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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Fine-tuning TrailMap: The utility of transfer learning to improve the performance of deep learning in axon segmentation of light-sheet microscopy images

Light-sheet microscopy has made possible the 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual undertaking. Machine learning methods promise the possibility of automating this process. This study seeks to advance the performance of prior models through optimizing transfer learning. We fine-tuned the existing TrailMap model using expert-labeled data from noradrenergic axonal structures in the mouse brain. By changing the cross-entropy weights and using augmentation, we demonstrate a generally improved adjusted F1-score over using the originally trained TrailMap model within our test datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Data-driven Mapping of the Mouse Connectome: The utility of transfer learning to improve the performance of deep learning models performing axon segmentation on light-sheet microscopy images

Light sheet microscopy has made possible the high temporal and spatial 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual process. Machine learning methods promise the possibility of automating this process. This study seeks to advance the performance of prior models through the application of refinements such as transfer learning.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2000

Electron and Proton Auroral Dynamics

Data from the Wide-band Imaging Camera (WIC) sensitive to far ultraviolet auroras and from the Spectrographic Imager (SI) channel SI12, sensitive to proton precipitation induced Lyman alpha were analyzed during a high altitude orbit segment of the IMAGE spacecraft. This segment began during the expansive phase of a substorm. The aurora changed into a double oval configuration, consisting of a set of discrete pole-ward forms and a separate diffuse auroral oval equatorwards, Although IMF Bz was strongly southward considerable activity could be seen poleward of the discrete auroras in the region that was considered to be the polar cap. The SI12 Doppler shifted Lyman alpha signature of precipitating protons show that the proton aurora is on the equatorward side of the diffuse aurora. In the following several hours the IMF Bz field changed signed. Although the general character of the proton and electron aurora did not change, the dayside aurora moved equatorward when the Bz was negative and more bright aurora was seen in the central polar cap during periods of positive Bz.

Mende, S. B.↗
NASA NTRS2006

Achromatic shearing phase sensor for generating images indicative of measure(s) of alignment between segments of a segmented telescope's mirrors

An achromatic shearing phase sensor generates an image indicative of at least one measure of alignment between two segments of a segmented telescope's mirrors. An optical grating receives at least a portion of irradiance originating at the segmented telescope in the form of a collimated beam and the collimated beam into a plurality of diffraction orders. Focusing optics separate and focus the diffraction orders. Filtering optics then filter the diffraction orders to generate a resultant set of diffraction orders that are modified. Imaging optics combine portions of the resultant set of diffraction orders to generate an interference pattern that is ultimately imaged by an imager.

Stahl, H. Philip↗
NASA NTRS2011

Material Characterization and Geometric Segmentation of a Composite Structure Using Microfocus X-Ray Computed Tomography Image-Based Finite Element Modeling

This study utilizes microfocus x-ray computed tomography (CT) slice sets to model and characterize the damage locations and sizes in thermal protection system materials that underwent impact testing. ScanIP/FE software is used to visualize and process the slice sets, followed by mesh generation on the segmented volumetric rendering. Then, the local stress fields around several of the damaged regions are calculated for realistic mission profiles that subject the sample to extreme temperature and other severe environmental conditions. The resulting stress fields are used to quantify damage severity and make an assessment as to whether damage that did not penetrate to the base material can still result in catastrophic failure of the structure. It is expected that this study will demonstrate that finite element modeling based on an accurate three-dimensional rendered model from a series of CT slices is an essential tool to quantify the internal macroscopic defects and damage of a complex system made out of thermal protection material. Results obtained showing details of segmented images; three-dimensional volume-rendered models, finite element meshes generated, and the resulting thermomechanical stress state due to impact loading for the material are presented and discussed. Further, this study is conducted to exhibit certain high-caliber capabilities that the nondestructive evaluation (NDE) group at NASA Glenn Research Center can offer to assist in assessing the structural durability of such highly specialized materials so improvements in their performance and capacities to handle harsh operating conditions can be made.

