Machine learning for micro-tomography
Machine learning for micro-tomography
复制标题
用于显微断层扫描的机器学习
DOI：
10.1117/12.2274731
复制
发表时间：
2017
影响因子：
5.2
通讯作者：
J. Sethian
中科院分区：
文献类型：
--
作者：
D. Parkinson;D. Pelt;T. Perciano;D. Ushizima;Harinarayan Krishnan;H. Barnard;A. MacDowell;J. Sethian
机器学习已经彻底改变了许多领域，但许多显微断层扫描用户从未在工作中使用过它。先进光源 (ALS) 的显微断层扫描光束线与劳伦斯伯克利国家实验室能源研究应用数学中心 (CAMERA) 合作，现已部署了一系列工具，利用机器学习为 ALS 用户实现数据处理自动化。这包括新的重建算法、特征提取工具以及科学图像的图像分类和推荐系统。其中一些工具要么位于对收集的数据进行操作的自动化管道中，要么作为独立软件。其他部署在伯克利实验室的计算资源上（从工作站到超级计算机），并可供用户通过脚本或易于使用的图形界面进行访问。本文提出了这项工作的进展报告。
Machine learning has revolutionized a number of fields, but many micro-tomography users have never used it for their work. The micro-tomography beamline at the Advanced Light Source (ALS), in collaboration with the Center for Applied Mathematics for Energy Research Applications (CAMERA) at Lawrence Berkeley National Laboratory, has now deployed a series of tools to automate data processing for ALS users using machine learning. This includes new reconstruction algorithms, feature extraction tools, and image classification and recommen- dation systems for scientific image. Some of these tools are either in automated pipelines that operate on data as it is collected or as stand-alone software. Others are deployed on computing resources at Berkeley Lab–from workstations to supercomputers–and made accessible to users through either scripting or easy-to-use graphical interfaces. This paper presents a progress report on this work.