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DOE OSTI2020

Risk-Informed Approach for Event Selection and Component Classification

Develop a report summarizing a structured approach for the incorporation of risk insights when making facility safety assessments and regulatory decisions is proposed to supplement the deterministic approach, with the following expected benefits: Increased confidence that key safety issues have been fully identified and addressed Better understanding of safety margins Facilitates more structured dialogue among international regulators Reduced reliance on “expert judgement” through a rigorous, pre-defined process with more predictable outcomes Proposed outcome is development of a report summarizing the structure and key considerations for applying this approach that: Is inclusive of advanced reactor technologies Provides for flexible implementation recognizing unique and varying sovereign regulatory structures Builds upon existing/current GIF safety approaches (e.g., GIF Basic Safety Approach, Integrated Safety Assessment Methodology)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2016

SU-G-BRC-13: Model Based Classification for Optimal Position Selection for Left-Sided Breast Radiotherapy: Free Breathing, DIBH, Or Prone

Purpose: There are clinical decision challenges to select optimal treatment positions for left-sided breast cancer patients—supine free breathing (FB), supine Deep Inspiration Breath Hold (DIBH) and prone free breathing (prone). Physicians often make the decision based on experiences and trials, which might not always result optimal OAR doses. We herein propose a mathematical model to predict the lowest OAR doses among these three positions, providing a quantitative tool for corresponding clinical decision. Methods: Patients were scanned in FB, DIBH, and prone positions under an IRB approved protocol. Tangential beam plans were generated for each position, and OAR doses were calculated. The position with least OAR doses is defined as the optimal position. The following features were extracted from each scan to build the model: heart, ipsilateral lung, breast volume, in-field heart, ipsilateral lung volume, distance between heart and target, laterality of heart, and dose to heart and ipsilateral lung. Principal Components Analysis (PCA) was applied to remove the co-linearity of the input data and also to lower the data dimensionality. Feature selection, another method to reduce dimensionality, was applied as a comparison. Support Vector Machine (SVM) was then used for classification. Thirtyseven patient data were acquired; up to now, five patient plans were available. K-fold cross validation was used to validate the accuracy of the classifier model with small training size. Results: The classification results and K-fold cross validation demonstrated the model is capable of predicting the optimal position for patients. The accuracy of K-fold cross validations has reached 80%. Compared to PCA, feature selection allows causal features of dose to be determined. This provides more clinical insights. Conclusion: The proposed classification system appeared to be feasible. We are generating plans for the rest of the 37 patient images, and more statistically significant results are to be presented.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

Intelligent feature selection techniques for pattern classification of Lamb wave signals

Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crosshole tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it’s never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes “line up” in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Cholangiographic Tumor Classification for Simple Patient Selection Prior to Hepatopancreatoduodenectomy for Cholangiocarcinoma

Background: Hepatopancreatoduodenectomy (HPD) is employed for patients with laterally advanced cholangiocarcinoma. However, the survival benefit of this extended approach remains controversial. The aim of this study is to identify a tumor feature benefiting from HPD from the standpoint of long-term survival. Patients and Methods: Patients with cholangiocarcinoma who underwent HPD with curative intent between 2001 and 2017 were retrospectively analyzed. Tumors were radiologically classified by preoperative cholangiogram. Diffuse type was defined as significant tumor/stricture located from the hilar to intrapancreatic duct; localized type was defined as tumor otherwise. Univariable and multivariable analyses were performed to identify prognostic indicators. Results: Of 100 study patients, 28 (28%) patients had diffuse tumor type, while the remaining 72 (72%) patients had localized tumors. The former group showed significantly longer lateral length (43 versus 22 mm, P < 0.001) and more frequent pancreatic invasion (50% versus 32%, P = 0.110), advanced T classification (64% versus 49%, P = 0.185), and nodal metastasis (57% versus 47%, P = 0.504), compared with the latter group. The survival for patients with diffuse tumor type was significantly worse than that for patients with localized tumor type, with 5-year survival rates of 59.0% versus 26.3%, respectively (P = 0.003). Multivariable analysis identified four independent factors deteriorating long-term survival: cholangiographic diffuse tumor (P = 0.021), higher age (P = 0.020), percutaneous biliary drainage (P = 0.007), and portal vein resection (P = 0.007). Conclusions: Presurgical cholangiographic classification, diffuse or localized type, is a tumor-related factor closely associated with survival probability; therefore, it may be a useful feature for patient selection prior to HPD for cholangiocarcinoma.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2013