Abdul-Aziz, Ali↗
DOE OSTI2023

EDS-PhaSe: Phase Segmentation and Analysis from EDS Elemental Map Images Using Markers of Elemental Segregation

Scanning electron microscopy (SEM), combined with energy-dispersive spectroscopy (EDS), is an extensively used technique for in-depth microstructural analysis. Here, we present the EDS-Phase Segmentation (EDS-PhaSe) tool that enables phase segmentation and phase analysis using the EDS elemental map images. Importantly, it converts the EDS map images into estimated composition maps for calculating markers of selective elemental redistribution in the scanned area and creates a phase-segmented micrograph while providing approximate fraction and composition of each identified phase. EDS-PhaSe offers two unique advantages. Firstly, it enables the direct processing of EDS elemental map images without requiring any raw or proprietary data/software, thereby allowing the analysis of EDS results available in the published literature as images. Secondly, it enables segmentation and analysis of phases even when the phase contrast is missing in backscattered micrographs, assisting in correlating the XRD and SEM-EDS data as shown in this work for a AlCoCrFeNi high-entropy alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2014

SpArcFiRe: Scalable automated detection of spiral galaxy arm segments

Given an approximately centered image of a spiral galaxy, we describe an entirely automated method that finds, centers, and sizes the galaxy (possibly masking nearby stars and other objects if necessary in order to isolate the galaxy itself) and then automatically extracts structural information about the spiral arms. For each arm segment found, we list the pixels in that segment, allowing image analysis on a per-arm-segment basis. We also perform a least-squares fit of a logarithmic spiral arc to the pixels in that segment, giving per-arc parameters, such as the pitch angle, arm segment length, location, etc. The algorithm takes about one minute per galaxies, and can easily be scaled using parallelism. We have run it on all ∼644,000 Sloan objects that are larger than 40 pixels across and classified as 'galaxies'. We find a very good correlation between our quantitative description of a spiral structure and the qualitative description provided by Galaxy Zoo humans. Our objective, quantitative measures of structure demonstrate the difficulty in defining exactly what constitutes a spiral 'arm', leading us to prefer the term 'arm segment'. We find that pitch angle often varies significantly segment-to-segment in a single spiral galaxy, making it difficult to define the pitch angle for a single galaxy. We demonstrate how our new database of arm segments can be queried to find galaxies satisfying specific quantitative visual criteria. For example, even though our code does not explicitly find rings, a good surrogate is to look for galaxies having one long, low-pitch-angle arm—which is how our code views ring galaxies. SpArcFiRe is available at http://sparcfire.ics.uci.edu.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

A Machine Learning Approach to Quantitative Analysis of Enamel Microstructure from Scanning Electron Microscopy Images

Dental enamel, the outermost tissue of mammalian teeth, must withstand a lifetime of wear and cyclic contact. To meet this demand, enamel possesses a combination of high hardness and resistance to fracture, properties that are typically mutually exclusive. The impressive damage tolerance has been attributed largely to decussation of the enamel rods, the principal unit of its microstructure. As such, enamel is inspiring the design of next‐generation structural materials. However, quantitative descriptions of the decussated enamel rod microstructure remain limited due to challenges encountered in applying computed tomography and in acquiring quality images appropriate for traditional digital processing methods. Here, a machine learning segmentation method is applied to images of the enamel obtained using scanning electron microscopy to support quantitative analysis of the microstructure. A pretrained convolutional neural network is used to expand the input training image dataset to allow the training of a random forest classifier, which ultimately segments the image with a very small training set ( n = 3 images). A validation of this segmentation method is presented, in addition to its application to calculate relevant microstructural parameters for images of tooth enamel from selected mammalian species. The methodology applied here is equally applicable to other hard tissues.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Automatic coronary calcium scoring using noncontrast and contrast CT images

