Uncertainty Quantification for the Image Segmentation Task [Slides]
This report provides information about image segmentation, including challenges, experiment results and future efforts.
97 MATHEMATICS AND COMPUTING↗
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Results for “Bayesian imaging”
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BEACON—automated aberration correction for scanning transmission electron microscopy using Bayesian optimization
Aberration correction is an important aspect of modern high-resolution scanning transmission electron microscopy. Most methods of aligning aberration correctors require specialized sample regions and are unsuitable for fine-tuning aberrations without interrupting on-going experiments. Here, we present an automated method of correcting first- and second-order aberrations called BEACON, which uses Bayesian optimization of the normalized image variance to efficiently determine the optimal corrector settings. We demonstrate its use on gold nanoparticles and a hafnium dioxide thin film showing its versatility in nano- and atomic-scale experiments. BEACON can correct all first- and second-order aberrations simultaneously to achieve an initial alignment and first- and second-order aberrations independently for fine alignment. Ptychographic reconstructions are used to demonstrate an improvement in probe shape and a reduction in the target aberration.
techniques and instrumentation↗
Measurement of charge state distributions using a scintillation screen
Absolute cross sections measured using electromagnetic devices to separate and detect heavy recoiling ions need to be corrected for charge state fractions. Accurate prediction of charge state distributions using theoretical models is not always a possibility, especially in energy and mass regions where data is sparse. As such, it is often necessary to measure charge state fractions directly. In this paper we present a novel method of using a scintillation screen along with a CMOS camera to image the charge dispersed beam after a set of magnetic dipoles. A measurement of the charge state distribution for 88 Sr passing through a natural carbon foil is performed. Using a Bayesian model to extract statistically meaningful uncertainties from these images, we find agreement between the new method and a more traditional method using Faraday cups. Additional future work is need to better understand systematic uncertainties. Our technique offers a viable method to measure charge state distributions.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Bayesian merging of numerical modeling and remote sensing for saltwater intrusion quantification in the Vietnamese Mekong Delta
Saltwater intrusion has become one of the most concerning issues in the Vietnamese Mekong Delta (VMD) due to its increasing impacts on agriculture and food security of Vietnam. Reliable estimation of salinity plays a crucial role to mitigate the impacts of saltwater intrusion. Here, this study developed a hybrid technique that merges satellite imagery with numerical simulations to improve the estimation of salinity in the VMD. The salinity derived from Landsat images and by numerical simulations was fused using the Bayesian inference technique. The results indicate that our technique significantly reduces the uncertainties and improves the accuracy of salinity estimates. The Nash–Sutcliffe coefficient is 0.74, which is much higher than that of numerical simulation (0.63) and Landsat estimation (0.6). The correlation coefficient between the ensemble and measured salinity is relatively high (0.88). The variance of the ensemble salinity errors (5.0 ppt 2 ) is lower than that of Landsat estimation (10.4 ppt 2 ) and numerical simulations (9.6 ppt 2 ). The proposed approach shows a great potential to combine multiple data sources of a variable of interest to improve its accuracy and reliability wherever these data are available.