A COMPREHENSIVE ANALYSIS OF FERMI GAMMA-RAY BURST DATA. III. ENERGY-DEPENDENT T {sub 90} DISTRIBUTIONS OF GBM GRBs AND INSTRUMENTAL SELECTION EFFECT ON DURATION CLASSIFICATION

The durations (T {sub 90}) of 315 gamma-ray bursts (GRBs) detected with Fermi/GBM (8-1000 keV) up to 2011 September are calculated using the Bayesian Block method. We compare the T {sub 90} distributions between this sample and those derived from previous/current GRB missions. We show that the T {sub 90} distribution of this GRB sample is bimodal, with a statistical significance level comparable to those derived from the BeppoSAX/GRBM sample and the Swift/BAT sample, but lower than that derived from the CGRO/BATSE sample. The short-to-long GRB number ratio is also much lower than that derived from the BATSE sample, i.e., 1:6.5 versus 1:3. We measure T {sub 90} in several bands, i.e., 8-15, 15-25, 25-50, 50-100, 100-350, and 350-1000 keV, to investigate the energy-dependence effect of the bimodal T {sub 90} distribution. It is found that the bimodal feature is well observed in the 50-100 and 100-350 keV bands, but is only marginally acceptable in the 25-50 keV and 350-1000 keV bands. The hypothesis of bimodality is confidently rejected in the 8-15 and 15-25 keV bands. The T {sub 90} distributions in these bands are roughly consistent with those observed by missions with similar energy bands. The parameter T {sub 90} as a function of energy follows T-bar {sub 90}{proportional_to}E{sup -0.20{+-}0.02} for long GRBs. Considering the erratic X-ray and optical flares, the duration of a burst would be even longer for most GRBs. Our results, together with the observed extended emission of some short GRBs, indicate that the central engine activity timescale would be much longer than T {sub 90} for both long and short GRBs and the observed bimodal T {sub 90} distribution may be due to an instrumental selection effect.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

The miniJPAS survey quasar selection – II. Machine learning classification with photometric measurements and uncertainties

Astrophysical surveys rely heavily on the classification of sources as stars, galaxies, or quasars from multiband photometry. Surveys in narrow-band filters allow for greater discriminatory power, but the variety of different types and redshifts of the objects present a challenge to standard template-based methods. In this work, which is part of a larger effort that aims at building a catalogue of quasars from the miniJPAS survey, we present a machine learning-based method that employs convolutional neural networks (CNNs) to classify point-like sources including the information in the measurement errors. We validate our methods using data from the miniJPAS survey, a proof-of-concept project of the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) collaboration covering ∼1 deg 2 of the northern sky using the 56 narrow-band filters of the J-PAS survey. Due to the scarcity of real data, we trained our algorithms using mocks that were purpose-built to reproduce the distributions of different types of objects that we expect to find in the miniJPAS survey, as well as the properties of the real observations in terms of signal and noise. We compare the performance of the CNNs with other well-established machine learning classification methods based on decision trees, finding that the CNNs improve the classification when the measurement errors are provided as inputs. The predicted distribution of objects in miniJPAS is consistent with the putative luminosity functions of stars, quasars, and unresolved galaxies. Our results are a proof of concept for the idea that the J-PAS survey will be able to detect unprecedented numbers of quasars with high confidence.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Selective Sampling for Sensor Type Classification in Buildings

