Mapping of Brain Activity by Automated Volume Analysis of Immediate Early Genes.

PubMed

Renier, Nicolas; Adams, Eliza L; Kirst, Christoph; Wu, Zhuhao; Azevedo, Ricardo; Kohl, Johannes; Autry, Anita E; Kadiri, Lolahon; Umadevi Venkataraju, Kannan; Zhou, Yu; Wang, Victoria X; Tang, Cheuk Y; Olsen, Olav; Dulac, Catherine; Osten, Pavel; Tessier-Lavigne, Marc

2016-06-16

Understanding how neural information is processed in physiological and pathological states would benefit from precise detection, localization, and quantification of the activity of all neurons across the entire brain, which has not, to date, been achieved in the mammalian brain. We introduce a pipeline for high-speed acquisition of brain activity at cellular resolution through profiling immediate early gene expression using immunostaining and light-sheet fluorescence imaging, followed by automated mapping and analysis of activity by an open-source software program we term ClearMap. We validate the pipeline first by analysis of brain regions activated in response to haloperidol. Next, we report new cortical regions downstream of whisker-evoked sensory processing during active exploration. Last, we combine activity mapping with axon tracing to uncover new brain regions differentially activated during parenting behavior. This pipeline is widely applicable to different experimental paradigms, including animal species for which transgenic activity reporters are not readily available. Copyright © 2016 Elsevier Inc. All rights reserved.

Mapping of brain activity by automated volume analysis of immediate early genes

PubMed Central

Renier, Nicolas; Adams, Eliza L.; Kirst, Christoph; Wu, Zhuhao; Azevedo, Ricardo; Kohl, Johannes; Autry, Anita E.; Kadiri, Lolahon; Venkataraju, Kannan Umadevi; Zhou, Yu; Wang, Victoria X.; Tang, Cheuk Y.; Olsen, Olav; Dulac, Catherine; Osten, Pavel; Tessier-Lavigne, Marc

2016-01-01

Summary Understanding how neural information is processed in physiological and pathological states would benefit from precise detection, localization and quantification of the activity of all neurons across the entire brain, which has not to date been achieved in the mammalian brain. We introduce a pipeline for high speed acquisition of brain activity at cellular resolution through profiling immediate early gene expression using immunostaining and light-sheet fluorescence imaging, followed by automated mapping and analysis of activity by an open-source software program we term ClearMap. We validate the pipeline first by analysis of brain regions activated in response to Haloperidol. Next, we report new cortical regions downstream of whisker-evoked sensory processing during active exploration. Lastly, we combine activity mapping with axon tracing to uncover new brain regions differentially activated during parenting behavior. This pipeline is widely applicable to different experimental paradigms, including animal species for which transgenic activity reporters are not readily available. PMID:27238021

Automated brain tumor segmentation in magnetic resonance imaging based on sliding-window technique and symmetry analysis.

PubMed

Lian, Yanyun; Song, Zhijian

2014-01-01

Brain tumor segmentation from magnetic resonance imaging (MRI) is an important step toward surgical planning, treatment planning, monitoring of therapy. However, manual tumor segmentation commonly used in clinic is time-consuming and challenging, and none of the existed automated methods are highly robust, reliable and efficient in clinic application. An accurate and automated tumor segmentation method has been developed for brain tumor segmentation that will provide reproducible and objective results close to manual segmentation results. Based on the symmetry of human brain, we employed sliding-window technique and correlation coefficient to locate the tumor position. At first, the image to be segmented was normalized, rotated, denoised, and bisected. Subsequently, through vertical and horizontal sliding-windows technique in turn, that is, two windows in the left and the right part of brain image moving simultaneously pixel by pixel in two parts of brain image, along with calculating of correlation coefficient of two windows, two windows with minimal correlation coefficient were obtained, and the window with bigger average gray value is the location of tumor and the pixel with biggest gray value is the locating point of tumor. At last, the segmentation threshold was decided by the average gray value of the pixels in the square with center at the locating point and 10 pixels of side length, and threshold segmentation and morphological operations were used to acquire the final tumor region. The method was evaluated on 3D FSPGR brain MR images of 10 patients. As a result, the average ratio of correct location was 93.4% for 575 slices containing tumor, the average Dice similarity coefficient was 0.77 for one scan, and the average time spent on one scan was 40 seconds. An fully automated, simple and efficient segmentation method for brain tumor is proposed and promising for future clinic use. Correlation coefficient is a new and effective feature for tumor location.

Associations between Family Adversity and Brain Volume in Adolescence: Manual vs. Automated Brain Segmentation Yields Different Results

PubMed Central

Lyden, Hannah; Gimbel, Sarah I.; Del Piero, Larissa; Tsai, A. Bryna; Sachs, Matthew E.; Kaplan, Jonas T.; Margolin, Gayla; Saxbe, Darby

2016-01-01

Associations between brain structure and early adversity have been inconsistent in the literature. These inconsistencies may be partially due to methodological differences. Different methods of brain segmentation may produce different results, obscuring the relationship between early adversity and brain volume. Moreover, adolescence is a time of significant brain growth and certain brain areas have distinct rates of development, which may compromise the accuracy of automated segmentation approaches. In the current study, 23 adolescents participated in two waves of a longitudinal study. Family aggression was measured when the youths were 12 years old, and structural scans were acquired an average of 4 years later. Bilateral amygdalae and hippocampi were segmented using three different methods (manual tracing, FSL, and NeuroQuant). The segmentation estimates were compared, and linear regressions were run to assess the relationship between early family aggression exposure and all three volume segmentation estimates. Manual tracing results showed a positive relationship between family aggression and right amygdala volume, whereas FSL segmentation showed negative relationships between family aggression and both the left and right hippocampi. However, results indicate poor overlap between methods, and different associations were found between early family aggression exposure and brain volume depending on the segmentation method used. PMID:27656121

Associations between Family Adversity and Brain Volume in Adolescence: Manual vs. Automated Brain Segmentation Yields Different Results.

PubMed

Lyden, Hannah; Gimbel, Sarah I; Del Piero, Larissa; Tsai, A Bryna; Sachs, Matthew E; Kaplan, Jonas T; Margolin, Gayla; Saxbe, Darby

2016-01-01

Associations between brain structure and early adversity have been inconsistent in the literature. These inconsistencies may be partially due to methodological differences. Different methods of brain segmentation may produce different results, obscuring the relationship between early adversity and brain volume. Moreover, adolescence is a time of significant brain growth and certain brain areas have distinct rates of development, which may compromise the accuracy of automated segmentation approaches. In the current study, 23 adolescents participated in two waves of a longitudinal study. Family aggression was measured when the youths were 12 years old, and structural scans were acquired an average of 4 years later. Bilateral amygdalae and hippocampi were segmented using three different methods (manual tracing, FSL, and NeuroQuant). The segmentation estimates were compared, and linear regressions were run to assess the relationship between early family aggression exposure and all three volume segmentation estimates. Manual tracing results showed a positive relationship between family aggression and right amygdala volume, whereas FSL segmentation showed negative relationships between family aggression and both the left and right hippocampi. However, results indicate poor overlap between methods, and different associations were found between early family aggression exposure and brain volume depending on the segmentation method used.

A Comparison of a Brain-Based Adaptive System and a Manual Adaptable System for Invoking Automation

NASA Technical Reports Server (NTRS)

Bailey, Nathan R.; Scerbo, Mark W.; Freeman, Frederick G.; Mikulka, Peter J.; Scott, Lorissa A.

2004-01-01

Two experiments are presented that examine alternative methods for invoking automation. In each experiment, participants were asked to perform simultaneously a monitoring task and a resource management task as well as a tracking task that changed between automatic and manual modes. The monitoring task required participants to detect failures of an automated system to correct aberrant conditions under either high or low system reliability. Performance on each task was assessed as well as situation awareness and subjective workload. In the first experiment, half of the participants worked with a brain-based system that used their EEG signals to switch the tracking task between automatic and manual modes. The remaining participants were yoked to participants from the adaptive condition and received the same schedule of mode switches, but their EEG had no effect on the automation. Within each group, half of the participants were assigned to either the low or high reliability monitoring task. In addition, within each combination of automation invocation and system reliability, participants were separated into high and low complacency potential groups. The results revealed no significant effects of automation invocation on the performance measures; however, the high complacency individuals demonstrated better situation awareness when working with the adaptive automation system. The second experiment was the same as the first with one important exception. Automation was invoked manually. Thus, half of the participants pressed a button to invoke automation for 10 s. The remaining participants were yoked to participants from the adaptable condition and received the same schedule of mode switches, but they had no control over the automation. The results showed that participants who could invoke automation performed more poorly on the resource management task and reported higher levels of subjective workload. Further, those who invoked automation more frequently performed

Nursing benefits of using an automated injection system for ictal brain single photon emission computed tomography.

PubMed

Vonhofen, Geraldine; Evangelista, Tonya; Lordeon, Patricia

2012-04-01

The traditional method of administering radioactive isotopes to pediatric patients undergoing ictal brain single photon emission computed tomography testing has been by manual injections. This method presents certain challenges for nursing, including time requirements and safety risks. This quality improvement project discusses the implementation of an automated injection system for isotope administration and its impact on staffing, safety, and nursing satisfaction. It was conducted in an epilepsy monitoring unit at a large urban pediatric facility. Results of this project showed a decrease in the number of nurses exposed to radiation and improved nursing satisfaction with the use of the automated injection system. In addition, there was a decrease in the number of nursing hours required during ictal brain single photon emission computed tomography testing.

Leveraging Clinical Imaging Archives for Radiomics: Reliability of Automated Methods for Brain Volume Measurement.

PubMed

Adduru, Viraj R; Michael, Andrew M; Helguera, Maria; Baum, Stefi A; Moore, Gregory J

2017-09-01

Purpose To validate the use of thick-section clinically acquired magnetic resonance (MR) imaging data for estimating total brain volume (TBV), gray matter (GM) volume (GMV), and white matter (WM) volume (WMV) by using three widely used automated toolboxes: SPM ( www.fil.ion.ucl.ac.uk/spm/ ), FreeSurfer ( surfer.nmr.mgh.harvard.edu ), and FSL (FMRIB software library; Oxford Centre for Functional MR Imaging of the Brain, Oxford, England, https://fsl.fmrib.ox.ac.uk/fsl ). Materials and Methods MR images from a clinical archive were used and data were deidentified. The three methods were applied to estimate brain volumes from thin-section research-quality brain MR images and routine thick-section clinical MR images acquired from the same 38 patients (age range, 1-71 years; mean age, 22 years; 11 women). By using these automated methods, TBV, GMV, and WMV were estimated. Thin- versus thick-section volume comparisons were made for each method by using intraclass correlation coefficients (ICCs). Results SPM exhibited excellent ICCs (0.97, 0.85, and 0.83 for TBV, GMV, and WMV, respectively). FSL exhibited ICCs of 0.69, 0.51, and 0.60 for TBV, GMV, and WMV, respectively, but they were lower than with SPM. FreeSurfer exhibited excellent ICC of 0.63 only for TBV. Application of SPM's voxel-based morphometry on the modulated images of thin-section images and interpolated thick-section images showed fair to excellent ICCs (0.37-0.98) for the majority of brain regions (88.47% [306924 of 346916 voxels] of WM and 80.35% [377 282 of 469 502 voxels] of GM). Conclusion Thick-section clinical-quality MR images can be reliably used for computing quantitative brain metrics such as TBV, GMV, and WMV by using SPM. © RSNA, 2017 Online supplemental material is available for this article.

