SimAQ: Mitigating Experimental Artifacts in Soft X-Ray Tomography using Simulated Acquisitions
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913146897170432 |
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| author | Egebjerg, Jacob Wüstner, Daniel |
| author_facet | Egebjerg, Jacob Wüstner, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10821 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SimAQ: Mitigating Experimental Artifacts in Soft X-Ray Tomography using Simulated Acquisitions Egebjerg, Jacob Wüstner, Daniel Quantitative Methods 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. |
| title | SimAQ: Mitigating Experimental Artifacts in Soft X-Ray Tomography using Simulated Acquisitions |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2508.10821 |