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Item type:Publication, Access status: with files , Generative adversarial networks for creating realistic training data for machine learning-based segmentation of FIB tomography dataAccurate 3D reconstruction of the structure of nanomaterials is essential for studying their physical properties. Focused ion beam (FIB) tomography is a preferred method for creating 3D image stacks of micrometer-sized material volumes at nanometer resolution. To achieve valid 3D reconstructions from FIB tomography data, semantic segmentation of these images using machine learning-based methods is often beneficial. However, supervised machine learning requires a large amount of training data and ground truth, which is challenging because FIB tomography is a destructive technique. This motivates the use of synthetic training data generated with Monte Carlo simulations of the FIB tomography process. However, these simulations are computationally expensive, and the resulting synthetic imaging data still differs from real FIB tomography data in terms of the statistical distribution of various features. In this study, we propose a novel approach to overcome both problems, i.e. requiring high computation time and difference in data distribution, using generative adversarial networks.Publicationtype: Journal Article55 30 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Enhancing 3D reconstruction accuracy of FIB tomography data using multi-voltage images and multimodal machine learningFIB-SEM tomography is a powerful technique that integrates a focused ion beam (FIB) and a scanning electron microscope (SEM) to capture high-resolution imaging data of nanostructures. This approach involves collecting in-plane SEM images and using FIB to remove material layers for imaging subsequent planes, thereby producing image stacks. However, these image stacks in FIB-SEM tomography are subject to the shine-through effect, which makes structures visible from the posterior regions of the current plane. This artifact introduces an ambiguity between image intensity and structures in the current plane, making conventional segmentation methods such as thresholding or the k-means algorithm insufficient. In this study, we propose a multimodal machine learning approach that combines intensity information obtained at different electron beam accelerating voltages to improve the three-dimensional (3D) reconstruction of nanostructures. By treating the increased shine-through effect at higher accelerating voltages as a form of additional information, the proposed method significantly improves segmentation accuracy and leads to more precise 3D reconstructions for real FIB tomography data.Publicationtype: Journal Article76 127 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Training deep neural networks to reconstruct nanoporous structures from FIB tomography images using synthetic training data(Frontiers Media S.A., 2022-02-28); ; ; ; 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.Publicationtype: Journal ArticleScopus© Citations 5 156 895