54 ENVIRONMENTAL SCIENCES↗
Evaluation of Plasma Phosphorylated Tau217 for Differentiation Between Alzheimer Disease and Frontotemporal Lobar Degeneration Subtypes Among Patients With Corticobasal Syndrome
Plasma phosphorylated tau217 (p-tau217), a biomarker of Alzheimer disease (AD), is of special interest in corticobasal syndrome (CBS) because autopsy studies have revealed AD is the driving neuropathology in up to 40% of cases. This differentiates CBS from other 4-repeat tauopathy (4RT)–associated syndromes, such as progressive supranuclear palsy Richardson syndrome (PSP-RS) and nonfluent primary progressive aphasia (nfvPPA), where underlying frontotemporal lobar degeneration (FTLD) is typically the primary neuropathology. To validate plasma p-tau217 against positron emission tomography (PET) in 4RT-associated syndromes, especially CBS. This multicohort study with 6, 12, and 24-month follow-up recruited adult participants between January 2011 and September 2020 from 8 tertiary care centers in the 4RT Neuroimaging Initiative (4RTNI). All participants with CBS (n = 113), PSP-RS (n = 121), and nfvPPA (n = 39) were included; other diagnoses were excluded due to rarity (n = 29). Individuals with PET-confirmed AD (n = 54) and PET-negative cognitively normal control individuals (n = 59) were evaluated at University of California San Francisco. Operators were blinded to the cohort. Plasma p-tau217, measured by Meso Scale Discovery electrochemiluminescence, was validated against amyloid-β (Aβ) and flortaucipir (FTP) PET. Imaging analyses used voxel-based morphometry and bayesian linear mixed-effects modeling. Clinical biomarker associations were evaluated using longitudinal mixed-effect modeling. Of 386 participants, 199 (52%) were female, and the mean (SD) age was 68 (8) years. Plasma p-tau217 was elevated in patients with CBS with positive Aβ PET results (mean [SD], 0.57 [0.43] pg/mL) or FTP PET (mean [SD], 0.75 [0.30] pg/mL) to concentrations comparable to control individuals with AD (mean [SD], 0.72 [0.37]), whereas PSP-RS and nfvPPA showed no increase relative to control. Within CBS, p-tau217 had excellent diagnostic performance with area under the receiver operating characteristic curve (AUC) for Aβ PET of 0.87 (95% CI, 0.76-0.98; P < .001) and FTP PET of 0.93 (95% CI, 0.83-1.00; P < .001). At baseline, individuals with CBS-AD (n = 12), defined by a PET-validated plasma p-tau217 cutoff 0.25 pg/mL or greater, had increased temporoparietal atrophy at baseline compared to individuals with CBS-FTLD (n = 39), whereas longitudinally, individuals with CBS-FTLD had faster brainstem atrophy rates. Individuals with CBS-FTLD also progressed more rapidly on a modified version of the PSP Rating Scale than those with CBS-AD (mean [SD], 3.5 [0.5] vs 0.8 [0.8] points/year; P = .005). In this cohort study, plasma p-tau217 had excellent diagnostic performance for identifying Aβ or FTP PET positivity within CBS with likely underlying AD pathology. Plasma P-tau217 may be a useful and inexpensive biomarker to select patients for CBS clinical trials.
59 BASIC BIOLOGICAL SCIENCES↗
Characterizing and Mitigating Intraday Variability: Reconstructing Source Structure in Accreting Black Holes with mm-VLBI
The extraordinary physical resolution afforded by the Event Horizon Telescope has opened a window onto the astrophysical phenomena unfolding on horizon scales in two known black holes, M87* and Sgr A*. However, with this leap in resolution has come a new set of practical complications. Sgr A* exhibits intraday variability that violates the assumptions underlying Earth aperture synthesis, limiting traditional image reconstruction methods to short timescales and data sets with very sparse (u, v) coverage. We present a new set of tools to detect and mitigate this variability. We develop a data-driven, model-agnostic procedure to detect and characterize the spatial structure of intraday variability. This method is calibrated against a large set of mock data sets, producing an empirical estimator of the spatial power spectrum of the brightness fluctuations. We present a novel Bayesian noise modeling algorithm that simultaneously reconstructs an average image and statistical measure of the fluctuations about it using a parameterized form for the excess variance in the complex visibilities not otherwise explained by the statistical errors. These methods are validated using a variety of simulated data, including general relativistic magnetohydrodynamic simulations appropriate for Sgr A* and M87*. We find that the reconstructed source structure and variability are robust to changes in the underlying image model. We apply these methods to the 2017 EHT observations of M87*, finding evidence for variability across the EHT observing campaign. The variability mitigation strategies presented are widely applicable to very long baseline interferometry observations of variable sources generally, for which they provide a data-informed averaging procedure and natural characterization of inter-epoch image consistency.
79 ASTRONOMY AND ASTROPHYSICS↗
Automating STEM Aberration Correction via Bayesian Optimization
Multipole aberration correctors have given scanning transmission electron microscopes (STEM) the ability to produce high-quality, atomic-resolution images, enabling STEM to be a key tool in material sciences for characterizing the structure and composition of materials. However, the process of correcting these aberrations typically requires human input and is accomplished using scanned STEM or Ronchigram images at different focii and beam tilts. In this work, we demonstrate an automated on-sample aberration correction system using Bayesian Optimization. We have developed a Python-based server able to communicate with the CEOS DCOR aberration corrector and the Thermo Fischer microscope scripting interface. This server allows us to change aberrations, acquire images and perform basic image analysis.
