The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining
Open accessAbstract
Despite advances in data augmentation and transfer learning, convolutional neural networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans, CNNs are highly sensitive to changes in resolution and contrast: even within the same MRI modality, performance can decrease across datasets. Here we introduce SynthSeg, the first segmentation CNN robust against changes in contrast and resolution. SynthSeg is trained with synthetic data sampled from a generative model conditioned on segmentations. Crucially, we adopt a domain randomisation strategy where we fully randomise the contrast and resolution of the synthetic training data. Consequently, SynthSeg can segment real scans from a wide range of target domains without retraining or fine-tuning, which enables straightforward analysis of huge amounts of heterogeneous clinical data. Because SynthSeg only requires segmentations to be trained (no images), it can learn from labels obtained by automated methods on diverse populations (e.g., ageing and diseased), thus achieving robustness to a wide range of morphological variability. We demonstrate SynthSeg on 5,000 scans of six modalities (including CT) and ten resolutions, where it exhibits unparallelled generalisation compared with supervised CNNs, state-of-the-art domain adaptation, and Bayesian segmentation. Finally, we demonstrate the generalisability of SynthSeg by applying it to cardiac MRI and CT scans.
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Wittmann, B., Wattenberg, Y., Amiranashvili, T., Shit, S., & Menze, B. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52734.2025.01944 · Cited by 25
A human brain network linked to restoration of consciousness after deep brain stimulation
Warren, A. E. L., Raguž, M., Friedrich, H., Schaper, F. L. W. V. J., Tasserie, J., Snider, S. B., Li, J., Chua, M. M. J., Butenko, K., Friedrich, M. U., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-61988-4 · Cited by 18
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Metadata
- Authors
- Billot, B., Greve, D. N., Puonti, O., Thielscher, A., Van Leemput, K., Fischl, B., Dalca, A. V., & Iglesias, J. E.
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2023-05
- Volume
- 86
- Pages
- 102789
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W4321760728
- Cited by
- 641
- References
- 72
- FWCI
- 57.73
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Billot, B., Greve, D. N., Puonti, O., Thielscher, A., Van Leemput, K., Fischl, B., Dalca, A. V., & Iglesias, J. E. (2023). SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining. Medical Image Analysis. https://doi.org/10.1016/j.media.2023.102789
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