利用改进的非原位断层扫描数据进行原位同步加速器x射线计算机断层扫描的分割。

IF 1.9 4区 工程技术 Q3 MICROSCOPY
Tristan Manchester, Adam Anders, Julio Spadotto, Hannah Eccleston, William Beavan, Hugues Arcis, Brian J Connolly
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引用次数: 0

摘要

原位同步加速器x射线计算机断层扫描使动态材料研究成为可能。然而,由于复杂的成像伪影(如环效应和拔罐效应)和有限的训练数据,自动分割仍然具有挑战性。我们提出了一种基于深度学习的分割方法,通过将高质量的非原位实验室数据转换为用于原位同步加速器数据分割的训练模型,并通过金属氧化物溶解研究进行了验证。使用改进的SegFormer架构,我们的方法实现了与人类注释器间可靠性(94.6% IoU)相匹配的分割性能(94.7% IoU)。这表明该模型已经达到了该任务的实际上限,同时与手动分割相比,每个3D数据集的处理时间减少了2个数量级。该方法在实验过程中对显著的形态变化保持稳健的性能,尽管只在静态标本上进行训练。这种方法可以很容易地应用于不同的材料系统,能够有效地分析在不同学科的典型原位实验中产生的大量时间分辨层析成像数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Leveraging modified ex situ tomography data for segmentation of in situ synchrotron X-ray computed tomography.

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.

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来源期刊
Journal of microscopy
Journal of microscopy 工程技术-显微镜技术
CiteScore
4.30
自引率
5.00%
发文量
83
审稿时长
1 months
期刊介绍: The Journal of Microscopy is the oldest journal dedicated to the science of microscopy and the only peer-reviewed publication of the Royal Microscopical Society. It publishes papers that report on the very latest developments in microscopy such as advances in microscopy techniques or novel areas of application. The Journal does not seek to publish routine applications of microscopy or specimen preparation even though the submission may otherwise have a high scientific merit. The scope covers research in the physical and biological sciences and covers imaging methods using light, electrons, X-rays and other radiations as well as atomic force and near field techniques. Interdisciplinary research is welcome. Papers pertaining to microscopy are also welcomed on optical theory, spectroscopy, novel specimen preparation and manipulation methods and image recording, processing and analysis including dynamic analysis of living specimens. Publication types include full papers, hot topic fast tracked communications and review articles. Authors considering submitting a review article should contact the editorial office first.
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