47 OTHER INSTRUMENTATION↗
Deep Bayesian local crystallography
Abstract The advent of high-resolution electron and scanning probe microscopy imaging has opened the floodgates for acquiring atomically resolved images of bulk materials, 2D materials, and surfaces. This plethora of data contains an immense volume of information on materials structures, structural distortions, and physical functionalities. Harnessing this knowledge regarding local physical phenomena necessitates the development of the mathematical frameworks for extraction of relevant information. However, the analysis of atomically resolved images is often based on the adaptation of concepts from macroscopic physics, notably translational and point group symmetries and symmetry lowering phenomena. Here, we explore the bottom-up definition of structural units and symmetry in atomically resolved data using a Bayesian framework. We demonstrate the need for a Bayesian definition of symmetry using a simple toy model and demonstrate how this definition can be extended to the experimental data using deep learning networks in a Bayesian setting, namely rotationally invariant variational autoencoders.
36 MATERIALS SCIENCE↗
EcoBOT: an AI/ML enabled automated phenotyping capability for model plants
Introduction: Advances in automation and AI/ML offer new opportunities for plant science, including design, modeling, and analysis. This study aimed to develop an automated platform for researching small model plants under axenic conditions and integrate it with AI/ML tools. Methods: The EcoBOT platform was developed, which consists of sterile containers (EcoFABs) for growing plants and imaging for monitoring plant growth and health. Brachypodium distachyon was grown on the EcoBOT, and its response to nutrient limitation and copper stress was evaluated. Results: The results showed that Brachypodium distachyon grown in the EcoBOT maintained sterility and responded to nutrient limitation and copper stress. Analysis of over 6,500 root and shoot images revealed varying sensitivity and response rates to copper. Bayesian Optimization was used to improve model accuracies relating copper concentrations to plant biomass via sequential experiments, resulting in a >30% improvement. Discussion: The findings of this study demonstrate the potential of the EcoBOT platform for researching plant responses to environmental factors. Future experiments could focus on relating other chemical stresses and microbial interactions to create generalized models of plant responses.
AI image analysis↗
Source localization for neutron imaging systems using convolutional neural networks
The nuclear imaging system at the National Ignition Facility (NIF) is a crucial diagnostic for determining the geometry of inertial confinement fusion implosions. The geometry is reconstructed from a neutron aperture image via a set of reconstruction algorithms using an iterative Bayesian inference approach. An important step in these reconstruction algorithms is finding the fusion source location within the camera field-of-view. Currently, source localization is achieved via an iterative optimization algorithm. In this paper, we introduce a machine learning approach for source localization. Specifically, we train a convolutional neural network to predict source locations given a neutron aperture image. We show that this approach decreases computation time by several orders of magnitude compared to the current optimization-based source localization while achieving similar accuracy on both synthetic data and a collection of recent NIF deuterium–tritium shots.
47 OTHER INSTRUMENTATION↗
Source localization for neutron imaging systems using convolutional neural networks
The nuclear imaging system at the National Ignition Facility (NIF) is a crucial diagnostic for determining the geometry of inertial confinement fusion implosions. The geometry is reconstructed from a neutron aperture image via a set of reconstruction algorithms using an iterative Bayesian inference approach. An important step in these reconstruction algorithms is finding the fusion source location within the camera field-of-view. Currently, source localization is achieved via an iterative optimization algorithm. In this paper, we introduce a machine learning approach for source localization. Specifically, we train a convolutional neural network to predict source locations given a neutron aperture image. We show that this approach decreases computation time by several orders of magnitude compared to the current optimization-based source localization while achieving similar accuracy on both synthetic data and a collection of recent NIF deuterium–tritium shots.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Posterior Regularized Bayesian Neural Network incorporating soft and hard knowledge constraints
Neural Networks (NNs) have been widely used in supervised learning due to their ability to model complex nonlinear patterns, often presented in high-dimensional data such as images and text. However, traditional NNs often lack the ability for uncertainty quantification. Bayesian NNs (BNNS) could help measure the uncertainty by considering the distributions of the NN model parameters. Besides, domain knowledge is commonly available and could improve the performance of BNNs if it can be appropriately incorporated. In this work, we propose a novel Posterior-Regularized Bayesian Neural Network (PR-BNN) model by incorporating different types of knowledge constraints, such as the soft and hard constraints, as a posterior regularization term. Furthermore, we propose to combine the augmented Lagrangian method and the existing BNN solvers for efficient inference. Furthermore, the experiments in simulation and two case studies about aviation landing prediction and solar energy output prediction have shown the knowledge constraints and the performance improvement of the proposed model over traditional BNNs without the constraints.
