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...

Celý popis

Uložené v:
Podrobná bibliografia
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
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia: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.
ISSN:1053-8119
1095-9572
1095-9572
DOI:10.1016/j.neuroimage.2020.117248