8,312 research outputs found
Software for full-color 3D reconstruction of the biological tissues internal structure
A software for processing sets of full-color images of biological tissue
histological sections is developed. We used histological sections obtained by
the method of high-precision layer-by-layer grinding of frozen biological
tissues. The software allows restoring the image of the tissue for an arbitrary
cross-section of the tissue sample. Thus, our method is designed to create a
full-color 3D reconstruction of the biological tissue structure. The resolution
of 3D reconstruction is determined by the quality of the initial histological
sections. The newly developed technology available to us provides a resolution
of up to 5 - 10 {\mu}m in three dimensions.Comment: 11 pages, 8 figure
Finding Nano-\"Otzi: Semi-Supervised Volume Visualization for Cryo-Electron Tomography
Cryo-Electron Tomography (cryo-ET) is a new 3D imaging technique with
unprecedented potential for resolving submicron structural detail. Existing
volume visualization methods, however, cannot cope with its very low
signal-to-noise ratio. In order to design more powerful transfer functions, we
propose to leverage soft segmentation as an explicit component of visualization
for noisy volumes. Our technical realization is based on semi-supervised
learning where we combine the advantages of two segmentation algorithms. A
first weak segmentation algorithm provides good results for propagating sparse
user provided labels to other voxels in the same volume. This weak segmentation
algorithm is used to generate dense pseudo labels. A second powerful
deep-learning based segmentation algorithm can learn from these pseudo labels
to generalize the segmentation to other unseen volumes, a task that the weak
segmentation algorithm fails at completely. The proposed volume visualization
uses the deep-learning based segmentation as a component for segmentation-aware
transfer function design. Appropriate ramp parameters can be suggested
automatically through histogram analysis. Finally, our visualization uses
gradient-free ambient occlusion shading to further suppress visual presence of
noise, and to give structural detail desired prominence. The cryo-ET data
studied throughout our technical experiments is based on the highest-quality
tilted series of intact SARS-CoV-2 virions. Our technique shows the high impact
in target sciences for visual data analysis of very noisy volumes that cannot
be visualized with existing techniques
Capturing Nucleation at 4D Atomic Resolution
Nucleation plays a critical role in many physical and biological phenomena
ranging from crystallization, melting and evaporation to the formation of
clouds and the initiation of neurodegenerative diseases. However, nucleation is
a challenging process to study in experiments especially in the early stage
when several atoms/molecules start to form a new phase from its parent phase.
Here, we advance atomic electron tomography to study early stage nucleation at
4D atomic resolution. Using FePt nanoparticles as a model system, we reveal
that early stage nuclei are irregularly shaped, each has a core of one to few
atoms with the maximum order parameter, and the order parameter gradient points
from the core to the boundary of the nucleus. We capture the structure and
dynamics of the same nuclei undergoing growth, fluctuation, dissolution,
merging and/or division, which are regulated by the order parameter
distribution and its gradient. These experimental observations differ from
classical nucleation theory (CNT) and to explain them we propose the order
parameter gradient (OPG) model. We show the OPG model generalizes CNT and
energetically favours diffuse interfaces for small nuclei and sharp interfaces
for large nuclei. We further corroborate this model using molecular dynamics
simulations of heterogeneous and homogeneous nucleation in liquid-solid phase
transitions of Pt. We anticipate that the OPG model is applicable to different
nucleation processes and our experimental method opens the door to study the
structure and dynamics of materials with 4D atomic resolution.Comment: 42 pages, 5 figures, 12 supplementary figures and one supplementary
tabl
Machine Learning on Neutron and X-Ray Scattering
Neutron and X-ray scattering represent two state-of-the-art materials
characterization techniques that measure materials' structural and dynamical
properties with high precision. These techniques play critical roles in
understanding a wide variety of materials systems, from catalysis to polymers,
nanomaterials to macromolecules, and energy materials to quantum materials. In
recent years, neutron and X-ray scattering have received a significant boost
due to the development and increased application of machine learning to
materials problems. This article reviews the recent progress in applying
machine learning techniques to augment various neutron and X-ray scattering
techniques. We highlight the integration of machine learning methods into the
typical workflow of scattering experiments. We focus on scattering problems
that faced challenge with traditional methods but addressable using machine
learning, such as leveraging the knowledge of simple materials to model more
complicated systems, learning with limited data or incomplete labels,
identifying meaningful spectra and materials' representations for learning
tasks, mitigating spectral noise, and many others. We present an outlook on a
few emerging roles machine learning may play in broad types of scattering and
spectroscopic problems in the foreseeable future.Comment: 56 pages, 12 figures. Feedback most welcom
Algorithmic and infrastructural software development for cryo electron tomography
Many Cryo Electron Microscopy (cryoEM) software packages have accumulated significant technical debts over the years, resulting in overcomplicated codebases that are costly to maintain and that slow down development. In this thesis, we advocate for the development of open-source cryoEM core libraries as a solution to this debt and with the ultimate goal of improving the developer and user experience.