Multimodality 3D Superposition and Automated Whole Brain Tractography: Comprehensive Printing of the Functional Brain

PubMed Central

Brimley, Cameron J; Sublett, Jesna Mathew; Stefanowicz, Edward; Flora, Sarah; Mongelluzzo, Gino; Schirmer, Clemens M

2017-01-01

Whole brain tractography using diffusion tensor imaging (DTI) sequences can be used to map cerebral connectivity; however, this can be time-consuming due to the manual component of image manipulation required, calling for the need for a standardized, automated, and accurate fiber tracking protocol with automatic whole brain tractography (AWBT). Interpreting conventional two-dimensional (2D) images, such as computed tomography (CT) and magnetic resonance imaging (MRI), as an intraoperative three-dimensional (3D) environment is a difficult task with recognized inter-operator variability. Three-dimensional printing in neurosurgery has gained significant traction in the past decade, and as software, equipment, and practices become more refined, trainee education, surgical skills, research endeavors, innovation, patient education, and outcomes via valued care is projected to improve. We describe a novel multimodality 3D superposition (MMTS) technique, which fuses multiple imaging sequences alongside cerebral tractography into one patient-specific 3D printed model. Inferences on cost and improved outcomes fueled by encouraging patient engagement are explored. PMID:29201580

Multimodality 3D Superposition and Automated Whole Brain Tractography: Comprehensive Printing of the Functional Brain.

PubMed

Konakondla, Sanjay; Brimley, Cameron J; Sublett, Jesna Mathew; Stefanowicz, Edward; Flora, Sarah; Mongelluzzo, Gino; Schirmer, Clemens M

2017-09-29

Whole brain tractography using diffusion tensor imaging (DTI) sequences can be used to map cerebral connectivity; however, this can be time-consuming due to the manual component of image manipulation required, calling for the need for a standardized, automated, and accurate fiber tracking protocol with automatic whole brain tractography (AWBT). Interpreting conventional two-dimensional (2D) images, such as computed tomography (CT) and magnetic resonance imaging (MRI), as an intraoperative three-dimensional (3D) environment is a difficult task with recognized inter-operator variability. Three-dimensional printing in neurosurgery has gained significant traction in the past decade, and as software, equipment, and practices become more refined, trainee education, surgical skills, research endeavors, innovation, patient education, and outcomes via valued care is projected to improve. We describe a novel multimodality 3D superposition (MMTS) technique, which fuses multiple imaging sequences alongside cerebral tractography into one patient-specific 3D printed model. Inferences on cost and improved outcomes fueled by encouraging patient engagement are explored.

CUDA-based acceleration and BPN-assisted automation of bilateral filtering for brain MR image restoration.

PubMed

Chang, Herng-Hua; Chang, Yu-Ning

2017-04-01

Bilateral filters have been substantially exploited in numerous magnetic resonance (MR) image restoration applications for decades. Due to the deficiency of theoretical basis on the filter parameter setting, empirical manipulation with fixed values and noise variance-related adjustments has generally been employed. The outcome of these strategies is usually sensitive to the variation of the brain structures and not all the three parameter values are optimal. This article is in an attempt to investigate the optimal setting of the bilateral filter, from which an accelerated and automated restoration framework is developed. To reduce the computational burden of the bilateral filter, parallel computing with the graphics processing unit (GPU) architecture is first introduced. The NVIDIA Tesla K40c GPU with the compute unified device architecture (CUDA) functionality is specifically utilized to emphasize thread usages and memory resources. To correlate the filter parameters with image characteristics for automation, optimal image texture features are subsequently acquired based on the sequential forward floating selection (SFFS) scheme. Subsequently, the selected features are introduced into the back propagation network (BPN) model for filter parameter estimation. Finally, the k-fold cross validation method is adopted to evaluate the accuracy of the proposed filter parameter prediction framework. A wide variety of T1-weighted brain MR images with various scenarios of noise levels and anatomic structures were utilized to train and validate this new parameter decision system with CUDA-based bilateral filtering. For a common brain MR image volume of 256 × 256 × 256 pixels, the speed-up gain reached 284. Six optimal texture features were acquired and associated with the BPN to establish a "high accuracy" parameter prediction system, which achieved a mean absolute percentage error (MAPE) of 5.6%. Automatic restoration results on 2460 brain MR images received an average

Evaluation of Cross-Protocol Stability of a Fully Automated Brain Multi-Atlas Parcellation Tool.

PubMed

Liang, Zifei; He, Xiaohai; Ceritoglu, Can; Tang, Xiaoying; Li, Yue; Kutten, Kwame S; Oishi, Kenichi; Miller, Michael I; Mori, Susumu; Faria, Andreia V

2015-01-01

Brain parcellation tools based on multiple-atlas algorithms have recently emerged as a promising method with which to accurately define brain structures. When dealing with data from various sources, it is crucial that these tools are robust for many different imaging protocols. In this study, we tested the robustness of a multiple-atlas, likelihood fusion algorithm using Alzheimer's Disease Neuroimaging Initiative (ADNI) data with six different protocols, comprising three manufacturers and two magnetic field strengths. The entire brain was parceled into five different levels of granularity. In each level, which defines a set of brain structures, ranging from eight to 286 regions, we evaluated the variability of brain volumes related to the protocol, age, and diagnosis (healthy or Alzheimer's disease). Our results indicated that, with proper pre-processing steps, the impact of different protocols is minor compared to biological effects, such as age and pathology. A precise knowledge of the sources of data variation enables sufficient statistical power and ensures the reliability of an anatomical analysis when using this automated brain parcellation tool on datasets from various imaging protocols, such as clinical databases.

Automated Robust Image Segmentation: Level Set Method Using Nonnegative Matrix Factorization with Application to Brain MRI.

PubMed

Dera, Dimah; Bouaynaya, Nidhal; Fathallah-Shaykh, Hassan M

2016-07-01

We address the problem of fully automated region discovery and robust image segmentation by devising a new deformable model based on the level set method (LSM) and the probabilistic nonnegative matrix factorization (NMF). We describe the use of NMF to calculate the number of distinct regions in the image and to derive the local distribution of the regions, which is incorporated into the energy functional of the LSM. The results demonstrate that our NMF-LSM method is superior to other approaches when applied to synthetic binary and gray-scale images and to clinical magnetic resonance images (MRI) of the human brain with and without a malignant brain tumor, glioblastoma multiforme. In particular, the NMF-LSM method is fully automated, highly accurate, less sensitive to the initial selection of the contour(s) or initial conditions, more robust to noise and model parameters, and able to detect as small distinct regions as desired. These advantages stem from the fact that the proposed method relies on histogram information instead of intensity values and does not introduce nuisance model parameters. These properties provide a general approach for automated robust region discovery and segmentation in heterogeneous images. Compared with the retrospective radiological diagnoses of two patients with non-enhancing grade 2 and 3 oligodendroglioma, the NMF-LSM detects earlier progression times and appears suitable for monitoring tumor response. The NMF-LSM method fills an important need of automated segmentation of clinical MRI.

Automated segmentation of neuroanatomical structures in multispectral MR microscopy of the mouse brain.

PubMed

Ali, Anjum A; Dale, Anders M; Badea, Alexandra; Johnson, G Allan

2005-08-15

We present the automated segmentation of magnetic resonance microscopy (MRM) images of the C57BL/6J mouse brain into 21 neuroanatomical structures, including the ventricular system, corpus callosum, hippocampus, caudate putamen, inferior colliculus, internal capsule, globus pallidus, and substantia nigra. The segmentation algorithm operates on multispectral, three-dimensional (3D) MR data acquired at 90-microm isotropic resolution. Probabilistic information used in the segmentation is extracted from training datasets of T2-weighted, proton density-weighted, and diffusion-weighted acquisitions. Spatial information is employed in the form of prior probabilities of occurrence of a structure at a location (location priors) and the pairwise probabilities between structures (contextual priors). Validation using standard morphometry indices shows good consistency between automatically segmented and manually traced data. Results achieved in the mouse brain are comparable with those achieved in human brain studies using similar techniques. The segmentation algorithm shows excellent potential for routine morphological phenotyping of mouse models.

Automated Computational Processing of 3-D MR Images of Mouse Brain for Phenotyping of Living Animals.

PubMed

Medina, Christopher S; Manifold-Wheeler, Brett; Gonzales, Aaron; Bearer, Elaine L

2017-07-05

Magnetic resonance (MR) imaging provides a method to obtain anatomical information from the brain in vivo that is not typically available by optical imaging because of this organ's opacity. MR is nondestructive and obtains deep tissue contrast with 100-µm 3 voxel resolution or better. Manganese-enhanced MRI (MEMRI) may be used to observe axonal transport and localized neural activity in the living rodent and avian brain. Such enhancement enables researchers to investigate differences in functional circuitry or neuronal activity in images of brains of different animals. Moreover, once MR images of a number of animals are aligned into a single matrix, statistical analysis can be done comparing MR intensities between different multi-animal cohorts comprising individuals from different mouse strains or different transgenic animals, or at different time points after an experimental manipulation. Although preprocessing steps for such comparisons (including skull stripping and alignment) are automated for human imaging, no such automated processing has previously been readily available for mouse or other widely used experimental animals, and most investigators use in-house custom processing. This protocol describes a stepwise method to perform such preprocessing for mouse. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.

Automated diagnosis of Alzheimer's disease with multi-atlas based whole brain segmentations

NASA Astrophysics Data System (ADS)

Luo, Yuan; Tang, Xiaoying

2017-03-01

Voxel-based analysis is widely used in quantitative analysis of structural brain magnetic resonance imaging (MRI) and automated disease detection, such as Alzheimer's disease (AD). However, noise at the voxel level may cause low sensitivity to AD-induced structural abnormalities. This can be addressed with the use of a whole brain structural segmentation approach which greatly reduces the dimension of features (the number of voxels). In this paper, we propose an automatic AD diagnosis system that combines such whole brain segmen- tations with advanced machine learning methods. We used a multi-atlas segmentation technique to parcellate T1-weighted images into 54 distinct brain regions and extract their structural volumes to serve as the features for principal-component-analysis-based dimension reduction and support-vector-machine-based classification. The relationship between the number of retained principal components (PCs) and the diagnosis accuracy was systematically evaluated, in a leave-one-out fashion, based on 28 AD subjects and 23 age-matched healthy subjects. Our approach yielded pretty good classification results with 96.08% overall accuracy being achieved using the three foremost PCs. In addition, our approach yielded 96.43% specificity, 100% sensitivity, and 0.9891 area under the receiver operating characteristic curve.