A key barrier to applying any smart technology to a building is the requirement of locating and connecting to the necessary resources among the thousands of sensing and control points, i.e., the metadata mapping problem. Existing solutions depend on exhaustive manual annotation of sensor metadata --- a laborious, costly, and hardly scalable process. To reduce the amount of manual effort required, this paper presents a multi-oracle selective sampling framework to leverage noisy labels from information sources with unknown reliability such as existing buildings, which we refer to as weak oracles, for metadata mapping. This framework involves an interactive process, where a small set of sensor instances are progressively selected and labeled for it to learn how to aggregate the noisy labels as well as to predict sensor types. Two key challenges arise in designing the framework, namely, weak oracle reliability estimation and instance selection for querying. To address the first challenge, we develop a clustering-based approach for weak oracle reliability estimation to capitalize on the observation that weak oracles perform differently in different groups of instances. For the second challenge, we propose a disagreement-based query selection strategy to combine the potential effect of a labeled instance on both reducing classifier uncertainty and improving the quality of label aggregation. We evaluate our solution on a large collection of real-world building sensor data from 5 buildings with more than 11,000 sensors of 18 different types. The experiment results validate the effectiveness of our solution, which outperforms a set of state-of-the-art baselines.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2011

A novel computer-aided lung nodule detection system for CT images

Purpose: The paper presents a complete computer-aided detection (CAD) system for the detection of lung nodules in computed tomography images. A new mixed feature selection and classification methodology is applied for the first time on a difficult medical image analysis problem. Methods: The CAD system was trained and tested on images from the publicly available Lung Image Database Consortium (LIDC) on the National Cancer Institute website. The detection stage of the system consists of a nodule segmentation method based on nodule and vessel enhancement filters and a computed divergence feature to locate the centers of the nodule clusters. In the subsequent classification stage, invariant features, defined on a gauge coordinates system, are used to differentiate between real nodules and some forms of blood vessels that are easily generating false positive detections. The performance of the novel feature-selective classifier based on genetic algorithms and artificial neural networks (ANNs) is compared with that of two other established classifiers, namely, support vector machines (SVMs) and fixed-topology neural networks. A set of 235 randomly selected cases from the LIDC database was used to train the CAD system. The system has been tested on 125 independent cases from the LIDC database. Results: The overall performance of the fixed-topology ANN classifier slightly exceeds that of the other classifiers, provided the number of internal ANN nodes is chosen well. Making educated guesses about the number of internal ANN nodes is not needed in the new feature-selective classifier, and therefore this classifier remains interesting due to its flexibility and adaptability to the complexity of the classification problem to be solved. Our fixed-topology ANN classifier with 11 hidden nodes reaches a detection sensitivity of 87.5% with an average of four false positives per scan, for nodules with diameter greater than or equal to 3 mm. Analysis of the false positive items reveals that a considerable proportion (18%) of them are smaller nodules, less than 3 mm in diameter. Conclusions: A complete CAD system incorporating novel features is presented, and its performance with three separate classifiers is compared and analyzed. The overall performance of our CAD system equipped with any of the three classifiers is well with respect to other methods described in literature.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2012

Screening tests for hazard classification of complex waste materials - Selection of methods

In this study we describe the development of an alternative methodology for hazard characterization of waste materials. Such an alternative methodology for hazard assessment of complex waste materials is urgently needed, because the lack of a validated instrument leads to arbitrary hazard classification of such complex waste materials. False classification can lead to human and environmental health risks and also has important financial consequences for the waste owner. The Hazardous Waste Directive (HWD) describes the methodology for hazard classification of waste materials. For mirror entries the HWD classification is based upon the hazardous properties (H1-15) of the waste which can be assessed from the hazardous properties of individual identified waste compounds or - if not all compounds are identified - from test results of hazard assessment tests performed on the waste material itself. For the latter the HWD recommends toxicity tests that were initially designed for risk assessment of chemicals in consumer products (pharmaceuticals, cosmetics, biocides, food, etc.). These tests (often using mammals) are not designed nor suitable for the hazard characterization of waste materials. With the present study we want to contribute to the development of an alternative and transparent test strategy for hazard assessment of complex wastes that is in line with the HWD principles for waste classification. It is necessary to cope with this important shortcoming in hazardous waste classification and to demonstrate that alternative methods are available that can be used for hazard assessment of waste materials. Next, by describing the pros and cons of the available methods, and by identifying the needs for additional or further development of test methods, we hope to stimulate research efforts and development in this direction. In this paper we describe promising techniques and argument on the test selection for the pilot study that we have performed on different types of waste materials. Test results are presented in a second paper. As the application of many of the proposed test methods is new in the field of waste management, the principles of the tests are described. The selected tests tackle important hazardous properties but refinement of the test battery is needed to fulfil the a priori conditions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