14 SOLAR ENERGY↗
Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference
Current ground-based cosmological surveys, such as the Dark Energy Survey (DES), are predicted to discover thousands of galaxy-scale strong lenses, while future surveys, such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST) will increase that number by 1-2 orders of magnitude. The large number of strong lenses discoverable in future surveys will make strong lensing a highly competitive and complementary cosmic probe. To leverage the increased statistical power of the lenses that will be discovered through upcoming surveys, automated lens analysis techniques are necessary. We present two Simulation-Based Inference (SBI) approaches for lens parameter estimation of galaxy-galaxy lenses. We demonstrate the successful application of Neural Posterior Estimation (NPE) to automate the inference of a 12-parameter lens mass model for DES-like ground-based imaging data. We compare our NPE constraints to a Bayesian Neural Network (BNN) and find that it outperforms the BNN, producing posterior distributions that are for the most part both more accurate and more precise; in particular, several source-light model parameters are systematically biased in the BNN implementation.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Uncertainty quantification and propagation in lithium-ion battery electrodes using bayesian convolutional neural networks
The complex nature of manufacturing processes stipulates electrodes to possess high variability with increased heterogeneity during production. X-ray computed tomography imaging has proved to be critical in visualizing the complicated stochastic particle distribution of as-manufactured electrodes in lithium-ion batteries. However, accurate prediction of their electrochemical performance necessitates precise evaluation of kinetic and transport properties from real electrodes. Image segmentation that characterizes voxels to particle/pore phase is often meticulous and fraught with subjectivity owing to a myriad of unconstrained choices and filter algorithms. Here we utilize a Bayesian convolutional neural network to tackle segmentation subjectivity and quantify its pertinent uncertainties. Otsu inter-variance and Blind/Referenceless Imaging Spatial Quality Evaluator are used to assess the relative image quality of grayscale tomograms, thus evaluating the uncertainty in the derived microstructural attributes. We analyze how image uncertainty is correlated with the uncertainties and magnitude of kinetic and transport properties of an electrode, further identifying pathways of uncertainty propagation within microstructural attributes. The coupled effect of spatial heterogeneity and microstructural anisotropy on the uncertainty quantification of transport parameters is also understood. This work demonstrates a novel methodology to extract microstructural descriptors from real electrode images through quantification of associated uncertainties and discerning the relative strength of their propagation, thus facilitating feedback to manufacturing processes from accurate image based electrochemical simulations.
25 ENERGY STORAGE↗
Machine learning enhanced characterization and optimization of photonic cured MAPbI 3 for efficient perovskite solar cells
Photonic curing (PC) can facilitate high-speed perovskite solar cell (PSC) manufacturing because it uses high-intensity light pulses to crystallize perovskite films in milliseconds. However, optimizing PC conditions is challenging due to its many variables, and using power conversion efficiency (PCE) as the optimization metric is both time-consuming and labor-intensive. This work presents a machine learning (ML) approach to optimize PC conditions for fabricating methylammonium lead iodide (MAPbI 3 ) films by quantitatively comparing their ultraviolet-visible (UV-vis) absorbance spectra to thermal annealed (TA) films using four similarity metrics. We perform Bayesian optimization coupled with Gaussian process regression (BO-GP) to minimize the similarity metrics. Refining PC conditions using active learning based on BO-GP models, we achieve a PC MAPbI3 film with an absorbance spectrum closely matching a TA reference film, which is further verified by its crystalline and morphological properties. Thus, we demonstrate that the UV-vis absorption spectrum can accurately proxy film quality. Additionally, we use an AI-based segmentation model for a more efficient grain size analysis. However, when we use the optimized PC condition to fabricate PSCs, we find that interaction between MAPbI 3 and the hole transport layer (HTL) during PC critically degrades the PSC performance. By adding a buffer layer between the HTL and MAPbI 3 , the optimized PC PSCs produce a champion PCE of 11.8%, comparable to the TA reference of 11.7%. Using UV-vis similarity metrics instead of device PCE as the objective in our BO-GP method accelerates the optimization of PC processing conditions for MAPbI 3 films.
