Title | ||
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Reliability and sensitivity of two whole-brain segmentation approaches included in FreeSurfer – ASEG and SAMSEG |
Abstract | ||
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Accurate and reliable whole-brain segmentation is critical to longitudinal neuroimaging studies. We undertake a comparative analysis of two subcortical segmentation methods, Automatic Segmentation (ASEG) and Sequence Adaptive Multimodal Segmentation (SAMSEG), recently provided in the open-source neuroimaging package FreeSurfer 7.1, with regard to reliability, bias, sensitivity to detect longitudinal change, and diagnostic sensitivity to Alzheimer’s disease. First, we assess intra- and inter-scanner reliability for eight bilateral subcortical structures: amygdala, caudate, hippocampus, lateral ventricles, nucleus accumbens, pallidum, putamen and thalamus. For intra-scanner analysis we use a large sample of participants (n = 1629) distributed across the lifespan (age range = 4–93 years) and acquired on a 1.5T Siemens Avanto (n = 774) and a 3T Siemens Skyra (n = 855) scanners. For inter-scanner analysis we use a sample of 24 participants scanned on the day with three models of Siemens scanners: 1.5T Avanto, 3T Skyra and 3T Prisma. Second, we test how each method detects volumetric age change using longitudinal follow up scans (n = 491 for Avanto and n = 245 for Skyra; interscan interval = 1–10 years). Finally, we test sensitivity to clinically relevant change. We compare annual rate of hippocampal atrophy in cognitively normal older adults (n = 20), patients with mild cognitive impairment (n = 20) and Alzheimer’s disease (n = 20). We find that both ASEG and SAMSEG are reliable and lead to the detection of within-person longitudinal change, although with notable differences between age-trajectories for most structures, including hippocampus and amygdala. In summary, SAMSEG yields significantly lower differences between repeated measures for intra- and inter-scanner analysis without compromising sensitivity to changes and demonstrating ability to detect clinically relevant longitudinal changes. |
Year | DOI | Venue |
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2021 | 10.1016/j.neuroimage.2021.118113 | NeuroImage |
DocType | Volume | ISSN |
Journal | 237 | 1053-8119 |
Citations | PageRank | References |
1 | 0.36 | 13 |
Authors | ||
11 |
Name | Order | Citations | PageRank |
---|---|---|---|
Donatas Sederevičius | 1 | 1 | 0.36 |
Didac Vidal-Piñeiro | 2 | 1 | 0.36 |
Øystein Sørensen | 3 | 1 | 0.36 |
Koen van Leemput | 4 | 1 | 0.36 |
Juan Eugenio Iglesias | 5 | 1 | 0.36 |
Dalca Adrian V. | 6 | 248 | 31.82 |
Douglas N Greve | 7 | 1 | 0.36 |
Fischl Bruce | 8 | 4131 | 219.39 |
Atle Bjørnerud | 9 | 1 | 0.36 |
Kristine B Walhovd | 10 | 1 | 0.36 |
Anders M Fjell | 11 | 1 | 0.36 |