatomy segmentations from Freesurfer, which uses a single probabilistic atlas strategy, against segmentations from Multi-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters and locally optimal atlas selection (MUSE), one of the leading ensemble-based methods that calculates a consensus segmentation through fusion of anatomical labels from multiple atlases and registrations. The focus of our evaluation was twofold. First, using manual ground-truth hippocampus segmentations, we found that Freesurfer segmentations showed a bias towards over-segmentation of larger hippocampi, and under-segmentation in older age. This bias was more pronounced in Freesurfer-v5.3, which has been used in multiple previous studies of aging, while the effect was mitigated in more recent Freesurfer-v6.0, albeit still present. Second, we evaluated inter-scanner segmentation stability using same day scan pairs from ADNI acquired on 1.5T and 3T scanners. We also found that MUSE obtains more consistent segmentations across scanners compared to Freesurfer, particularly in the deep structures.
ArticleNumber	117248
Author	Davatzikos, Christos Habes, Mohamad Shou, Haochang Wolk, David A. Srinivasan, Dhivya Doshi, Jimit Erus, Guray
AuthorAffiliation	c Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, United States b Department of Neurology, University of Pennsylvania, United States a Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Richards Building, 3700 Hamilton Walk, 7th Floor, Philadelphia, PA 19104, United States
AuthorAffiliation_xml	– name: c Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, United States – name: b Department of Neurology, University of Pennsylvania, United States – name: a Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Richards Building, 3700 Hamilton Walk, 7th Floor, Philadelphia, PA 19104, United States
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Copyright	2020 Copyright © 2020. Published by Elsevier Inc. Copyright Elsevier Limited Dec 2020
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Keywords	Brain Freesurfer Segmentation MRI ROI MUSE
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Denotes equally contributing senior authors. Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. Denotes equally contributing first authors.
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SubjectTerms	Adult Aged Aging Algorithms Alzheimer's disease Anatomy Automation Bias Brain Brain - anatomy & histology Brain - diagnostic imaging Datasets Female Freesurfer Hippocampus Hippocampus - anatomy & histology Hippocampus - diagnostic imaging Humans Image Processing, Computer-Assisted - methods Magnetic Resonance Imaging Male Medical imaging MRI MUSE Neuroimaging Organ Size Protocol Quality control Registration Reproducibility of Results ROI Segmentation Software Software packages Young Adult
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Title	A comparison of Freesurfer and multi-atlas MUSE for brain anatomy segmentation: Findings about size and age bias, and inter-scanner stability in multi-site aging studies
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