FreeSurfer-initiated fully-automated subcortical brain segmentation in MRI using Large Deformation Diffeomorphic Metric Mapping.

PubMed

Khan, Ali R; Wang, Lei; Beg, Mirza Faisal

2008-07-01

Fully-automated brain segmentation methods have not been widely adopted for clinical use because of issues related to reliability, accuracy, and limitations of delineation protocol. By combining the probabilistic-based FreeSurfer (FS) method with the Large Deformation Diffeomorphic Metric Mapping (LDDMM)-based label-propagation method, we are able to increase reliability and accuracy, and allow for flexibility in template choice. Our method uses the automated FreeSurfer subcortical labeling to provide a coarse-to-fine introduction of information in the LDDMM template-based segmentation resulting in a fully-automated subcortical brain segmentation method (FS+LDDMM). One major advantage of the FS+LDDMM-based approach is that the automatically generated segmentations generated are inherently smooth, thus subsequent steps in shape analysis can directly follow without manual post-processing or loss of detail. We have evaluated our new FS+LDDMM method on several databases containing a total of 50 subjects with different pathologies, scan sequences and manual delineation protocols for labeling the basal ganglia, thalamus, and hippocampus. In healthy controls we report Dice overlap measures of 0.81, 0.83, 0.74, 0.86 and 0.75 for the right caudate nucleus, putamen, pallidum, thalamus and hippocampus respectively. We also find statistically significant improvement of accuracy in FS+LDDMM over FreeSurfer for the caudate nucleus and putamen of Huntington's disease and Tourette's syndrome subjects, and the right hippocampus of Schizophrenia subjects.

Semiautomated volumetry of the cerebrum, cerebellum-brain stem, and temporal lobe on brain magnetic resonance images.

PubMed

Hayashi, Norio; Sanada, Shigeru; Suzuki, Masayuki; Matsuura, Yukihiro; Kawahara, Kazuhiro; Tsujii, Hideo; Yamamoto, Tomoyuki; Matsui, Osamu

2008-02-01

The aim of this study was to develop an automated method of segmenting the cerebrum, cerebellum-brain stem, and temporal lobe simultaneously on magnetic resonance (MR) images. We obtained T1-weighted MR images from 10 normal subjects and 19 patients with brain atrophy. To perform automated volumetry from MR images, we performed the following three steps: (1) segmentation of the brain region; (2) separation between the cerebrum and the cerebellum-brain stem; and (3) segmentation of the temporal lobe. Evaluation was based on the correctly recognized region (CRR) (i.e., the region recognized by both the automated and manual methods). The mean CRRs of the normal and atrophic brains were 98.2% and 97.9% for the cerebrum, 87.9% and 88.5% for the cerebellum-brain stem, and 76.9% and 85.8% for the temporal lobe, respectively. We introduce an automated volumetric method for the cerebrum, cerebellum-brain stem, and temporal lobe on brain MR images. Our method can be applied to not only the normal brain but also the atrophic brain.

Comparison of Automated Brain Volume Measures obtained with NeuroQuant and FreeSurfer.

PubMed

Ochs, Alfred L; Ross, David E; Zannoni, Megan D; Abildskov, Tracy J; Bigler, Erin D

2015-01-01

To examine intermethod reliabilities and differences between FreeSurfer and the FDA-cleared congener, NeuroQuant, both fully automated methods for structural brain MRI measurements. MRI scans from 20 normal control subjects, 20 Alzheimer's disease patients, and 20 mild traumatically brain-injured patients were analyzed with NeuroQuant and with FreeSurfer. Intermethod reliability was evaluated. Pairwise correlation coefficients, intraclass correlation coefficients, and effect size differences were computed. NeuroQuant versus FreeSurfer measures showed excellent to good intermethod reliability for the 21 regions evaluated (r: .63 to .99/ICC: .62 to .99/ES: -.33 to 2.08) except for the pallidum (r/ICC/ES = .31/.29/-2.2) and cerebellar white matter (r/ICC/ES = .31/.31/.08). Volumes reported by NeuroQuant were generally larger than those reported by FreeSurfer with the whole brain parenchyma volume reported by NeuroQuant 6.50% larger than the volume reported by FreeSurfer. There was no systematic difference in results between the 3 subgroups. NeuroQuant and FreeSurfer showed good to excellent intermethod reliability in volumetric measurements for all brain regions examined with the only exceptions being the pallidum and cerebellar white matter. This finding was robust for normal individuals, patients with Alzheimer's disease, and patients with mild traumatic brain injury. Copyright © 2015 by the American Society of Neuroimaging.

A multi-atlas based method for automated anatomical Macaca fascicularis brain MRI segmentation and PET kinetic extraction.

PubMed

Ballanger, Bénédicte; Tremblay, Léon; Sgambato-Faure, Véronique; Beaudoin-Gobert, Maude; Lavenne, Franck; Le Bars, Didier; Costes, Nicolas

2013-08-15

MRI templates and digital atlases are needed for automated and reproducible quantitative analysis of non-human primate PET studies. Segmenting brain images via multiple atlases outperforms single-atlas labelling in humans. We present a set of atlases manually delineated on brain MRI scans of the monkey Macaca fascicularis. We use this multi-atlas dataset to evaluate two automated methods in terms of accuracy, robustness and reliability in segmenting brain structures on MRI and extracting regional PET measures. Twelve individual Macaca fascicularis high-resolution 3DT1 MR images were acquired. Four individual atlases were created by manually drawing 42 anatomical structures, including cortical and sub-cortical structures, white matter regions, and ventricles. To create the MRI template, we first chose one MRI to define a reference space, and then performed a two-step iterative procedure: affine registration of individual MRIs to the reference MRI, followed by averaging of the twelve resampled MRIs. Automated segmentation in native space was obtained in two ways: 1) Maximum probability atlases were created by decision fusion of two to four individual atlases in the reference space, and transformation back into the individual native space (MAXPROB)(.) 2) One to four individual atlases were registered directly to the individual native space, and combined by decision fusion (PROPAG). Accuracy was evaluated by computing the Dice similarity index and the volume difference. The robustness and reproducibility of PET regional measurements obtained via automated segmentation was evaluated on four co-registered MRI/PET datasets, which included test-retest data. Dice indices were always over 0.7 and reached maximal values of 0.9 for PROPAG with all four individual atlases. There was no significant mean volume bias. The standard deviation of the bias decreased significantly when increasing the number of individual atlases. MAXPROB performed better when increasing the number of

Three validation metrics for automated probabilistic image segmentation of brain tumours

PubMed Central

Zou, Kelly H.; Wells, William M.; Kikinis, Ron; Warfield, Simon K.

2005-01-01

SUMMARY The validity of brain tumour segmentation is an important issue in image processing because it has a direct impact on surgical planning. We examined the segmentation accuracy based on three two-sample validation metrics against the estimated composite latent gold standard, which was derived from several experts’ manual segmentations by an EM algorithm. The distribution functions of the tumour and control pixel data were parametrically assumed to be a mixture of two beta distributions with different shape parameters. We estimated the corresponding receiver operating characteristic curve, Dice similarity coefficient, and mutual information, over all possible decision thresholds. Based on each validation metric, an optimal threshold was then computed via maximization. We illustrated these methods on MR imaging data from nine brain tumour cases of three different tumour types, each consisting of a large number of pixels. The automated segmentation yielded satisfactory accuracy with varied optimal thresholds. The performances of these validation metrics were also investigated via Monte Carlo simulation. Extensions of incorporating spatial correlation structures using a Markov random field model were considered. PMID:15083482

Fully automated tumor segmentation based on improved fuzzy connectedness algorithm in brain MR images.

PubMed

Harati, Vida; Khayati, Rasoul; Farzan, Abdolreza

2011-07-01

Uncontrollable and unlimited cell growth leads to tumor genesis in the brain. If brain tumors are not diagnosed early and cured properly, they could cause permanent brain damage or even death to patients. As in all methods of treatments, any information about tumor position and size is important for successful treatment; hence, finding an accurate and a fully automated method to give information to physicians is necessary. A fully automatic and accurate method for tumor region detection and segmentation in brain magnetic resonance (MR) images is suggested. The presented approach is an improved fuzzy connectedness (FC) algorithm based on a scale in which the seed point is selected automatically. This algorithm is independent of the tumor type in terms of its pixels intensity. Tumor segmentation evaluation results based on similarity criteria (similarity index (SI), overlap fraction (OF), and extra fraction (EF) are 92.89%, 91.75%, and 3.95%, respectively) indicate a higher performance of the proposed approach compared to the conventional methods, especially in MR images, in tumor regions with low contrast. Thus, the suggested method is useful for increasing the ability of automatic estimation of tumor size and position in brain tissues, which provides more accurate investigation of the required surgery, chemotherapy, and radiotherapy procedures. Copyright © 2011 Elsevier Ltd. All rights reserved.

Automating Cell Detection and Classification in Human Brain Fluorescent Microscopy Images Using Dictionary Learning and Sparse Coding

PubMed Central

Alegro, Maryana; Theofilas, Panagiotis; Nguy, Austin; Castruita, Patricia A.; Seeley, William; Heinsen, Helmut; Ushizima, Daniela M.

2017-01-01

Background Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. New method Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. Results Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. Comparison with existing methods We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples. Conclusion The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks. PMID:28267565

Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding.

PubMed

Alegro, Maryana; Theofilas, Panagiotis; Nguy, Austin; Castruita, Patricia A; Seeley, William; Heinsen, Helmut; Ushizima, Daniela M; Grinberg, Lea T

2017-04-15

Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples. The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks. Copyright © 2017 Elsevier B.V. All rights reserved.

Automated Spatial Brain Normalization and Hindbrain White Matter Reference Tissue Give Improved [(18)F]-Florbetaben PET Quantitation in Alzheimer's Model Mice.