A Comparison of Neuromorphic Classification Tasks

A variety of neural network models and machine learning techniques have arisen over the past decade, and their successes with image classification have been stunning. With other classification tasks, selecting and configuring a neural network solution is not straightforward. In this paper, we evaluate and compare a variety of neural network models, trained by a variety of machine learning techniques, on a variety of classification tasks. While Deep Learning typically exhibits the best classification accuracy, we note the promise of Reservoir Computing, and evolutionary optimization on spiking neural networks. In many cases, these technologies perform as well as, or better than Deep Learning, and the resulting networks are much smaller than their Deep Learning counterparts.

Reynolds, John↗

DOE OSTI2024

Data-driven search for promising intercalating ions and layered materials for metal-ion batteries

The rise in demand for lithium-ion batteries has led to a large-scale search for electrode materials and intercalating ion species to meet the demands of next-generation energy technologies. Recent efforts largely focus on searching for cathodes that can accommodate large amounts of intercalating ions, but similar work on anodes is relatively limited. This study utilizes machine learning methods to find alternative two-dimensional (2D) materials and intercalating ions beyond Li for metal-ion batteries with high-power efficiencies. The approach first uses density functional theory (DFT) calculations to estimate the theoretical capacities and voltages of various metal ions on 2D materials. The DFT-generated data also provide insights into the local structural accommodation upon ion intercalation on various 2D materials. Significant changes to the lattice can result in irreversible changes to the bonding environments in the anode material, resulting in poor cycling stability. Next, this study develops a binding energy and structural accommodation-based classification model to screen anode materials for next-generation batteries. The classification model selects intercalating ions and 2D material pairs suitable for batteries based on the calculated voltage and volumetric changes in the 2D material upon intercalation. Finally, this study builds a regression model to accurately predict the binding energies of the various intercalating ions on 2D materials. The approach highlights the importance of different elemental and structural features for classification and regression tasks. In conclusion, the insights gained from this study on the role of involved features, such as electronegativities of the constituent ions and the presence of unfilled electronic levels, will help to streamline further studies towards the search for future layered battery materials.

36 MATERIALS SCIENCE↗

DOE OSTI2011

A PHOTOMETRIC VARIABILITY SURVEY OF FIELD K AND M DWARF STARS WITH HATNet

Using light curves from the HATNet survey for transiting extrasolar planets we investigate the optical broadband photometric variability of a sample of 27, 560 field K and M dwarfs selected by color and proper motion (V - K {approx}> 3.0, {mu} > 30 mas yr{sup -1}, plus additional cuts in J - H versus H - K{sub S} and on the reduced proper motion). We search the light curves for periodic variations and for large-amplitude, long-duration flare events. A total of 2120 stars exhibit potential variability, including 95 stars with eclipses and 60 stars with flares. Based on a visual inspection of these light curves and an automated blending classification, we select 1568 stars, including 78 eclipsing binaries (EBs), as secure variable star detections that are not obvious blends. We estimate that a further {approx}26% of these stars may be blends with fainter variables, though most of these blends are likely to be among the hotter stars in our sample. We find that only 38 of the 1568 stars, including five of the EBs, have previously been identified as variables or are blended with previously identified variables. One of the newly identified EBs is 1RXS J154727.5+450803, a known P = 3.55 day, late M-dwarf SB2 system, for which we derive preliminary estimates for the component masses and radii of M{sub 1} = M{sub 2} = 0.258 {+-} 0.008 M{sub sun} and R{sub 1} = R{sub 2} = 0.289 {+-} 0.007 R{sub sun}. The radii of the component stars are larger than theoretical expectations if the system is older than {approx}200 Myr. The majority of the variables are heavily spotted BY Dra-type stars for which we determine rotation periods. Using this sample, we investigate the relations between period, color, age, and activity measures, including optical flaring, for K and M dwarfs, finding that many of the well-established relations for F, G, and K dwarfs continue into the M dwarf regime. We find that the fraction of stars that is variable with peak-to-peak amplitudes greater than 0.01 mag increases exponentially with the V - K{sub S} color such that approximately half of field dwarfs in the solar neighborhood with M {approx}< 0.2 M{sub sun} are variable at this level. Our data hint at a change in the rotation-activity-age connection for stars with M {approx}< 0.25 M{sub sun}.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2012