Purpose: Calcium scoring is widely used to assess the risk of coronary heart disease (CHD). Accurate coronary artery calcification detection in noncontrast CT image is a prerequisite step for coronary calcium scoring. Currently, calcified lesions in the coronary arteries are manually identified by radiologists in clinical practice. Thus, in this paper, a fully automatic calcium scoring method was developed to alleviate the work load of the radiologists or cardiologists. Methods: The challenge of automatic coronary calcification detection is to discriminate the calcification in the coronary arteries from the calcification in the other tissues. Since the anatomy of coronary arteries is difficult to be observed in the noncontrast CT images, the contrast CT image of the same patient is used to extract the regions of the aorta, heart, and coronary arteries. Then, a patient-specific region-of-interest (ROI) is generated in the noncontrast CT image according to the segmentation results in the contrast CT image. This patient-specific ROI focuses on the regions in the neighborhood of coronary arteries for calcification detection, which can eliminate the calcifications in the surrounding tissues. A support vector machine classifier is applied finally to refine the results by removing possible image noise. Furthermore, the calcified lesions in the noncontrast images belonging to the different main coronary arteries are identified automatically using the labeling results of the extracted coronary arteries. Results: Forty datasets from four different CT machine vendors were used to evaluate their algorithm, which were provided by the MICCAI 2014 Coronary Calcium Scoring (orCaScore) Challenge. The sensitivity and positive predictive value for the volume of detected calcifications are 0.989 and 0.948. Only one patient out of 40 patients had been assigned to the wrong risk category defined according to Agatston scores (0, 1–100, 101–300, >300) by comparing with the ground truth. Conclusions: The calcified lesions in the noncontrast CT images can be detected automatically by using the segmentation results of the aorta, heart, and coronary arteries obtained in the contrast CT images with a very high accuracy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Cold Cap Bubble Topology

The feed composition of a high level nuclear waste (HLW) glass melter establishes the overall melting rate by influencing the thermo-physical and morphological properties of a relatively insulating cold cap layer over the molten phase. Data from X ray tomography of laboratory scale quenched cold caps provide insight into the distribution and morphology of bubbles, collectively known as primary foam, within this layer for various feed compositions at temperatures between 600°C and 1040°C. The images were digitally segmented to capture features of interest, then processed to obtain statistics that characterize the cold cap structure, including total pellet volume, void fraction, bubble radius distribution and morphology as a function of temperature. These statistics will be used to analyze the melting behavior of the waste glass as a function of silicate content. This temperature-dependent morphological data can be used in future investigations to synthetically generate cold cap structures for use in models of heat transfer through the upper layer of a HLW glass melter, with emphasis on nuclear waste remediation application at the Hanford Site in Washington, USA.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRSDate not supplied

The Porous Microstructure Analysis (PuMA) software

The open-source Porous Microstructure Analysis (PuMA) software was created to offer an efficient framework for determining material properties from 3D microstructures. Its development was inspired by progress in X-ray microtomography, an imaging technology that captures the internal structure of materials in 3D, and even in a 4D temporal context. Over recent years, this method has transformed the domain of materials science due to its capability to non-destructively examine material microstructures while presenting digital data about their geometrical details. It has provided insights into materials relevant to several NASA missions, including heatshields, parachute fabrics, meteorites, and other advanced composites. PuMA, in its current version 3, delivers an array of features, spanning from basic geometric insights of a microstructure to intricate anisotropic thermo-elastic and chemical behavior. Specifically, the software evaluates morphological attributes (specific surface area, volume fractions, mean intercept lengths, orientation) and physical characteristics (conductivity, elasticity, permeability, and tortuosity). Additionally, it can model material degradation processes, such as oxidation and surface chemistry interactions. The software can generate synthetic microstructures, from straightforward geometrical designs to intricate woven and non-woven geometries. Coupling material generation and characterization enables parametric studies and sensitivity analysis to optimize the microstructural performance and inform design decisions and reliability assessment based on uncertainty quantification. A recent addition to PuMA includes the TomoSAM plugin, devised to incorporate the cutting-edge Segment Anything Model (SAM) into our image segmentation workflow. SAM is a promptable deep learning model that can identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography and other imaging techniques, which would otherwise require a laborious manual segmentation process.

