SIAM: Head and Brain MRI Segmentation from Few High-Quality Templates via Synthetic Training
Synthetic training has recently advanced brain MRI segmentation by enabling contrast-agnostic models trained entirely on generated data. However, most existing approaches rely on hundreds of automatically labeled templates, introducing systematic biases and limiting their flexibility to incorporate new anatomical structures. We present the Segment It All Model (SIAM), a 3D whole-head segmentation framework for 16 anatomical structures, trained using only six high-quality, manually annotated templates. SIAM extends domain randomization to both intensity and shape domains: synthetic image generation ensures contrast variability, while high-resolution spatial transformations model anatomical...
| Authors | Romain Valabregue, Ines Khemir, Eric Badinet et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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