NEOWISE STUDIES OF ASTEROIDS WITH SLOAN PHOTOMETRY: PRELIMINARY RESULTS

We have combined the NEOWISE and Sloan Digital Sky Survey data to study the albedos of 24,353 asteroids with candidate taxonomic classifications derived using Sloan photometry. We find a wide range of moderate to high albedos for candidate S-type asteroids that are analogous to the S complex defined by previous spectrophotometrically based taxonomic systems. The candidate C-type asteroids, while generally very dark, have a tail of higher albedos that overlaps the S types. The albedo distribution for asteroids with a photometrically derived Q classification is extremely similar to those of the S types. Asteroids with similar colors to (4) Vesta have higher albedos than the S types, and most have orbital elements similar to known Vesta family members. Finally, we show that the relative reflectance at 3.4 and 4.6 {mu}m is higher for D-type asteroids and suggest that their red visible and near-infrared spectral slope extends out to these wavelengths. Understanding the relationship between size, albedo, and taxonomic classification is complicated by the fact that the objects with classifications were selected from the visible/near-infrared Sloan Moving Object Catalog, which is biased against fainter asteroids, including those with lower albedos.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Automatic detection of small bowel tumors in wireless capsule endoscopy images using ensemble learning

Purpose Wireless Capsule Endoscopy (WCE) is a minimally invasive diagnosis tool for lesion detection in the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the significant amount of acquired data leads to difficulties in the diagnosis by the physicians; which can be eased with computer assistance. This paper addresses a method for the automatic detection of tumors in WCE by using a two‐step based procedure: region of interest selection and classification. Methods The first step aims to separate abnormal from normal tissue by using automatic segmentation based on a Gaussian Mixture Model (GMM). A modified version of the Anderson method for convergence acceleration of the expectation–maximization (EM) algorithm is proposed. The proposed features for both segmentation and classification are based on the CIELab color space, as a way of bypassing lightness variations, where the L component is discarded. Tissue variability among subjects, light inhomogeneities and even intensity differences among different devices can be overcome by using simultaneously features from both regions. In the second step, an ensemble system with partition of the training data with a new training scheme is proposed. At this stage, the gating network is trained after the experts have been trained decoupling the joint maximization of both modules. The partition module is also used at the test step, leading the incoming data to the most likely expert allowing incremental adaptation by preserving data diversity. Results This algorithm outperforms others based on texture features selected from Wavelets and Curvelets transforms, classified by a regular support vector machine (SVM) in more than 5%. Conclusions This work shows that simpler features can outperform more elaborate ones if appropriately designed. In the current case, luminance was discarded to cope with saturated tissue, facilitating the color perception. Ensemble systems remain an open research field. In the current case, changes in both topology and training strategy have led to significant performance improvements. A system with this level of performance can be used in current clinical practice.

Vieira, Pedro M.↗

DOE OSTI2022

Using ensembles and distillation to optimize the deployment of deep learning models for the classification of electronic cancer pathology reports

One of the goals of the Surveillance, Epidemiology, and End Results (SEER) program is to estimate incidence, prevalence, and mortality of all cancers. To that end, cancer registries across the country maintain a massive database of cancer pathology reports which contain rich information to understand cancer trends. However, these reports are stored in the form of unstructured text, and human annotators are required to read and extract relevant information. In this article, we show that existing deep learning models for automating information extraction from cancer pathology reports can be significantly improved by using ensemble model distillation. We found that by training multiple predictive models and transferring their knowledge to a single, low-resource model, we can reduce the number of highly confident wrong predictions. Our results show that our implemented methods could save 1000s of manual annotation hours.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Toward memory-efficient melt pool monitoring: a classification framework using event-based imaging and sparse sensing technique