Tomography↗
NASA NTRS1999

Brain segmentation and the generation of cortical surfaces

This paper describes methods for white matter segmentation in brain images and the generation of cortical surfaces from the segmentations. We have developed a system that allows a user to start with a brain volume, obtained by modalities such as MRI or cryosection, and constructs a complete digital representation of the cortical surface. The methodology consists of three basic components: local parametric modeling and Bayesian segmentation; surface generation and local quadratic coordinate fitting; and surface editing. Segmentations are computed by parametrically fitting known density functions to the histogram of the image using the expectation maximization algorithm [DLR77]. The parametric fits are obtained locally rather than globally over the whole volume to overcome local variations in gray levels. To represent the boundary of the gray and white matter we use triangulated meshes generated using isosurface generation algorithms [GH95]. A complete system of local parametric quadratic charts [JWM+95] is superimposed on the triangulated graph to facilitate smoothing and geodesic curve tracking. Algorithms for surface editing include extraction of the largest closed surface. Results for several macaque brains are presented comparing automated and hand surface generation. Copyright 1999 Academic Press.

NASA Discipline Neuroscience↗
DOE OSTI2013

Combinational pixel-by-pixel and object-level classifying, segmenting, and agglomerating in performing quantitative image analysis that distinguishes between healthy non-cancerous and cancerous cell nuclei and delineates nuclear, cytoplasm, and stromal material objects from stained biological tissue materials

Quantitative object and spatial arrangement-level analysis of tissue are detailed using expert (pathologist) input to guide the classification process. A two-step method is disclosed for imaging tissue, by classifying one or more biological materials, e.g. nuclei, cytoplasm, and stroma, in the tissue into one or more identified classes on a pixel-by-pixel basis, and segmenting the identified classes to agglomerate one or more sets of identified pixels into segmented regions. Typically, the one or more biological materials comprises nuclear material, cytoplasm material, and stromal material. The method further allows a user to markup the image subsequent to the classification to re-classify said materials. The markup is performed via a graphic user interface to edit designated regions in the image.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Metal Artifact Reduction for CT-based Luggage Screening

This report describes research on active problems in computed tomography (CT) scanning applications. The problems are metal artifact reduction in CT images for medical imaging and luggage screening, and methods to evaluate segmentation of CT images. The research forms the basis of a PhD dissertation topic at UCSD, and is sponsored by LLNL.

42 ENGINEERING↗
NASA NTRS1992

Visual specification of robot motion

The authors describe the use of stereo pairs of images to specify robot motion. The experimental setup includes a SUN workstation, a PUMA 560 robot, and an Imaging 151 vision system. An X-window environment displays stereo images of the work scene. Image processing is performed to extract linear edge segments from the images and the results are displayed on screen. Using a pointing device, the user selects a group of edges from the object relevant to the task. The 3D structure of this group of features is found by stereo triangulation and they can be displayed in 3D from any point of view. A viewpoint orthogonal to the plane defined by these 3D edges is used to specify the robot position relative to object position. The actual robot will then be moved to the specified position.

Shiu, Y. C.↗
DOE OSTI2014

TH-E-17A-06: Anatomical-Adaptive Compressed Sensing (AACS) Reconstruction for Thoracic 4-Dimensional Cone-Beam CT