PubMed

Overhoff, Felix; Brendel, Matthias; Jaworska, Anna; Korzhova, Viktoria; Delker, Andreas; Probst, Federico; Focke, Carola; Gildehaus, Franz-Josef; Carlsen, Janette; Baumann, Karlheinz; Haass, Christian; Bartenstein, Peter; Herms, Jochen; Rominger, Axel

2016-01-01

Preclinical PET studies of β-amyloid (Aβ) accumulation are of growing importance, but comparisons between research sites require standardized and optimized methods for quantitation. Therefore, we aimed to evaluate systematically the (1) impact of an automated algorithm for spatial brain normalization, and (2) intensity scaling methods of different reference regions for Aβ-PET in a large dataset of transgenic mice. PS2APP mice in a 6 week longitudinal setting (N = 37) and another set of PS2APP mice at a histologically assessed narrow range of Aβ burden (N = 40) were investigated by [(18)F]-florbetaben PET. Manual spatial normalization by three readers at different training levels was performed prior to application of an automated brain spatial normalization and inter-reader agreement was assessed by Fleiss Kappa (κ). For this method the impact of templates at different pathology stages was investigated. Four different reference regions on brain uptake normalization were used to calculate frontal cortical standardized uptake value ratios (SUVRCTX∕REF), relative to raw SUVCTX. Results were compared on the basis of longitudinal stability (Cohen's d), and in reference to gold standard histopathological quantitation (Pearson's R). Application of an automated brain spatial normalization resulted in nearly perfect agreement (all κ≥0.99) between different readers, with constant or improved correlation with histology. Templates based on inappropriate pathology stage resulted in up to 2.9% systematic bias for SUVRCTX∕REF. All SUVRCTX∕REF methods performed better than SUVCTX both with regard to longitudinal stability (d≥1.21 vs. d = 0.23) and histological gold standard agreement (R≥0.66 vs. R≥0.31). Voxel-wise analysis suggested a physiologically implausible longitudinal decrease by global mean scaling. The hindbrain white matter reference (R mean = 0.75) was slightly superior to the brainstem (R mean = 0.74) and the cerebellum (R mean = 0.73). Automated

Automated metastatic brain lesion detection: a computer aided diagnostic and clinical research tool

NASA Astrophysics Data System (ADS)

Devine, Jeremy; Sahgal, Arjun; Karam, Irene; Martel, Anne L.

2016-03-01

The accurate localization of brain metastases in magnetic resonance (MR) images is crucial for patients undergoing stereotactic radiosurgery (SRS) to ensure that all neoplastic foci are targeted. Computer automated tumor localization and analysis can improve both of these tasks by eliminating inter and intra-observer variations during the MR image reading process. Lesion localization is accomplished using adaptive thresholding to extract enhancing objects. Each enhancing object is represented as a vector of features which includes information on object size, symmetry, position, shape, and context. These vectors are then used to train a random forest classifier. We trained and tested the image analysis pipeline on 3D axial contrast-enhanced MR images with the intention of localizing the brain metastases. In our cross validation study and at the most effective algorithm operating point, we were able to identify 90% of the lesions at a precision rate of 60%.

Associative image analysis: a method for automated quantification of 3D multi-parameter images of brain tissue

PubMed Central

Bjornsson, Christopher S; Lin, Gang; Al-Kofahi, Yousef; Narayanaswamy, Arunachalam; Smith, Karen L; Shain, William; Roysam, Badrinath

2009-01-01

Brain structural complexity has confounded prior efforts to extract quantitative image-based measurements. We present a systematic ‘divide and conquer’ methodology for analyzing three-dimensional (3D) multi-parameter images of brain tissue to delineate and classify key structures, and compute quantitative associations among them. To demonstrate the method, thick (~100 μm) slices of rat brain tissue were labeled using 3 – 5 fluorescent signals, and imaged using spectral confocal microscopy and unmixing algorithms. Automated 3D segmentation and tracing algorithms were used to delineate cell nuclei, vasculature, and cell processes. From these segmentations, a set of 23 intrinsic and 8 associative image-based measurements was computed for each cell. These features were used to classify astrocytes, microglia, neurons, and endothelial cells. Associations among cells and between cells and vasculature were computed and represented as graphical networks to enable further analysis. The automated results were validated using a graphical interface that permits investigator inspection and corrective editing of each cell in 3D. Nuclear counting accuracy was >89%, and cell classification accuracy ranged from 81–92% depending on cell type. We present a software system named FARSIGHT implementing our methodology. Its output is a detailed XML file containing measurements that may be used for diverse quantitative hypothesis-driven and exploratory studies of the central nervous system. PMID:18294697

Automated segmentation of ventricles from serial brain MRI for the quantification of volumetric changes associated with communicating hydrocephalus in patients with brain tumor

NASA Astrophysics Data System (ADS)

Pura, John A.; Hamilton, Allison M.; Vargish, Geoffrey A.; Butman, John A.; Linguraru, Marius George

2011-03-01

Accurate ventricle volume estimates could improve the understanding and diagnosis of postoperative communicating hydrocephalus. For this category of patients, associated changes in ventricle volume can be difficult to identify, particularly over short time intervals. We present an automated segmentation algorithm that evaluates ventricle size from serial brain MRI examination. The technique combines serial T1- weighted images to increase SNR and segments the means image to generate a ventricle template. After pre-processing, the segmentation is initiated by a fuzzy c-means clustering algorithm to find the seeds used in a combination of fast marching methods and geodesic active contours. Finally, the ventricle template is propagated onto the serial data via non-linear registration. Serial volume estimates were obtained in an automated robust and accurate manner from difficult data.

Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features.

PubMed

Kushibar, Kaisar; Valverde, Sergi; González-Villà, Sandra; Bernal, Jose; Cabezas, Mariano; Oliver, Arnau; Lladó, Xavier

2018-06-15

Sub-cortical brain structure segmentation in Magnetic Resonance Images (MRI) has attracted the interest of the research community for a long time as morphological changes in these structures are related to different neurodegenerative disorders. However, manual segmentation of these structures can be tedious and prone to variability, highlighting the need for robust automated segmentation methods. In this paper, we present a novel convolutional neural network based approach for accurate segmentation of the sub-cortical brain structures that combines both convolutional and prior spatial features for improving the segmentation accuracy. In order to increase the accuracy of the automated segmentation, we propose to train the network using a restricted sample selection to force the network to learn the most difficult parts of the structures. We evaluate the accuracy of the proposed method on the public MICCAI 2012 challenge and IBSR 18 datasets, comparing it with different traditional and deep learning state-of-the-art methods. On the MICCAI 2012 dataset, our method shows an excellent performance comparable to the best participant strategy on the challenge, while performing significantly better than state-of-the-art techniques such as FreeSurfer and FIRST. On the IBSR 18 dataset, our method also exhibits a significant increase in the performance with respect to not only FreeSurfer and FIRST, but also comparable or better results than other recent deep learning approaches. Moreover, our experiments show that both the addition of the spatial priors and the restricted sampling strategy have a significant effect on the accuracy of the proposed method. In order to encourage the reproducibility and the use of the proposed method, a public version of our approach is available to download for the neuroimaging community. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration

PubMed Central

Klein, Arno; Andersson, Jesper; Ardekani, Babak A.; Ashburner, John; Avants, Brian; Chiang, Ming-Chang; Christensen, Gary E.; Collins, D. Louis; Gee, James; Hellier, Pierre; Song, Joo Hyun; Jenkinson, Mark; Lepage, Claude; Rueckert, Daniel; Thompson, Paul; Vercauteren, Tom; Woods, Roger P.; Mann, J. John; Parsey, Ramin V.

2009-01-01

All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functional and physiological data require coregistration of brains to establish correspondences across brain structures. It is well established that linear registration of one brain to another is inadequate for aligning brain structures, so numerous algorithms have emerged to nonlinearly register brains to one another. This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted. Fourteen algorithms from laboratories around the world are evaluated using 8 different error measures. More than 45,000 registrations between 80 manually labeled brains were performed by algorithms including: AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO, SICLE, SyN, and four different SPM5 algorithms (“SPM2-type” and regular Normalization, Unified Segmentation, and the DARTEL Toolbox). All of these registrations were preceded by linear registration between the same image pairs using FLIRT. One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure. This is important because it suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols. Furthermore, we ranked the 14 methods according to three completely independent analyses (permutation tests, one-way ANOVA tests, and indifference-zone ranking) and derived three almost identical top rankings of the methods. ART, SyN, IRTK, and SPM's DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets

A semi-automated volumetric software for segmentation and perfusion parameter quantification of brain tumors using 320-row multidetector computed tomography: a validation study.

PubMed

Chae, Soo Young; Suh, Sangil; Ryoo, Inseon; Park, Arim; Noh, Kyoung Jin; Shim, Hackjoon; Seol, Hae Young

2017-05-01

We developed a semi-automated volumetric software, NPerfusion, to segment brain tumors and quantify perfusion parameters on whole-brain CT perfusion (WBCTP) images. The purpose of this study was to assess the feasibility of the software and to validate its performance compared with manual segmentation. Twenty-nine patients with pathologically proven brain tumors who underwent preoperative WBCTP between August 2012 and February 2015 were included. Three perfusion parameters, arterial flow (AF), equivalent blood volume (EBV), and Patlak flow (PF, which is a measure of permeability of capillaries), of brain tumors were generated by a commercial software and then quantified volumetrically by NPerfusion, which also semi-automatically segmented tumor boundaries. The quantification was validated by comparison with that of manual segmentation in terms of the concordance correlation coefficient and Bland-Altman analysis. With NPerfusion, we successfully performed segmentation and quantified whole volumetric perfusion parameters of all 29 brain tumors that showed consistent perfusion trends with previous studies. The validation of the perfusion parameter quantification exhibited almost perfect agreement with manual segmentation, with Lin concordance correlation coefficients (ρ c ) for AF, EBV, and PF of 0.9988, 0.9994, and 0.9976, respectively. On Bland-Altman analysis, most differences between this software and manual segmentation on the commercial software were within the limit of agreement. NPerfusion successfully performs segmentation of brain tumors and calculates perfusion parameters of brain tumors. We validated this semi-automated segmentation software by comparing it with manual segmentation. NPerfusion can be used to calculate volumetric perfusion parameters of brain tumors from WBCTP.

Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm.

PubMed

Pizarro, Ricardo A; Cheng, Xi; Barnett, Alan; Lemaitre, Herve; Verchinski, Beth A; Goldman, Aaron L; Xiao, Ena; Luo, Qian; Berman, Karen F; Callicott, Joseph H; Weinberger, Daniel R; Mattay, Venkata S

2016-01-01

High-resolution three-dimensional magnetic resonance imaging (3D-MRI) is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM) algorithm in the quality assessment of structural brain images, using global and region of interest (ROI) automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy) of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation.

PubMed

Dereymaeker, Anneleen; Pillay, Kirubin; Vervisch, Jan; Van Huffel, Sabine; Naulaers, Gunnar; Jansen, Katrien; De Vos, Maarten

2017-09-01

Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age ([Formula: see text] age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27-42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement [Formula: see text]), using Sensitivity, Specificity, Detection Factor ([Formula: see text] of visual QS periods correctly detected by CLASS) and Misclassification Factor ([Formula: see text] of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31-38 weeks (median [Formula: see text], median MF 0-0.25, median Sensitivity 0.93-1.0, and median Specificity 0.80-0.91 across this age range), with minimal misclassifications at 35-36 weeks (median [Formula: see text]). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. This highlights the benefit of automated QS detection for exploring brain maturation.

Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field.

PubMed

Nie, Jingxin; Xue, Zhong; Liu, Tianming; Young, Geoffrey S; Setayesh, Kian; Guo, Lei; Wong, Stephen T C

2009-09-01

A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted image, can be performed prior to the segmentation, the results are usually limited by partial volume effects due to interpolation of low-resolution images. To improve the quality of tumor segmentation in clinical applications where low-resolution sequences are commonly used together with high-resolution images, we propose the algorithm based on Spatial accuracy-weighted Hidden Markov random field and Expectation maximization (SHE) approach for both automated tumor and enhanced-tumor segmentation. SHE incorporates the spatial interpolation accuracy of low-resolution images into the optimization procedure of the Hidden Markov Random Field (HMRF) to segment tumor using multi-channel MR images with different resolutions, e.g., high-resolution T1-weighted and low-resolution T2-weighted images. In experiments, we evaluated this algorithm using a set of simulated multi-channel brain MR images with known ground-truth tissue segmentation and also applied it to a dataset of MR images obtained during clinical trials of brain tumor chemotherapy. The results show that more accurate tumor segmentation results can be obtained by comparing with conventional multi-channel segmentation algorithms.

The "social brain" is highly sensitive to the mere presence of social information: An automated meta-analysis and an independent study.

PubMed

Tso, Ivy F; Rutherford, Saige; Fang, Yu; Angstadt, Mike; Taylor, Stephan F

2018-01-01

How the human brain processes social information is an increasingly researched topic in psychology and neuroscience, advancing our understanding of basic human cognition and psychopathologies. Neuroimaging studies typically seek to isolate one specific aspect of social cognition when trying to map its neural substrates. It is unclear if brain activation elicited by different social cognitive processes and task instructions are also spontaneously elicited by general social information. In this study, we investigated whether these brain regions are evoked by the mere presence of social information using an automated meta-analysis and confirmatory data from an independent study of simple appraisal of social vs. non-social images. Results of 1,000 published fMRI studies containing the keyword of "social" were subject to an automated meta-analysis (http://neurosynth.org). To confirm that significant brain regions in the meta-analysis were driven by a social effect, these brain regions were used as regions of interest (ROIs) to extract and compare BOLD fMRI signals of social vs. non-social conditions in the independent study. The NeuroSynth results indicated that the dorsal and ventral medial prefrontal cortex, posterior cingulate cortex, bilateral amygdala, bilateral occipito-temporal junction, right fusiform gyrus, bilateral temporal pole, and right inferior frontal gyrus are commonly engaged in studies with a prominent social element. The social-non-social contrast in the independent study showed a strong resemblance to the NeuroSynth map. ROI analyses revealed that a social effect was credible in 9 out of the 11 NeuroSynth regions in the independent dataset. The findings support the conclusion that the "social brain" is highly sensitive to the mere presence of social information.

Automated Outcome Classification of Computed Tomography Imaging Reports for Pediatric Traumatic Brain Injury.

PubMed

Yadav, Kabir; Sarioglu, Efsun; Choi, Hyeong Ah; Cartwright, Walter B; Hinds, Pamela S; Chamberlain, James M

2016-02-01

The authors have previously demonstrated highly reliable automated classification of free-text computed tomography (CT) imaging reports using a hybrid system that pairs linguistic (natural language processing) and statistical (machine learning) techniques. Previously performed for identifying the outcome of orbital fracture in unprocessed radiology reports from a clinical data repository, the performance has not been replicated for more complex outcomes. To validate automated outcome classification performance of a hybrid natural language processing (NLP) and machine learning system for brain CT imaging reports. The hypothesis was that our system has performance characteristics for identifying pediatric traumatic brain injury (TBI). This was a secondary analysis of a subset of 2,121 CT reports from the Pediatric Emergency Care Applied Research Network (PECARN) TBI study. For that project, radiologists dictated CT reports as free text, which were then deidentified and scanned as PDF documents. Trained data abstractors manually coded each report for TBI outcome. Text was extracted from the PDF files using optical character recognition. The data set was randomly split evenly for training and testing. Training patient reports were used as input to the Medical Language Extraction and Encoding (MedLEE) NLP tool to create structured output containing standardized medical terms and modifiers for negation, certainty, and temporal status. A random subset stratified by site was analyzed using descriptive quantitative content analysis to confirm identification of TBI findings based on the National Institute of Neurological Disorders and Stroke (NINDS) Common Data Elements project. Findings were coded for presence or absence, weighted by frequency of mentions, and past/future/indication modifiers were filtered. After combining with the manual reference standard, a decision tree classifier was created using data mining tools WEKA 3.7.5 and Salford Predictive Miner 7

Automated Outcome Classification of Computed Tomography Imaging Reports for Pediatric Traumatic Brain Injury

PubMed Central

Yadav, Kabir; Sarioglu, Efsun; Choi, Hyeong-Ah; Cartwright, Walter B.; Hinds, Pamela S.; Chamberlain, James M.

2016-01-01

Background The authors have previously demonstrated highly reliable automated classification of free text computed tomography (CT) imaging reports using a hybrid system that pairs linguistic (natural language processing) and statistical (machine learning) techniques. Previously performed for identifying the outcome of orbital fracture in unprocessed radiology reports from a clinical data repository, the performance has not been replicated for more complex outcomes. Objectives To validate automated outcome classification performance of a hybrid natural language processing (NLP) and machine learning system for brain CT imaging reports. The hypothesis was that our system has performance characteristics for identifying pediatric traumatic brain injury (TBI). Methods This was a secondary analysis of a subset of 2,121 CT reports from the Pediatric Emergency Care Applied Research Network (PECARN) TBI study. For that project, radiologists dictated CT reports as free text, which were then de-identified and scanned as PDF documents. Trained data abstractors manually coded each report for TBI outcome. Text was extracted from the PDF files using optical character recognition. The dataset was randomly split evenly for training and testing. Training patient reports were used as input to the Medical Language Extraction and Encoding (MedLEE) NLP tool to create structured output containing standardized medical terms and modifiers for negation, certainty, and temporal status. A random subset stratified by site was analyzed using descriptive quantitative content analysis to confirm identification of TBI findings based upon the National Institute of Neurological Disorders and Stroke Common Data Elements project. Findings were coded for presence or absence, weighted by frequency of mentions, and past/future/indication modifiers were filtered. After combining with the manual reference standard, a decision tree classifier was created using data mining tools WEKA 3.7.5 and Salford

Automated tissue segmentation of MR brain images in the presence of white matter lesions.

PubMed

Valverde, Sergi; Oliver, Arnau; Roura, Eloy; González-Villà, Sandra; Pareto, Deborah; Vilanova, Joan C; Ramió-Torrentà, Lluís; Rovira, Àlex; Lladó, Xavier

2017-01-01

Over the last few years, the increasing interest in brain tissue volume measurements on clinical settings has led to the development of a wide number of automated tissue segmentation methods. However, white matter lesions are known to reduce the performance of automated tissue segmentation methods, which requires manual annotation of the lesions and refilling them before segmentation, which is tedious and time-consuming. Here, we propose a new, fully automated T1-w/FLAIR tissue segmentation approach designed to deal with images in the presence of WM lesions. This approach integrates a robust partial volume tissue segmentation with WM outlier rejection and filling, combining intensity and probabilistic and morphological prior maps. We evaluate the performance of this method on the MRBrainS13 tissue segmentation challenge database, which contains images with vascular WM lesions, and also on a set of Multiple Sclerosis (MS) patient images. On both databases, we validate the performance of our method with other state-of-the-art techniques. On the MRBrainS13 data, the presented approach was at the time of submission the best ranked unsupervised intensity model method of the challenge (7th position) and clearly outperformed the other unsupervised pipelines such as FAST and SPM12. On MS data, the differences in tissue segmentation between the images segmented with our method and the same images where manual expert annotations were used to refill lesions on T1-w images before segmentation were lower or similar to the best state-of-the-art pipeline incorporating automated lesion segmentation and filling. Our results show that the proposed pipeline achieved very competitive results on both vascular and MS lesions. A public version of this approach is available to download for the neuro-imaging community. Copyright © 2016 Elsevier B.V. All rights reserved.

Automated data processing of { 1H-decoupled} 13C MR spectra acquired from human brain in vivo

NASA Astrophysics Data System (ADS)

Shic, Frederick; Ross, Brian

2003-06-01

In clinical 13C infusion studies, broadband excitation of 200 ppm of the human brain yields 13C MR spectra with a time resolution of 2-5 min and generates up to 2000 metabolite peaks over 2 h. We describe a fast, automated, observer-independent technique for processing { 1H-decoupled} 13C spectra. Quantified 13C spectroscopic signals, before and after the administration of [1- 13C]glucose and/or [1- 13C]acetate in human subjects are determined. Stepwise improvements of data processing are illustrated by examples of normal and pathological results. Variation in analysis of individual 13C resonances ranged between 2 and 14%. Using this method it is possible to reliably identify subtle metabolic effects of brain disease including Alzheimer's disease and epilepsy.

[Time consumption and quality of an automated fusion tool for SPECT and MRI images of the brain].

PubMed

Fiedler, E; Platsch, G; Schwarz, A; Schmiedehausen, K; Tomandl, B; Huk, W; Rupprecht, Th; Rahn, N; Kuwert, T

2003-10-01

Although the fusion of images from different modalities may improve diagnostic accuracy, it is rarely used in clinical routine work due to logistic problems. Therefore we evaluated performance and time needed for fusing MRI and SPECT images using a semiautomated dedicated software. PATIENTS, MATERIAL AND METHOD: In 32 patients regional cerebral blood flow was measured using (99m)Tc ethylcystein dimer (ECD) and the three-headed SPECT camera MultiSPECT 3. MRI scans of the brain were performed using either a 0,2 T Open or a 1,5 T Sonata. Twelve of the MRI data sets were acquired using a 3D-T1w MPRAGE sequence, 20 with a 2D acquisition technique and different echo sequences. Image fusion was performed on a Syngo workstation using an entropy minimizing algorithm by an experienced user of the software. The fusion results were classified. We measured the time needed for the automated fusion procedure and in case of need that for manual realignment after automated, but insufficient fusion. The mean time of the automated fusion procedure was 123 s. It was for the 2D significantly shorter than for the 3D MRI datasets. For four of the 2D data sets and two of the 3D data sets an optimal fit was reached using the automated approach. The remaining 26 data sets required manual correction. The sum of the time required for automated fusion and that needed for manual correction averaged 320 s (50-886 s). The fusion of 3D MRI data sets lasted significantly longer than that of the 2D MRI data. The automated fusion tool delivered in 20% an optimal fit, in 80% manual correction was necessary. Nevertheless, each of the 32 SPECT data sets could be merged in less than 15 min with the corresponding MRI data, which seems acceptable for clinical routine use.

