Leveraging Modified Ex Situ Tomography Data for Segmentation of In Situ Synchrotron X-Ray Computed Tomography
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912592159571968 |
|---|---|
| author | Manchester, Tristan Anders, Adam Spadotto, Julio Eccleston, Hannah Beavan, William Arcis, Hugues Connolly, Brian J. |
| author_facet | Manchester, Tristan Anders, Adam Spadotto, Julio Eccleston, Hannah Beavan, William Arcis, Hugues Connolly, Brian J. |
| contents | In situ synchrotron X-ray computed tomography enables dynamic material studies. However, automated segmentation remains challenging due to complex imaging artefacts - like ring and cupping effects - and limited training data. We present a methodology for deep learning-based segmentation by transforming high-quality ex situ laboratory data to train models for segmentation of in situ synchrotron data, demonstrated through a metal oxide dissolution study. Using a modified SegFormer architecture, our approach achieves segmentation performance (94.7% IoU) that matches human inter-annotator reliability (94.6% IoU). This indicates the model has reached the practical upper bound for this task, while reducing processing time by 2 orders of magnitude per 3D dataset compared to manual segmentation. The method maintains robust performance over significant morphological changes during experiments, despite training only on static specimens. This methodology can be readily applied to diverse materials systems, enabling the efficient analysis of the large volumes of time-resolved tomographic data generated in typical in situ experiments across scientific disciplines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_19200 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging Modified Ex Situ Tomography Data for Segmentation of In Situ Synchrotron X-Ray Computed Tomography Manchester, Tristan Anders, Adam Spadotto, Julio Eccleston, Hannah Beavan, William Arcis, Hugues Connolly, Brian J. Materials Science Computer Vision and Pattern Recognition In situ synchrotron X-ray computed tomography enables dynamic material studies. However, automated segmentation remains challenging due to complex imaging artefacts - like ring and cupping effects - and limited training data. We present a methodology for deep learning-based segmentation by transforming high-quality ex situ laboratory data to train models for segmentation of in situ synchrotron data, demonstrated through a metal oxide dissolution study. Using a modified SegFormer architecture, our approach achieves segmentation performance (94.7% IoU) that matches human inter-annotator reliability (94.6% IoU). This indicates the model has reached the practical upper bound for this task, while reducing processing time by 2 orders of magnitude per 3D dataset compared to manual segmentation. The method maintains robust performance over significant morphological changes during experiments, despite training only on static specimens. This methodology can be readily applied to diverse materials systems, enabling the efficient analysis of the large volumes of time-resolved tomographic data generated in typical in situ experiments across scientific disciplines. |
| title | Leveraging Modified Ex Situ Tomography Data for Segmentation of In Situ Synchrotron X-Ray Computed Tomography |
| topic | Materials Science Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.19200 |