Purpose: The Feldkamp-Davis-Kress (FDK) algorithm currently used for clinical thoracic 4-dimensional (4D) cone-beam CT (CBCT) reconstruction suffers from noise and streaking artifacts due to projection under-sampling. Compressed sensing theory enables reconstruction of under-sampled datasets via total-variation (TV) minimization, but TV-minimization algorithms such as adaptive-steepest-descent-projection-onto-convex-sets (ASD-POCS) often converge slowly and are prone to over-smoothing anatomical details. These disadvantages can be overcome by incorporating general anatomical knowledge via anatomy segmentation. Based on this concept, we have developed an anatomical-adaptive compressed sensing (AACS) algorithm for thoracic 4D-CBCT reconstruction. Methods: AACS is based on the ASD-POCS framework, where each iteration consists of a TV-minimization step and a data fidelity constraint step. Prior to every AACS iteration, four major thoracic anatomical structures - soft tissue, lungs, bony anatomy, and pulmonary details - were segmented from the updated solution image. Based on the segmentation, an anatomical-adaptive weighting was applied to the TV-minimization step, so that TV-minimization was enhanced at noisy/streaky regions and suppressed at anatomical structures of interest. The image quality and convergence speed of AACS was compared to conventional ASD-POCS using an XCAT digital phantom and a patient scan. Results: For the XCAT phantom, the AACS image represented the ground truth better than the ASD-POCS image, giving a higher structural similarity index (0.93 vs. 0.84) and lower absolute difference (1.1*10{sup 4} vs. 1.4*10{sup 4}). For the patient case, while both algorithms resulted in much less noise and streaking than FDK, the AACS image showed considerably better contrast and sharpness of the vessels, tumor, and fiducial marker than the ASD-POCS image. In addition, AACS converged over 50% faster than ASD-POCS in both cases. Conclusions: The proposed AACS algorithm was shown to reconstruct thoracic 4D-CBCT images more accurately and with faster convergence compared to ASD-POCS. The superior image quality and rapid convergence makes AACS promising for future clinical use.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Measurement of hybrid rocket solid fuel regression rate for a slab burner using deep learning

This study presents an imaging-based deep learning tool to measure the fuel regression rate in a 2D slab burner experiment for hybrid rocket fuels. The slab burner experiment is designed to verify mechanistic models of reacting boundary layer combustion in hybrid rockets by the measurement of fuel regression rates. A DSLR camera with a high intensity flash is used to capture images throughout the burn and the images are then used to find the fuel boundary to calculate the regression rate. A U-net convolutional neural network architecture is explored to segment the fuel from the experimental images. Here, a Monte-Carlo Dropout process is used to quantify the regression rate uncertainty produced from the network. The U-net computed regression rates are compared with values from other techniques from literature and show error less than 10%. An oxidizer flux dependency study is performed and shows the U-net predictions of regression rates are accurate and independent of the oxidizer flux, when the images in the training set are not over-saturated. Training with monochrome images is explored and is not successful at predicting the fuel regression rate from images with high noise. The network is superior at filtering out noise introduced by soot, pitting, and wax deposition on the chamber glass as well as the flame when compared to traditional image processing techniques, such as threshold binary conversion and spatial filtering. U-net consistently provides low error image segmentations to allow accurate computation of the regression rate of the fuel.

42 ENGINEERING↗
DOE OSTI2015

A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy

Purpose: To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. Methods: Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to the planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation–maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the “ground truth” for quantitative evaluation. Results: The median multichannel segmented GTV of the primary tumor was 15.7 cm{sup 3} (range, 6.6–44.3 cm{sup 3}), while the PET segmented GTV was 10.2 cm{sup 3} (range, 2.8–45.1 cm{sup 3}). The median physician-defined GTV was 22.1 cm{sup 3} (range, 4.2–38.4 cm{sup 3}). The median difference between the multichannel segmented and physician-defined GTVs was −10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was −19.2%, showing a statistically significant difference (p-value =0.0037). The median Dice similarity coefficient between the multichannel segmented and physician-defined GTVs was 0.75 (range, 0.55–0.84), and the median sensitivity and positive predictive value between them were 0.76 and 0.81, respectively. Conclusions: The authors developed an automated multimodality segmentation algorithm for tumor volume delineation and validated this algorithm for head and neck cancer radiotherapy. The multichannel segmented GTV agreed well with the physician-defined GTV. The authors expect that their algorithm will improve the accuracy and consistency in target definition for radiotherapy.

60 APPLIED LIFE SCIENCES↗