An algorithm for automatic parameter adjustment for brain extraction in BrainSuite

NASA Astrophysics Data System (ADS)

Rajagopal, Gautham; Joshi, Anand A.; Leahy, Richard M.

2017-02-01

Brain Extraction (classification of brain and non-brain tissue) of MRI brain images is a crucial pre-processing step necessary for imaging-based anatomical studies of the human brain. Several automated methods and software tools are available for performing this task, but differences in MR image parameters (pulse sequence, resolution) and instrumentand subject-dependent noise and artefacts affect the performance of these automated methods. We describe and evaluate a method that automatically adapts the default parameters of the Brain Surface Extraction (BSE) algorithm to optimize a cost function chosen to reflect accurate brain extraction. BSE uses a combination of anisotropic filtering, Marr-Hildreth edge detection, and binary morphology for brain extraction. Our algorithm automatically adapts four parameters associated with these steps to maximize the brain surface area to volume ratio. We evaluate the method on a total of 109 brain volumes with ground truth brain masks generated by an expert user. A quantitative evaluation of the performance of the proposed algorithm showed an improvement in the mean (s.d.) Dice coefficient from 0.8969 (0.0376) for default parameters to 0.9509 (0.0504) for the optimized case. These results indicate that automatic parameter optimization can result in significant improvements in definition of the brain mask.

Skeleton-based region competition for automated gray matter and white matter segmentation of human brain MR images

NASA Astrophysics Data System (ADS)

Chu, Yong; Chen, Ya-Fang; Su, Min-Ying; Nalcioglu, Orhan

2005-04-01

Image segmentation is an essential process for quantitative analysis. Segmentation of brain tissues in magnetic resonance (MR) images is very important for understanding the structural-functional relationship for various pathological conditions, such as dementia vs. normal brain aging. Different brain regions are responsible for certain functions and may have specific implication for diagnosis. Segmentation may facilitate the analysis of different brain regions to aid in early diagnosis. Region competition has been recently proposed as an effective method for image segmentation by minimizing a generalized Bayes/MDL criterion. However, it is sensitive to initial conditions - the "seeds", therefore an optimal choice of "seeds" is necessary for accurate segmentation. In this paper, we present a new skeleton-based region competition algorithm for automated gray and white matter segmentation. Skeletons can be considered as good "seed regions" since they provide the morphological a priori information, thus guarantee a correct initial condition. Intensity gradient information is also added to the global energy function to achieve a precise boundary localization. This algorithm was applied to perform gray and white matter segmentation using simulated MRI images from a realistic digital brain phantom. Nine different brain regions were manually outlined for evaluation of the performance in these separate regions. The results were compared to the gold-standard measure to calculate the true positive and true negative percentages. In general, this method worked well with a 96% accuracy, although the performance varied in different regions. We conclude that the skeleton-based region competition is an effective method for gray and white matter segmentation.

Bayesian automated cortical segmentation for neonatal MRI

NASA Astrophysics Data System (ADS)

Chou, Zane; Paquette, Natacha; Ganesh, Bhavana; Wang, Yalin; Ceschin, Rafael; Nelson, Marvin D.; Macyszyn, Luke; Gaonkar, Bilwaj; Panigrahy, Ashok; Lepore, Natasha

2017-11-01

Several attempts have been made in the past few years to develop and implement an automated segmentation of neonatal brain structural MRI. However, accurate automated MRI segmentation remains challenging in this population because of the low signal-to-noise ratio, large partial volume effects and inter-individual anatomical variability of the neonatal brain. In this paper, we propose a learning method for segmenting the whole brain cortical grey matter on neonatal T2-weighted images. We trained our algorithm using a neonatal dataset composed of 3 fullterm and 4 preterm infants scanned at term equivalent age. Our segmentation pipeline combines the FAST algorithm from the FSL library software and a Bayesian segmentation approach to create a threshold matrix that minimizes the error of mislabeling brain tissue types. Our method shows promising results with our pilot training set. In both preterm and full-term neonates, automated Bayesian segmentation generates a smoother and more consistent parcellation compared to FAST, while successfully removing the subcortical structure and cleaning the edges of the cortical grey matter. This method show promising refinement of the FAST segmentation by considerably reducing manual input and editing required from the user, and further improving reliability and processing time of neonatal MR images. Further improvement will include a larger dataset of training images acquired from different manufacturers.

Improved plan quality with automated radiotherapy planning for whole brain with hippocampus sparing: a comparison to the RTOG 0933 trial.

PubMed

Krayenbuehl, J; Di Martino, M; Guckenberger, M; Andratschke, N

2017-10-02

Whole-brain radiation therapy (WBRT) with hippocampus sparing (HS) has been investigated by the radiation oncology working group (RTOG) 0933 trial for patients with multiple brain metastases. They showed a decrease of adverse neurocognitive effects with HS WBRT compared to WBRT alone. With the development of automated treatment planning system (aTPS) in the last years, a standardization of the plan quality at a high level was achieved. The goal of this study was to evaluate the feasibility of using an aTPS for the treatment of HS WBRT and see if the RTOG 0933 dose constraints could be achieved and improved. Ten consecutive patients treated with HS WBRT were enrolled in this study. 10 × 3 Gy was prescribed according to the RTOG 0933 protocol to 92% of the target volume (whole-brain excluding the hippocampus expanded by 5 mm in 3-dimensions). In contrast to RTOG 0933, the maximum allowed point dose to normal brain was significantly lowered and restricted to 36.5 Gy. All patients were planned with volumetric modulated arc therapy (VMAT) technique using four arcs. Plans were optimized using Auto-Planning (AP) (Philips Radiation Oncology Systems) with one single AP template and optimization. All the constraints from the RTOG 0933 trial were achieved. A significant improvement for the maximal dose to 2% of the brain with a reduction of 4 Gy was achieved (33.5 Gy vs. RTOG 37.5 Gy) and the minimum hippocampus dose was reduced by 10% (8.1 Gy vs. RTOG 9 Gy). A steep dose gradient around the hippocampus was achieved with a mean dose of 27.3 Gy at a distance between 0.5 cm and 1 cm from the hippocampus. The effective working time to optimize a plan was kept below 6'. Automated treatment planning for HS WBRT was able to fulfil all the recommendations from the RTOG 0933 study while significantly improving dose homogeneity and decreasing unnecessary hot spot in the normal brain. With this approach, a standardization of plan quality was achieved and the effective

Automated voxel classification used with atlas-guided diffuse optical tomography for assessment of functional brain networks in young and older adults.

PubMed

Li, Lin; Cazzell, Mary; Babawale, Olajide; Liu, Hanli

2016-10-01

Atlas-guided diffuse optical tomography (atlas-DOT) is a computational means to image changes in cortical hemodynamic signals during human brain activities. Graph theory analysis (GTA) is a network analysis tool commonly used in functional neuroimaging to study brain networks. Atlas-DOT has not been analyzed with GTA to derive large-scale brain connectivity/networks based on near-infrared spectroscopy (NIRS) measurements. We introduced an automated voxel classification (AVC) method that facilitated the use of GTA with atlas-DOT images by grouping unequal-sized finite element voxels into anatomically meaningful regions of interest within the human brain. The overall approach included volume segmentation, AVC, and cross-correlation. To demonstrate the usefulness of AVC, we applied reproducibility analysis to resting-state functional connectivity measurements conducted from 15 young adults in a two-week period. We also quantified and compared changes in several brain network metrics between young and older adults, which were in agreement with those reported by a previous positron emission tomography study. Overall, this study demonstrated that AVC is a useful means for facilitating integration or combination of atlas-DOT with GTA and thus for quantifying NIRS-based, voxel-wise resting-state functional brain networks.

A discriminative model-constrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3-D MRI.

PubMed

Wels, Michael; Carneiro, Gustavo; Aplas, Alexander; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin

2008-01-01

In this paper we present a fully automated approach to the segmentation of pediatric brain tumors in multi-spectral 3-D magnetic resonance images. It is a top-down segmentation approach based on a Markov random field (MRF) model that combines probabilistic boosting trees (PBT) and lower-level segmentation via graph cuts. The PBT algorithm provides a strong discriminative observation model that classifies tumor appearance while a spatial prior takes into account the pair-wise homogeneity in terms of classification labels and multi-spectral voxel intensities. The discriminative model relies not only on observed local intensities but also on surrounding context for detecting candidate regions for pathology. A mathematically sound formulation for integrating the two approaches into a unified statistical framework is given. The proposed method is applied to the challenging task of detection and delineation of pediatric brain tumors. This segmentation task is characterized by a high non-uniformity of both the pathology and the surrounding non-pathologic brain tissue. A quantitative evaluation illustrates the robustness of the proposed method. Despite dealing with more complicated cases of pediatric brain tumors the results obtained are mostly better than those reported for current state-of-the-art approaches to 3-D MR brain tumor segmentation in adult patients. The entire processing of one multi-spectral data set does not require any user interaction, and takes less time than previously proposed methods.

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI.

PubMed

Soltaninejad, Mohammadreza; Yang, Guang; Lambrou, Tryphon; Allinson, Nigel; Jones, Timothy L; Barrick, Thomas R; Howe, Franklyn A; Ye, Xujiong

2017-02-01

We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively. This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.

Rat brain digital stereotaxic white matter atlas with fine tract delineation in Paxinos space and its automated applications in DTI data analysis.

PubMed

Liang, Shengxiang; Wu, Shang; Huang, Qi; Duan, Shaofeng; Liu, Hua; Li, Yuxiao; Zhao, Shujun; Nie, Binbin; Shan, Baoci

2017-11-01

To automatically analyze diffusion tensor images of the rat brain via both voxel-based and ROI-based approaches, we constructed a new white matter atlas of the rat brain with fine tracts delineation in the Paxinos and Watson space. Unlike in previous studies, we constructed a digital atlas image from the latest edition of the Paxinos and Watson. This atlas contains 111 carefully delineated white matter fibers. A white matter network of rat brain based on anatomy was constructed by locating the intersection of all these tracts and recording the nuclei on the pathway of each white matter tract. Moreover, a compatible rat brain template from DTI images was created and standardized into the atlas space. To evaluate the automated application of the atlas in DTI data analysis, a group of rats with right-side middle cerebral artery occlusion (MCAO) and those without were enrolled in this study. The voxel-based analysis result shows that the brain region showing significant declines in signal in the MCAO rats was consistent with the occlusion position. We constructed a stereotaxic white matter atlas of the rat brain with fine tract delineation and a compatible template for the data analysis of DTI images of the rat brain. Copyright © 2017 Elsevier Inc. All rights reserved.