First, a brief summary of cryoEM is presented, with an emphasis on projection algorithms and tomography. Second, the requirements of modern and future cryoEM image processing are discussed. Third, a new experimental cryoEM core library written in modern C++ is introduced. This library prioritises performance and code reusability, and is designed around a few core functions which offers an efficient model to manipulate multidimensional arrays at an index-wise and element-wise level. C++ template metaprogramming allowed us to develop modular and transparent compute backends, that provide great CPU and GPU performance, unified in an easy to use interface. Fourth, new projection algorithms will be described, notably a grid-driven approach to accurately insert and sample central slices in 3-dimensional (3d) Fourier space. A Fourier-based fused backward-forward projection, further improving the computational efficiency and accuracy of reprojections, will also be presented. Fifth, and as part of our efforts to test and showcase the library, we have started to implement a tilt series alignment package that gathers existing and new techniques into an automated pipeline. The current program first estimates the per-tilt translations and specimen stage rotation using a coarse alignment based on cosine stretching. It then fits the Thon rings of each tilt image as part of a global optimization to estimate the specimen inclination. Finally, we are using our Fourier-based fused reprojection to efficiently refine the per-tilt translations, and are starting to explore ways that would allow us to refine the per-tilt stage rotations
Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion
Computed Tomography (CT) reconstruction is a fundamental component to a wide
variety of applications ranging from security, to healthcare. The classical
techniques require measuring projections, called sinograms, from a full
180 view of the object. This is impractical in a limited angle
scenario, when the viewing angle is less than 180, which can occur due
to different factors including restrictions on scanning time, limited
flexibility of scanner rotation, etc. The sinograms obtained as a result, cause
existing techniques to produce highly artifact-laden reconstructions. In this
paper, we propose to address this problem through implicit sinogram completion,
on a challenging real world dataset containing scans of common checked-in
luggage. We propose a system, consisting of 1D and 2D convolutional neural
networks, that operates on a limited angle sinogram to directly produce the
best estimate of a reconstruction. Next, we use the x-ray transform on this
reconstruction to obtain a "completed" sinogram, as if it came from a full
180 measurement. We feed this to standard analytical and iterative
reconstruction techniques to obtain the final reconstruction. We show with
extensive experimentation that this combined strategy outperforms many
competitive baselines. We also propose a measure of confidence for the
reconstruction that enables a practitioner to gauge the reliability of a
prediction made by our network. We show that this measure is a strong indicator
of quality as measured by the PSNR, while not requiring ground truth at test
time. Finally, using a segmentation experiment, we show that our reconstruction
preserves the 3D structure of objects effectively.Comment: Spotlight presentation at CVPR 201
Automated Segmentation of Large Image Datasets using Artificial Intelligence for Microstructure Characterisation, Damage Analysis and High-Throughput Modelling Input
Many properties of commonly used materials are driven by their
microstructure, which can be influenced by the composition and manufacturing
processes. To optimise future materials, understanding the microstructure is
critically important. Here, we present two novel approaches based on artificial
intelligence that allow the segmentation of the phases of a microstructure for
which simple numerical approaches, such as thresholding, are not applicable:
One is based on the nnU-Net neural network, and the other on generative
adversarial networks (GAN). Using large panoramic scanning electron microscopy
images of dual-phase steels as a case study, we demonstrate how both methods
effectively segment intricate microstructural details, including martensite,
ferrite, and damage sites, for subsequent analysis. Either method shows
substantial generalizability across a range of image sizes and conditions,
including heat-treated microstructures with different phase configurations. The
nnU-Net excels in mapping large image areas. Conversely, the GAN-based method
performs reliably on smaller images, providing greater step-by-step control and
flexibility over the segmentation process. This study highlights the benefits
of segmented microstructural data for various purposes, such as calculating
phase fractions, modelling material behaviour through finite element
simulation, and conducting geometrical analyses of damage sites and the local
properties of their surrounding microstructure.Comment: 37 pages, 24 figure
- …