14 SOLAR ENERGY↗
Evaluation of Deep Learning Model Architectures for Point-of-Care Ultrasound Diagnostics
Point-of-care ultrasound imaging is a critical tool for patient triage during trauma for diagnosing injuries and prioritizing limited medical evacuation resources. Specifically, an eFAST exam evaluates if there are free fluids in the chest or abdomen but this is only possible if ultrasound scans can be accurately interpreted, a challenge in the pre-hospital setting. In this effort, we evaluated the use of artificial intelligent eFAST image interpretation models. Widely used deep learning model architectures were evaluated as well as Bayesian models optimized for six different diagnostic models: pneumothorax (i) B- or (ii) M-mode, hemothorax (iii) B- or (iv) M-mode, (v) pelvic or bladder abdominal hemorrhage and (vi) right upper quadrant abdominal hemorrhage. Models were trained using images captured in 27 swine. Using a leave-one-subject-out training approach, the MobileNetV2 and DarkNet53 models surpassed 85% accuracy for each M-mode scan site. The different B-mode models performed worse with accuracies between 68% and 74% except for the pelvic hemorrhage model, which only reached 62% accuracy for all model architectures. These results highlight which eFAST scan sites can be easily automated with image interpretation models, while other scan sites, such as the bladder hemorrhage model, will require more robust model development or data augmentation to improve performance. With these additional improvements, the skill threshold for ultrasound-based triage can be reduced, thus expanding its utility in the pre-hospital setting.
47 OTHER INSTRUMENTATION↗
First Sagittarius A* Event Horizon Telescope Results. III. Imaging of the Galactic Center Supermassive Black Hole
We present the first event-horizon-scale images and spatiotemporal analysis of Sgr A* taken with the Event Horizon Telescope in 2017 April at a wavelength of 1.3 mm. Imaging of Sgr A* has been conducted through surveys over a wide range of imaging assumptions using the classical CLEAN algorithm, regularized maximum likelihood methods, and a Bayesian posterior sampling method. Different prescriptions have been used to account for scattering effects by the interstellar medium toward the Galactic center. Mitigation of the rapid intraday variability that characterizes Sgr A* has been carried out through the addition of a “variability noise budget” in the observed visibilities, facilitating the reconstruction of static full-track images. Our static reconstructions of Sgr A* can be clustered into four representative morphologies that correspond to ring images with three different azimuthal brightness distributions and a small cluster that contains diverse nonring morphologies. Based on our extensive analysis of the effects of sparse (u,v)-coverage, source variability, and interstellar scattering, as well as studies of simulated visibility data, we conclude that the Event Horizon Telescope Sgr A* data show compelling evidence for an image that is dominated by a bright ring of emission with a ring diameter of ∼50 μas, consistent with the expected “shadow” of a 4 × 10$^{6}$ M $_{⊙}$ black hole in the Galactic center located at a distance of 8 kpc.
79 ASTRONOMY AND ASTROPHYSICS↗
Automated scanning probe microscopy of combinatorial ferroelectric libraries: Gaussian-process-guided exploration and noise-aware experiment planning
Combinatorial materials libraries provide an efficient route for mapping composition–property relationships, but their broader impact depends on rapid, quantitative, and functionally relevant characterization. Scanning Probe Microscopy (SPM), including piezoresponse force microscopy (PFM), offers significant potential for quantitative, functionally relevant combi-library readouts. Here, we implement a fully automated SPM workflow for ferroelectric combinatorial libraries and benchmark Gaussian-process-based Bayesian optimization strategies for autonomous experiment planning. The workflow integrates automated probe motion, contact optimization, imaging, and dual amplitude resonance tracking-PFM spectroscopy, and uses scalarized spectroscopic observables to guide subsequent measurements. Stage motion, probe engagement, in-contact tuning, imaging, spectroscopy, and the choice of the next measurement location all proceed without human input. We demonstrate the approach on Sm-doped BiFeO 3 and Zn x Mg 1−x O libraries. By comparing vanilla Bayesian optimization with a measured-noise variant, we show that explicit treatment of local reproducibility can improve modeling of composition-dependent response when the measured variance is physically meaningful, but can also reduce robustness when variability is dominated by outliers or topographic artifacts. Furthermore, these results establish automated SPM as a bridge between combinatorial synthesis and quantitative functional characterization.
Liu, Yu [University of Tennessee, Knoxville, TN (U↗