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

多相材料内部相分布的准确映射对于提取特征度量(如体积分数和表面积)以及指定用于多物理模拟的网格域至关重要。各种高分辨率成像技术已被开发用于这一任务；然而，每一种技术都有自己的优势和劣势。例如，聚焦离子束扫描电子显微镜(FIB-SEM)可以获得非常高的分辨率(c.5 nm)和位相灵敏度，但很难捕捉到代表性体积，而且是一种破坏性技术。或者，X射线计算机层析成像(XCT)可以在现场捕获大体积，但在区分相位方面往往表现不佳，特别是对于小特征。通过组合来自多种技术(甚至是单一技术的多个模式或探测器)的图像数据，分段数据(即，将每个像素分配给一个相位)可能比任何单独的方法都更可信。然而，这需要使用能够组合来自各种输入通道的信息的卷积方法。特别是，深度卷积神经网络非常适合这种任务，在过去5年中，由于它们在自动驾驶汽车中的使用，它们的实施取得了戏剧性的进步。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式图像数据，潜在地影响材料科学的所有领域。

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.