Training Deep Neural Networks to Reconstruct Nanoporous Structures From FIB Tomography Images Using Synthetic Training Data

Author:

Sardhara Trushal,Aydin Roland C.,Li Yong,Piché Nicolas,Gauvin Raynald,Cyron Christian J.,Ritter Martin

Abstract

Focused ion beam (FIB) tomography is a destructive technique used to collect three-dimensional (3D) structural information at a resolution of a few nanometers. For FIB tomography, a material sample is degraded by layer-wise milling. After each layer, the current surface is imaged by a scanning electron microscope (SEM), providing a consecutive series of cross-sections of the three-dimensional material sample. Especially for nanoporous materials, the reconstruction of the 3D microstructure of the material, from the information collected during FIB tomography, is impaired by the so-called shine-through effect. This effect prevents a unique mapping between voxel intensity values and material phase (e.g., solid or void). It often substantially reduces the accuracy of conventional methods for image segmentation. Here we demonstrate how machine learning can be used to tackle this problem. A bottleneck in doing so is the availability of sufficient training data. To overcome this problem, we present a novel approach to generate synthetic training data in the form of FIB-SEM images generated by Monte Carlo simulations. Based on this approach, we compare the performance of different machine learning architectures for segmenting FIB tomography data of nanoporous materials. We demonstrate that two-dimensional (2D) convolutional neural network (CNN) architectures processing a group of adjacent slices as input data as well as 3D CNN perform best and can enhance the segmentation performance significantly.

Funder

Deutsche Forschungsgemeinschaft

Publisher

Frontiers Media SA

Subject

Materials Science (miscellaneous)

Reference33 articles.

1. Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images;Ciresan;Adv. Neural Inf. Process. Syst.,2012

2. For the National Institute of Standards and TechnologySimulated Sem Images for Resolution Measurement;Cizmar;Scanning,2008

3. 3d U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation;Çiçek,2016

4. Optimization of FIB-SEM Tomography and Reconstruction for Soft, Porous, and Poorly Conducting Materials;Fager;Microsc. Microanal,2020

5. Reconstruction of Highly Porous Structures from FIB‐SEM Using a Deep Neural Network Trained on Synthetic Images;Fend;J. Microsc.,2021

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3