X-ray tomography reconstruction with machine learning: a study focusing on accuracy and fidelity

Author(s):  
Jiayong Zhang ◽  
Mingyuan Ge ◽  
Thomas Flynn ◽  
Sandeep Mittal
2019 ◽  
Author(s):  
Carianne Martinez ◽  
John P. Korbin ◽  
Kevin Matthew Potter ◽  
Emily Donahue ◽  
Jeremy David Gamet ◽  
...  

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.


2021 ◽  
Vol 143 (10) ◽  
pp. 3779-3793
Author(s):  
Paula C. Ortet ◽  
Samantha N. Muellers ◽  
Lauren A. Viarengo-Baker ◽  
Kristina Streu ◽  
Blair R. Szymczyna ◽  
...  

2021 ◽  
pp. 115152
Author(s):  
Mahbubunnabi Tamal ◽  
Maha Alshammari ◽  
Meernah Alabdullah ◽  
Rana Hourani ◽  
Hossain Abu Alola ◽  
...  

Author(s):  
Ali Guven ◽  
Imam Samil Yetik ◽  
Ahmet Culhaoglu ◽  
Kaan Orhan ◽  
Mehmet Kilicarslan Kilicarslan
Keyword(s):  

Nature ◽  
2021 ◽  
Author(s):  
Stefanie Warnat-Herresthal ◽  
◽  
Hartmut Schultze ◽  
Krishnaprasad Lingadahalli Shastry ◽  
Sathyanarayanan Manamohan ◽  
...  

AbstractFast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.


Author(s):  
R Pattnaik ◽  
K Sharma ◽  
K Alabarta ◽  
D Altamirano ◽  
M Chakraborty ◽  
...  

Abstract Low Mass X-ray binaries (LMXBs) are binary systems where one of the components is either a black hole or a neutron star and the other is a less massive star. It is challenging to unambiguously determine whether a LMXB hosts a black hole or a neutron star. In the last few decades, multiple observational works have tried, with different levels of success, to address this problem. In this paper, we explore the use of machine learning to tackle this observational challenge. We train a random forest classifier to identify the type of compact object using the energy spectrum in the energy range 5-25 keV obtained from the Rossi X-ray Timing Explorer archive. We report an average accuracy of 87±13% in classifying the spectra of LMXB sources. We further use the trained model for predicting the classes for LMXB systems with unknown or ambiguous classification. With the ever-increasing volume of astronomical data in the X-ray domain from present and upcoming missions (e.g., SWIFT, XMM-Newton, XARM, ATHENA, NICER), such methods can be extremely useful for faster and robust classification of X-ray sources and can also be deployed as part of the data reduction pipeline.


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