Enhancing the confidence of segmenetation maps using complimentary data and convolutional segmentation

多相材料内部相分布的精确地图对于提取特征度量（如体积分数和表面积）以及指定用于多物理场模拟的网格域至关重要。各种高分辨率成像技术已经被开发出来；然而，每一种都有自己的优点和缺点。例如，聚焦离子束扫描选择显微镜（FIB-SEM）可以实现非常高的分辨率（c. 5 nm）和相灵敏度，但难以捕获代表性体积，并且是一种破坏性技术。另外，x射线计算机断层扫描（XCT）可以捕获大量的原位数据，但通常在鉴别阶段表现不佳，特别是小特征。通过结合来自多种技术的图像数据（甚至只是单一技术的多种模式或检测器），可以比任何单独的方法更有信心地分割数据（即将每个像素分配到一个阶段）。然而，这需要使用卷积方法，它能够组合来自不同输入通道的信息。特别是，深度卷积神经网络非常适合这类任务，并且在过去的5年里，由于在自动驾驶汽车中的应用，其实现取得了巨大的进步。该项目将开发基于机器学习的卷积分割工具，用于自信地表征多模态图像数据，在材料科学的所有领域都有潜在的影响。

Accurate maps of the distribution of phases inside multiphase materials is vital for extracting characteristic metrics (such as volume fractions and surface areas), as well as for specifying mesh domains for use in multiphysics simulations. Various high resolution imaging techniques have been developed for this task; however, each has its own strengths and weaknesses. For example, focused ion-beam scanning electing microscopy (FIB-SEM) can achieve very high resolution (c. 5 nm) and phase sensitivity, but struggles to capture representative volumes and is a destructive technique. Alternatively, X-ray computed tomography (XCT) can capture large volumes in-situ, but often performs poorly at differentiating phases, particularly of small features.By combining image data from multiple techniques (or even just multiple modes or detectors of a single technique) it is possible segment data (i.e. assign each pixel to a phase) with more confidence than any individual method. However, this requires the use of convolutional methods that are able to combine the information from various input channels. In particular, deep convolutional neural networks are very well suited to this kind of task and have seen dramatic advancements in their implementation over the last 5 years due, in part, to their use in self driving cars.This project would develop machine learning based convolutional segmentation tools for confidently characterising multimodal image data, with potential for impact across all areas of materials science.