Comparative assessment of statistical brain MR image segmentation algorithms and their impact on partial volume correction in PET
Abstract
Magnetic resonance imaging (MRI)-guided partial volume effect correction (PVC) in brain positron emission tomography (PET) is now a well-established approach to compensate the large bias in the estimate of regional radioactivity concentration, especially for small structures. The accuracy of the algorithms developed so far is, however, largely dependent on the performance of segmentation methods partitioning MRI brain data into its main classes, namely gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). A comparative evaluation of three brain MRI segmentation algorithms using simulated and clinical brain MR data was performed, and subsequently their impact on PVC in 18F-FDG and 18F-DOPA brain PET imaging was assessed. Two algorithms, the first is bundled in the Statistical Parametric Mapping (SPM2) package while the other is the Expectation Maximization Segmentation (EMS) algorithm, incorporate a priori probability images derived from MR images of a large number of subjects. The third, here referred to as the HBSA algorithm, is a histogram-based segmentation algorithm incorporating an Expectation Maximization approach to model a four-Gaussian mixture for both global and local histograms. Simulated under different combinations of noise and intensity non-uniformity, MR brain phantoms with known true volumes for the different brain classes were generated. The algorithms' performance was checked by calculating the kappa index assessing similarities with the "ground truth" as well as multiclass type I and type II errors including misclassification rates. The impact of image segmentation algorithms on PVC was then quantified using clinical data. The segmented tissues of patients' brain MRI were given as input to the region of interest (RoI)-based geometric transfer matrix (GTM) PVC algorithm, and quantitative comparisons were made. The results of digital MRI phantom studies suggest that the use of HBSA produces the best performance for WM classification. For GM classification, it is suggested to use the EMS. Segmentation performed on clinical MRI data show quite substantial differences, especially when lesions are present. For the particular case of PVC, SPM2 and EMS algorithms show very similar results and may be used interchangeably. The use of HBSA is not recommended for PVC. The partial volume corrected activities in some regions of the brain show quite large relative differences when performing paired analysis on 2 algorithms, implying a careful choice of the segmentation algorithm for GTM-based PVC.
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The accuracy of PVC algorithms is highly dependent on the degree of accuracy achieved by the MRI-PET realignment and MRI segmentation procedures.
“Thirdly, we admit that the accuracy of PVC algorithms is highly dependent on the degree of accuracy achieved by the MRI-PET realignment and MRI segmentation procedures, as assessed in many previous studies (for reviews, see [61,74,88]).”
From Voxel-based mapping of brain gray matter volume and glucose metabolism profiles in normal aging (2009) · cites this paper as [88]
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종설 : 뇌기능 양전자방출단층촬영영상 분석 기법의 방법론적 고찰
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- Authors
- Zaidi, H., Ruest, T., Schoenahl, F., & Montandon, M.-L.
- Affiliations
- University Hospital of Geneva (CH)
- Corresponding author
- Zaidi, Habib
- ORCID
- Zaidi, Habib
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2006-10
- Volume
- 32
- Issue
- 4
- Pages
- 1591-1607
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2166728875
- Cited by
- 108
- References
- 48
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 3 · 2023: 2 · 2022: 3 · 2021: 1 · 2020: 3 · 2019: 3 · 2018: 4 · 2017: 1 · 2016: 6 · 2015: 8 · 2014: 8 · 2013: 6 · 2012: 12
- FWCI
- 9.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (SNSF 3152A0-102143)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- brain MRI segmentation, partial volume correction, PET, cerebrospinal fluid, expectation-maximization segmentation, histogram-based segmentation, 18F-FDG PET, 18F-DOPA PET, MRI phantom, statistical parametric mapping
- MeSH terms
- Algorithms, Brain, Brain Mapping, Data Interpretation, Statistical, Dihydroxyphenylalanine, Humans, Magnetic Resonance Imaging, Phantoms, Imaging, Radiopharmaceuticals, Fluorodeoxyglucose F18, Positron-Emission Tomography
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