STAMPS: Software Tool for Automated MRI Post-processing on a supercomputer.

PubMed

Bigler, Don C; Aksu, Yaman; Miller, David J; Yang, Qing X

2009-08-01

This paper describes a Software Tool for Automated MRI Post-processing (STAMP) of multiple types of brain MRIs on a workstation and for parallel processing on a supercomputer (STAMPS). This software tool enables the automation of nonlinear registration for a large image set and for multiple MR image types. The tool uses standard brain MRI post-processing tools (such as SPM, FSL, and HAMMER) for multiple MR image types in a pipeline fashion. It also contains novel MRI post-processing features. The STAMP image outputs can be used to perform brain analysis using Statistical Parametric Mapping (SPM) or single-/multi-image modality brain analysis using Support Vector Machines (SVMs). Since STAMPS is PBS-based, the supercomputer may be a multi-node computer cluster or one of the latest multi-core computers.

Neural Signatures of Trust During Human-Automation Interactions

DTIC Science & Technology

2016-04-01

magnetic resonance imaging by manipulating the reliability of advice from a human or automated luggage inspector framed as experts. HAT and HHT were...human-human trust, human-automation trust, brain, functional magnetic resonance imaging 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18...behavioral X-ray luggage-screening task with functional magnetic resonance imaging (fMRI) and manipulated reliabilities of advice (unknown to the

An Automated Method for High-Definition Transcranial Direct Current Stimulation Modeling*

PubMed Central

Huang, Yu; Su, Yuzhuo; Rorden, Christopher; Dmochowski, Jacek; Datta, Abhishek; Parra, Lucas C.

2014-01-01

Targeted transcranial stimulation with electric currents requires accurate models of the current flow from scalp electrodes to the human brain. Idiosyncratic anatomy of individual brains and heads leads to significant variability in such current flows across subjects, thus, necessitating accurate individualized head models. Here we report on an automated processing chain that computes current distributions in the head starting from a structural magnetic resonance image (MRI). The main purpose of automating this process is to reduce the substantial effort currently required for manual segmentation, electrode placement, and solving of finite element models. In doing so, several weeks of manual labor were reduced to no more than 4 hours of computation time and minimal user interaction, while current-flow results for the automated method deviated by less than 27.9% from the manual method. Key facilitating factors are the addition of three tissue types (skull, scalp and air) to a state-of-the-art automated segmentation process, morphological processing to correct small but important segmentation errors, and automated placement of small electrodes based on easily reproducible standard electrode configurations. We anticipate that such an automated processing will become an indispensable tool to individualize transcranial direct current stimulation (tDCS) therapy. PMID:23367144

SU-D-BRD-06: Automated Population-Based Planning for Whole Brain Radiation Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schreibmann, E; Fox, T; Crocker, I

2014-06-01

Purpose: Treatment planning for whole brain radiation treatment is technically a simple process but in practice it takes valuable clinical time of repetitive and tedious tasks. This report presents a method that automatically segments the relevant target and normal tissues and creates a treatment plan in only a few minutes after patient simulation. Methods: Segmentation is performed automatically through morphological operations on the soft tissue. The treatment plan is generated by searching a database of previous cases for patients with similar anatomy. In this search, each database case is ranked in terms of similarity using a customized metric designed formore » sensitivity by including only geometrical changes that affect the dose distribution. The database case with the best match is automatically modified to replace relevant patient info and isocenter position while maintaining original beam and MLC settings. Results: Fifteen patients were used to validate the method. In each of these cases the anatomy was accurately segmented to mean Dice coefficients of 0.970 ± 0.008 for the brain, 0.846 ± 0.009 for the eyes and 0.672 ± 0.111 for the lens as compared to clinical segmentations. Each case was then subsequently matched against a database of 70 validated treatment plans and the best matching plan (termed auto-planned), was compared retrospectively with the clinical plans in terms of brain coverage and maximum doses to critical structures. Maximum doses were reduced by a maximum of 20.809 Gy for the left eye (mean 3.533), by 13.352 (1.311) for the right eye, and by 27.471 (4.856), 25.218 (6.315) for the left and right lens. Time from simulation to auto-plan was 3-4 minutes. Conclusion: Automated database- based matching is an alternative to classical treatment planning that improves quality while providing a cost—effective solution to planning through modifying previous validated plans to match a current patient's anatomy.« less

PyDBS: an automated image processing workflow for deep brain stimulation surgery.

PubMed

D'Albis, Tiziano; Haegelen, Claire; Essert, Caroline; Fernández-Vidal, Sara; Lalys, Florent; Jannin, Pierre

2015-02-01

Deep brain stimulation (DBS) is a surgical procedure for treating motor-related neurological disorders. DBS clinical efficacy hinges on precise surgical planning and accurate electrode placement, which in turn call upon several image processing and visualization tasks, such as image registration, image segmentation, image fusion, and 3D visualization. These tasks are often performed by a heterogeneous set of software tools, which adopt differing formats and geometrical conventions and require patient-specific parameterization or interactive tuning. To overcome these issues, we introduce in this article PyDBS, a fully integrated and automated image processing workflow for DBS surgery. PyDBS consists of three image processing pipelines and three visualization modules assisting clinicians through the entire DBS surgical workflow, from the preoperative planning of electrode trajectories to the postoperative assessment of electrode placement. The system's robustness, speed, and accuracy were assessed by means of a retrospective validation, based on 92 clinical cases. The complete PyDBS workflow achieved satisfactory results in 92 % of tested cases, with a median processing time of 28 min per patient. The results obtained are compatible with the adoption of PyDBS in clinical practice.

Automated MRI segmentation for individualized modeling of current flow in the human head.

PubMed

Huang, Yu; Dmochowski, Jacek P; Su, Yuzhuo; Datta, Abhishek; Rorden, Christopher; Parra, Lucas C

2013-12-01

High-definition transcranial direct current stimulation (HD-tDCS) and high-density electroencephalography require accurate models of current flow for precise targeting and current source reconstruction. At a minimum, such modeling must capture the idiosyncratic anatomy of the brain, cerebrospinal fluid (CSF) and skull for each individual subject. Currently, the process to build such high-resolution individualized models from structural magnetic resonance images requires labor-intensive manual segmentation, even when utilizing available automated segmentation tools. Also, accurate placement of many high-density electrodes on an individual scalp is a tedious procedure. The goal was to develop fully automated techniques to reduce the manual effort in such a modeling process. A fully automated segmentation technique based on Statical Parametric Mapping 8, including an improved tissue probability map and an automated correction routine for segmentation errors, was developed, along with an automated electrode placement tool for high-density arrays. The performance of these automated routines was evaluated against results from manual segmentation on four healthy subjects and seven stroke patients. The criteria include segmentation accuracy, the difference of current flow distributions in resulting HD-tDCS models and the optimized current flow intensities on cortical targets. The segmentation tool can segment out not just the brain but also provide accurate results for CSF, skull and other soft tissues with a field of view extending to the neck. Compared to manual results, automated segmentation deviates by only 7% and 18% for normal and stroke subjects, respectively. The predicted electric fields in the brain deviate by 12% and 29% respectively, which is well within the variability observed for various modeling choices. Finally, optimized current flow intensities on cortical targets do not differ significantly. Fully automated individualized modeling may now be feasible

Automated MRI Segmentation for Individualized Modeling of Current Flow in the Human Head

PubMed Central

Huang, Yu; Dmochowski, Jacek P.; Su, Yuzhuo; Datta, Abhishek; Rorden, Christopher; Parra, Lucas C.

2013-01-01

Objective High-definition transcranial direct current stimulation (HD-tDCS) and high-density electroencephalography (HD-EEG) require accurate models of current flow for precise targeting and current source reconstruction. At a minimum, such modeling must capture the idiosyncratic anatomy of brain, cerebrospinal fluid (CSF) and skull for each individual subject. Currently, the process to build such high-resolution individualized models from structural magnetic resonance images (MRI) requires labor-intensive manual segmentation, even when leveraging available automated segmentation tools. Also, accurate placement of many high-density electrodes on individual scalp is a tedious procedure. The goal was to develop fully automated techniques to reduce the manual effort in such a modeling process. Approach A fully automated segmentation technique based on Statical Parametric Mapping 8 (SPM8), including an improved tissue probability map (TPM) and an automated correction routine for segmentation errors, was developed, along with an automated electrode placement tool for high-density arrays. The performance of these automated routines was evaluated against results from manual segmentation on 4 healthy subjects and 7 stroke patients. The criteria include segmentation accuracy, the difference of current flow distributions in resulting HD-tDCS models and the optimized current flow intensities on cortical targets. Main results The segmentation tool can segment out not just the brain but also provide accurate results for CSF, skull and other soft tissues with a field of view (FOV) extending to the neck. Compared to manual results, automated segmentation deviates by only 7% and 18% for normal and stroke subjects, respectively. The predicted electric fields in the brain deviate by 12% and 29% respectively, which is well within the variability observed for various modeling choices. Finally, optimized current flow intensities on cortical targets do not differ significantly

Automated MRI segmentation for individualized modeling of current flow in the human head

NASA Astrophysics Data System (ADS)

Huang, Yu; Dmochowski, Jacek P.; Su, Yuzhuo; Datta, Abhishek; Rorden, Christopher; Parra, Lucas C.

2013-12-01

Objective. High-definition transcranial direct current stimulation (HD-tDCS) and high-density electroencephalography require accurate models of current flow for precise targeting and current source reconstruction. At a minimum, such modeling must capture the idiosyncratic anatomy of the brain, cerebrospinal fluid (CSF) and skull for each individual subject. Currently, the process to build such high-resolution individualized models from structural magnetic resonance images requires labor-intensive manual segmentation, even when utilizing available automated segmentation tools. Also, accurate placement of many high-density electrodes on an individual scalp is a tedious procedure. The goal was to develop fully automated techniques to reduce the manual effort in such a modeling process. Approach. A fully automated segmentation technique based on Statical Parametric Mapping 8, including an improved tissue probability map and an automated correction routine for segmentation errors, was developed, along with an automated electrode placement tool for high-density arrays. The performance of these automated routines was evaluated against results from manual segmentation on four healthy subjects and seven stroke patients. The criteria include segmentation accuracy, the difference of current flow distributions in resulting HD-tDCS models and the optimized current flow intensities on cortical targets.Main results. The segmentation tool can segment out not just the brain but also provide accurate results for CSF, skull and other soft tissues with a field of view extending to the neck. Compared to manual results, automated segmentation deviates by only 7% and 18% for normal and stroke subjects, respectively. The predicted electric fields in the brain deviate by 12% and 29% respectively, which is well within the variability observed for various modeling choices. Finally, optimized current flow intensities on cortical targets do not differ significantly.Significance. Fully

Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization.

