The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling
Open accessAbstract
Quantitative analysis of magnetic resonance imaging (MRI) scans of the brain requires accurate automated segmentation of anatomical structures. A desirable feature for such segmentation methods is to be robust against changes in acquisition platform and imaging protocol. In this paper we validate the performance of a segmentation algorithm designed to meet these requirements, building upon generative parametric models previously used in tissue classification. The method is tested on four different datasets acquired with different scanners, field strengths and pulse sequences, demonstrating comparable accuracy to state-of-the-art methods on T1-weighted scans while being one to two orders of magnitude faster. The proposed algorithm is also shown to be robust against small training datasets, and readily handles images with different MRI contrast as well as multi-contrast data.
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Cites
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Nonrigid registration using free-form deformations: application to breast mr images
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Cited by
SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining
Billot, B., Greve, D. N., Puonti, O., Thielscher, A., Van Leemput, K., Fischl, B., Dalca, A. V., & Iglesias, J. E. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102789 · Cited by 641
Self-citationYazdan-Panah, A., Schmidt-Mengin, M., Ricigliano, V. A. G., Soulier, T., Stankoff, B., & Colliot, O. (2023) · NeuroImage: Clinical · doi:10.1016/j.nicl.2023.103368 · Cited by 58
van den Brink, H., Kozberg, M. G., Makkinejad, N., Kirsch, J. E., Thrippleton, M. J., van Harten, T. W., Voigt, S., Freeze, W. M., Van Osch, M. J. P., Viswanathan, A., et al. (2025) · Neurology · doi:10.1212/wnl.0000000000214336 · Cited by 11
Metadata
- Authors
- Puonti, O., Iglesias, J. E., & Van Leemput, K.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-12
- Volume
- 143
- Pages
- 235-249
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2514101822
- Cited by
- 258
- References
- 72
- FWCI
- 2.51
- Type
- journal article
- Language
- en
- Open access
- hybrid
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