Vision sensors like CMOS and CCD cameras are often used for in-process monitoring of melt pools in laser-based additive and welding processes, but they require transferring large amounts of data and computational processing resources. Event-based neuromorphic imagery, on the other hand, detects only the change in pixel intensity, thus potentially reducing the data amount and latency. With an event imager, this study develops a framework for melt pool condition classification, including image construction, time scale selection, optimal pixel selection, and sparse classification, to achieve a highly memory-efficient scheme. These are based on sparse sensing techniques with singular value decomposition (SVD) and QR pivoting, the two fundamental matrix transformations for linear dimensionality reduction. The framework is then validated by classifying a controlled experiment by exciting various mode shapes of liquid gallium pools of varying depths (3, 6, and 8 mm). At 200 pixels, the classifier can reach overall accuracy of 75%, while at 2000 pixels (0.013% of the total possible pixels), the accuracy is nearly 90% (89.86%). At the same number of pixels, random selection can only achieve 46% and 67%, respectively. The memory savings of the sparsely sampled event data compared to a conventional imager is about 500 times. In addition to performance, implementation and limitations of the framework are also discussed.

42 ENGINEERING↗

DOE OSTI2018

An Efficient Pipeline for Abdomen Segmentation in CT Images

Computed tomography (CT) scans usually include some disadvantages due to the nature of the imaging procedure, and these handicaps prevent accurate abdomen segmentation. Discontinuous abdomen edges, bed section of CT, patient information, closeness between the edges of the abdomen and CT, poor contrast, and a narrow histogram can be regarded as the most important handicaps that occur in abdominal CT scans. Currently, one or more handicaps can arise and prevent technicians obtaining abdomen images through simple segmentation techniques. In other words, CT scans can include the bed section of CT, a patient’s diagnostic information, low-quality abdomen edges, low-level contrast, and narrow histogram, all in one scan. These phenomena constitute a challenge, and an efficient pipeline that is unaffected by handicaps is required. In addition, analysis such as segmentation, feature selection, and classification has meaning for a real-time diagnosis system in cases where the abdomen section is directly used with a specific size. A statistical pipeline is designed in this study that is unaffected by the handicaps mentioned above. Intensity-based approaches, morphological processes, and histogram-based procedures are utilized to design an efficient structure. Performance evaluation is realized in experiments on 58 CT images (16 training, 16 test, and 26 validation) that include the abdomen and one or more disadvantage(s). The first part of the data (16 training images) is used to detect the pipeline’s optimum parameters, while the second and third parts are utilized to evaluate and to confirm the segmentation performance. The segmentation results are presented as the means of six performance metrics. Thus, the proposed method achieves remarkable average rates for training/test/validation of 98.95/99.36/99.57% (jaccard), 99.47/99.67/99.79% (dice), 100/99.91/99.91% (sensitivity), 98.47/99.23/99.85% (specificity), 99.38/99.63/99.87% (classification accuracy), and 98.98/99.45/99.66% (precision). In summary, a statistical pipeline performing the task of abdomen segmentation is achieved that is not affected by the disadvantages, and the most detailed abdomen segmentation study is performed for the use before organ and tumor segmentation, feature extraction, and classification.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2015

A predictive machine learning approach for microstructure optimization and materials design

This paper addresses an important materials engineering question: How can one identify the complete space (or as much of it as possible) of microstructures that are theoretically predicted to yield the desired combination of properties demanded by a selected application? We present a problem involving design of magnetoelastic Fe-Ga alloy microstructure for enhanced elastic, plastic and magnetostrictive properties. While theoretical models for computing properties given the microstructure are known for this alloy, inversion of these relationships to obtain microstructures that lead to desired properties is challenging, primarily due to the high dimensionality of microstructure space, multi-objective design requirement and non-uniqueness of solutions. These challenges render traditional search-based optimization methods incompetent in terms of both searching efficiency and result optimality. In this paper, a route to address these challenges using a machine learning methodology is proposed. A systematic framework consisting of random data generation, feature selection and classification algorithms is developed. In conclusion, experiments with five design problems that involve identification of microstructures that satisfy both linear and nonlinear property constraints show that our framework outperforms traditional optimization methods with the average running time reduced by as much as 80% and with optimality that would not be achieved otherwise.

36 MATERIALS SCIENCE↗