3d Autoencoders For Feature Extraction In X-Ray Tomography

Tekawade, Aniket; Liu, Zhengchun; Kenesei, Péter; Biçer, Tekin; Carlo, Francesco De; Kettimuthu, Rajkumar; Foster, Ian

doi:10.1109/icip42928.2021.9506494

Cited by 7 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the automatic lens changing mechanism we can easily pad high resolution with low resolution projections and correct the high-resolution scans from local tomography artifacts (Xiao et al, 2007). Finally, we plan to add AI-based methods (Schoonhoven et al, 2020;Tekawade et al, 2021) for detecting events and trigger data saving automatically. In fast-evolving dynamic systems, automatic segmentation, classification, and detection may allow for steering tomographic experiments, e.g.…”

Section: Discussionmentioning

confidence: 99%

Real-time streaming tomographic reconstruction with on-demand data capturing and 3D zooming to regions of interest

Никитин¹,

Tekawade²,

Duchkov³

et al. 2022

J Synchrotron Rad

View full text Add to dashboard Cite

Complex dynamic tomographic experiments at brilliant X-ray light sources require real-time feedback on the sample changes with respect to environmental conditions, selecting representative regions of interest for high-resolution scanning, and on-demand data saving mechanisms for storing only relevant projections acquired by fast area detectors and reducing data volumes. Here the implementation details of a 3D real-time imaging monitoring instrument, with zooming to a volume of interest with easy-to-use visualization via ImageJ, a tool familiar to most beamline users, is presented. The instrument relies on optimized data flow between the detector and processing machines and is implemented on commodity computers. The instrument has been developed at beamline 2-BM of the Advanced Photon Source, where the automatic lens changing mechanism for zooming is implemented with an Optique Peter microscope. Performance tests demonstrate the ability to process more than 3 GB of projection data per second and generate real-time 3D zooming with different magnification. These new capabilities are essential for new APS Upgrade instruments such as the projection microscope under development at beamline 32-ID. The efficacy of the proposed instrument was demonstrated during an in situ tomographic experiment on ice and gas hydrate formation in porous samples.

show abstract

Section: Discussionmentioning

confidence: 99%

Real-time streaming tomographic reconstruction with on-demand data capturing and 3D zooming to regions of interest

Никитин¹,

Tekawade²,

Duchkov³

et al. 2022

J Synchrotron Rad

View full text Add to dashboard Cite

show abstract

“…The obvious and historically primary application of X-rays is medicine. There are many works here that use autoencoders to look for anomalies in X-ray images, e.g., [ 46 , 47 , 48 ].…”

Section: Related Workmentioning

confidence: 99%

Ultra-Lightweight Fast Anomaly Detectors for Industrial Applications

Kocon,

Malesa,

Rapcewicz

2023

Sensors

View full text Add to dashboard Cite

Quality inspection in the pharmaceutical and food industry is crucial to ensure that products are safe for the customers. Among the properties that are controlled in the production process are chemical composition, the content of the active substances, and visual appearance. Although the latter may not influence the product’s properties, it lowers customers’ confidence in drugs or food and affects brand perception. The visual appearance of the consumer goods is typically inspected during the packaging process using machine vision quality inspection systems. In line with the current trends, the processing of the images is often supported with deep neural networks, which increases the accuracy of detection and classification of faults. Solutions based on AI are best suited to production lines with a limited number of formats or highly repeatable production. In the case where formats differ significantly from each other and are often being changed, a quality inspection system has to enable fast training. In this paper, we present a fast method for image anomaly detection that is used in high-speed production lines. The proposed method meets these requirements: It is easy and fast to train, even on devices with limited computing power. The inference time for each production sample is sufficient for real-time scenarios. Additionally, the ultra-lightweight algorithm can be easily adapted to different products and different market segments. In this work, we present the results of our algorithm on three different real production data gathered from food and pharmaceutical industries.

show abstract

“…In previous work, the authors from ANL showed that if U-net is too deep, it overfits on the shapes observed in the training data, leading to models that do not generalize accurately [1]. Further details about the training and data sampling algorithm were detailed previously [9].…”

Section: Surface Determinationmentioning

confidence: 99%

“…Each subset was reconstructed using DeepRecon as well as a traditional Feldkamp-Davis-Kress (FDK) reconstruction algorithm. Both datasets were then directly segmented (or binarized) using the surface determination algorithm from TomoEncoders, which utilizes 3D fully convolutional 1 More info about this article: http://www.ndt.net/?id=26583 https://doi.org/10.58286/26583 11th Conference on Industrial Computed Tomography, Wels, Austria (iCT 2022), www.ict-conference.com/2022 neural networks (fCNN) to perform surface segmentation [5], resulting in a binarized volume which implicitly determines the surface. For segmentation, the labeled mask for the training and testing data (two separate specimens) was generated from the full exposure data.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for improving the efficiency of dimensional measurement workflows with high-resolution X-ray computed tomography

Tekawade¹,

Villarraga-Gómez²,

Andrew³

et al. 2022

eJNDT

View full text Add to dashboard Cite

High-resolution X-ray computed tomography (CT) instruments, also known as three-dimensional (3D) X-ray microscopes, can be adapted for dimensional metrology applications such as geometric dimensioning and tolerancing of metallic components. However, CT scanning times can be prohibitively high for industrial measurement inspection tasks owing to the poor contrast from X-ray attenuation in Ferrous metals, especially if the measurement of spatial resolutions under 5 µm are required. This paper describes a software-defined approach to dramatically reducing total exposure time (or scanning time) while maintaining resolution loss within 2 micrometers as compared to the baseline scans acquired over 6 hours. Here, we combine two deep learning (DL) codes in our surface extraction workflow to compensate for lower signal-to-noise ratio in short exposure data (acquired with lower number of projections): (1) a surface determination (post-reconstruction) , and (2) a denoising algorithm (pre-reconstruction). Training data was acquired from a scan of an 8-hole automotive fuel injector (sample 1) with a 165 µm nominal diameter per hole. For testing the accuracy of the workflow, a separate scan of a 6-hole side-mount injector (sample 2) was acquired. For both samples, the acquired X-ray projections (or radiographs) were binned down to 10X such as to simulate faster scans. For training and testing workflows, the full exposure scans (baseline) were used as target and the shorter exposure scans as inputs to the deep learning models. To determine loss of surface accuracy from the baseline case, a metric is formulated (in micrometers) and the trends are reported for when the total measurement time was reduced by up to 10X (up to 0.6 hours, using only 360 projections). We report that scan times can be reduced by over 10X while retaining the limiting the resolution loss to under 1 micrometer.

show abstract

3d Autoencoders For Feature Extraction In X-Ray Tomography

Cited by 7 publications

References 18 publications

Real-time streaming tomographic reconstruction with on-demand data capturing and 3D zooming to regions of interest

Real-time streaming tomographic reconstruction with on-demand data capturing and 3D zooming to regions of interest

Ultra-Lightweight Fast Anomaly Detectors for Industrial Applications

Deep Learning for improving the efficiency of dimensional measurement workflows with high-resolution X-ray computed tomography

Contact Info

Product

Resources

About