Guardat en:
| Autors principals: | , |
|---|---|
| Format: | Preprint |
| Publicat: |
2025
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| Matèries: | |
| Accés en línia: | https://arxiv.org/abs/2508.10821 |
| Etiquetes: |
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Taula de continguts:
- Soft X-ray tomography provides detailed structural insight into whole cells but is hindered by experimental artifacts such as the missing wedge and by limited availability of annotated datasets. We present SimAQ, a simulation pipeline that generates realistic yeast phantoms and applies synthetic imaging artifacts to produce paired noisy volumes, sinograms, and reconstructions. We validate our approach by training a neural network primarily on synthetic data and demonstrate effective few-shot and zero-shot transfer learning on real X-ray tomograms. Our model delivers accurate segmentations, enabling quantitative analysis of noisy tomograms without relying on large labeled datasets.