PubMed

Sauwen, Nicolas; Acou, Marjan; Sima, Diana M; Veraart, Jelle; Maes, Frederik; Himmelreich, Uwe; Achten, Eric; Huffel, Sabine Van

2017-05-04

Segmentation of gliomas in multi-parametric (MP-)MR images is challenging due to their heterogeneous nature in terms of size, appearance and location. Manual tumor segmentation is a time-consuming task and clinical practice would benefit from (semi-) automated segmentation of the different tumor compartments. We present a semi-automated framework for brain tumor segmentation based on non-negative matrix factorization (NMF) that does not require prior training of the method. L1-regularization is incorporated into the NMF objective function to promote spatial consistency and sparseness of the tissue abundance maps. The pathological sources are initialized through user-defined voxel selection. Knowledge about the spatial location of the selected voxels is combined with tissue adjacency constraints in a post-processing step to enhance segmentation quality. The method is applied to an MP-MRI dataset of 21 high-grade glioma patients, including conventional, perfusion-weighted and diffusion-weighted MRI. To assess the effect of using MP-MRI data and the L1-regularization term, analyses are also run using only conventional MRI and without L1-regularization. Robustness against user input variability is verified by considering the statistical distribution of the segmentation results when repeatedly analyzing each patient's dataset with a different set of random seeding points. Using L1-regularized semi-automated NMF segmentation, mean Dice-scores of 65%, 74 and 80% are found for active tumor, the tumor core and the whole tumor region. Mean Hausdorff distances of 6.1 mm, 7.4 mm and 8.2 mm are found for active tumor, the tumor core and the whole tumor region. Lower Dice-scores and higher Hausdorff distances are found without L1-regularization and when only considering conventional MRI data. Based on the mean Dice-scores and Hausdorff distances, segmentation results are competitive with state-of-the-art in literature. Robust results were found for most patients, although

Migraine Subclassification via a Data-Driven Automated Approach Using Multimodality Factor Mixture Modeling of Brain Structure Measurements.

PubMed

Schwedt, Todd J; Si, Bing; Li, Jing; Wu, Teresa; Chong, Catherine D

2017-07-01

The current subclassification of migraine is according to headache frequency and aura status. The variability in migraine symptoms, disease course, and response to treatment suggest the presence of additional heterogeneity or subclasses within migraine. The study objective was to subclassify migraine via a data-driven approach, identifying latent factors by jointly exploiting multiple sets of brain structural features obtained via magnetic resonance imaging (MRI). Migraineurs (n = 66) and healthy controls (n = 54) had brain MRI measurements of cortical thickness, cortical surface area, and volumes for 68 regions. A multimodality factor mixture model was used to subclassify MRIs and to determine the brain structural factors that most contributed to the subclassification. Clinical characteristics of subjects in each subgroup were compared. Automated MRI classification divided the subjects into two subgroups. Migraineurs in subgroup #1 had more severe allodynia symptoms during migraines (6.1 ± 5.3 vs. 3.6 ± 3.2, P = .03), more years with migraine (19.2 ± 11.3 years vs 13 ± 8.3 years, P = .01), and higher Migraine Disability Assessment (MIDAS) scores (25 ± 22.9 vs 15.7 ± 12.2, P = .04). There were not differences in headache frequency or migraine aura status between the two subgroups. Data-driven subclassification of brain MRIs based upon structural measurements identified two subgroups. Amongst migraineurs, the subgroups differed in allodynia symptom severity, years with migraine, and migraine-related disability. Since allodynia is associated with this imaging-based subclassification of migraine and prior publications suggest that allodynia impacts migraine treatment response and disease prognosis, future migraine diagnostic criteria could consider allodynia when defining migraine subgroups. © 2017 American Headache Society.

Multi-spectral brain tissue segmentation using automatically trained k-Nearest-Neighbor classification.

PubMed

Vrooman, Henri A; Cocosco, Chris A; van der Lijn, Fedde; Stokking, Rik; Ikram, M Arfan; Vernooij, Meike W; Breteler, Monique M B; Niessen, Wiro J

2007-08-01

Conventional k-Nearest-Neighbor (kNN) classification, which has been successfully applied to classify brain tissue in MR data, requires training on manually labeled subjects. This manual labeling is a laborious and time-consuming procedure. In this work, a new fully automated brain tissue classification procedure is presented, in which kNN training is automated. This is achieved by non-rigidly registering the MR data with a tissue probability atlas to automatically select training samples, followed by a post-processing step to keep the most reliable samples. The accuracy of the new method was compared to rigid registration-based training and to conventional kNN-based segmentation using training on manually labeled subjects for segmenting gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) in 12 data sets. Furthermore, for all classification methods, the performance was assessed when varying the free parameters. Finally, the robustness of the fully automated procedure was evaluated on 59 subjects. The automated training method using non-rigid registration with a tissue probability atlas was significantly more accurate than rigid registration. For both automated training using non-rigid registration and for the manually trained kNN classifier, the difference with the manual labeling by observers was not significantly larger than inter-observer variability for all tissue types. From the robustness study, it was clear that, given an appropriate brain atlas and optimal parameters, our new fully automated, non-rigid registration-based method gives accurate and robust segmentation results. A similarity index was used for comparison with manually trained kNN. The similarity indices were 0.93, 0.92 and 0.92, for CSF, GM and WM, respectively. It can be concluded that our fully automated method using non-rigid registration may replace manual segmentation, and thus that automated brain tissue segmentation without laborious manual training is feasible.

Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment.

PubMed

Aricò, Pietro; Borghini, Gianluca; Di Flumeri, Gianluca; Colosimo, Alfredo; Bonelli, Stefano; Golfetti, Alessia; Pozzi, Simone; Imbert, Jean-Paul; Granger, Géraud; Benhacene, Raïlane; Babiloni, Fabio

2016-01-01

Adaptive Automation (AA) is a promising approach to keep the task workload demand within appropriate levels in order to avoid both the under - and over-load conditions, hence enhancing the overall performance and safety of the human-machine system. The main issue on the use of AA is how to trigger the AA solutions without affecting the operative task. In this regard, passive Brain-Computer Interface (pBCI) systems are a good candidate to activate automation, since they are able to gather information about the covert behavior (e.g., mental workload) of a subject by analyzing its neurophysiological signals (i.e., brain activity), and without interfering with the ongoing operational activity. We proposed a pBCI system able to trigger AA solutions integrated in a realistic Air Traffic Management (ATM) research simulator developed and hosted at ENAC (É cole Nationale de l'Aviation Civile of Toulouse, France). Twelve Air Traffic Controller (ATCO) students have been involved in the experiment and they have been asked to perform ATM scenarios with and without the support of the AA solutions. Results demonstrated the effectiveness of the proposed pBCI system, since it enabled the AA mostly during the high-demanding conditions (i.e., overload situations) inducing a reduction of the mental workload under which the ATCOs were operating. On the contrary, as desired, the AA was not activated when workload level was under the threshold, to prevent too low demanding conditions that could bring the operator's workload level toward potentially dangerous conditions of underload.

Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment

PubMed Central

Aricò, Pietro; Borghini, Gianluca; Di Flumeri, Gianluca; Colosimo, Alfredo; Bonelli, Stefano; Golfetti, Alessia; Pozzi, Simone; Imbert, Jean-Paul; Granger, Géraud; Benhacene, Raïlane; Babiloni, Fabio

2016-01-01

Adaptive Automation (AA) is a promising approach to keep the task workload demand within appropriate levels in order to avoid both the under- and over-load conditions, hence enhancing the overall performance and safety of the human-machine system. The main issue on the use of AA is how to trigger the AA solutions without affecting the operative task. In this regard, passive Brain-Computer Interface (pBCI) systems are a good candidate to activate automation, since they are able to gather information about the covert behavior (e.g., mental workload) of a subject by analyzing its neurophysiological signals (i.e., brain activity), and without interfering with the ongoing operational activity. We proposed a pBCI system able to trigger AA solutions integrated in a realistic Air Traffic Management (ATM) research simulator developed and hosted at ENAC (École Nationale de l'Aviation Civile of Toulouse, France). Twelve Air Traffic Controller (ATCO) students have been involved in the experiment and they have been asked to perform ATM scenarios with and without the support of the AA solutions. Results demonstrated the effectiveness of the proposed pBCI system, since it enabled the AA mostly during the high-demanding conditions (i.e., overload situations) inducing a reduction of the mental workload under which the ATCOs were operating. On the contrary, as desired, the AA was not activated when workload level was under the threshold, to prevent too low demanding conditions that could bring the operator's workload level toward potentially dangerous conditions of underload. PMID:27833542

Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors

PubMed Central

Sanjuán, Ana; Price, Cathy J.; Mancini, Laura; Josse, Goulven; Grogan, Alice; Yamamoto, Adam K.; Geva, Sharon; Leff, Alex P.; Yousry, Tarek A.; Seghier, Mohamed L.

2013-01-01

Brain tumors can have different shapes or locations, making their identification very challenging. In functional MRI, it is not unusual that patients have only one anatomical image due to time and financial constraints. Here, we provide a modified automatic lesion identification (ALI) procedure which enables brain tumor identification from single MR images. Our method rests on (A) a modified segmentation-normalization procedure with an explicit “extra prior” for the tumor and (B) an outlier detection procedure for abnormal voxel (i.e., tumor) classification. To minimize tissue misclassification, the segmentation-normalization procedure requires prior information of the tumor location and extent. We therefore propose that ALI is run iteratively so that the output of Step B is used as a patient-specific prior in Step A. We test this procedure on real T1-weighted images from 18 patients, and the results were validated in comparison to two independent observers' manual tracings. The automated procedure identified the tumors successfully with an excellent agreement with the manual segmentation (area under the ROC curve = 0.97 ± 0.03). The proposed procedure increases the flexibility and robustness of the ALI tool and will be particularly useful for lesion-behavior mapping studies, or when lesion identification and/or spatial normalization are problematic. PMID:24381535

A Fully Automated Method for Quantifying and Localizing White Matter Hyperintensities on MR Images

PubMed Central

Wu, Minjie; Rosano, Caterina; Butters, Meryl; Whyte, Ellen; Nable, Megan; Crooks, Ryan; Meltzer, Carolyn C.; Reynolds, Charles F.; Aizenstein3, Howard J.

2006-01-01

White matter hyperintensities (WMH), commonly found on T2-weighted FLAIR brain MR images in the elderly, are associated with a number of neuropsychiatric disorders, including vascular dementia, Alzheimer’s disease, and late-life depression. Previous MRI studies of WMHs have primarily relied on the subjective and global (i.e., full-brain) ratings of WMH grade. In the current study we implement and validate an automated method for quantifying and localizing WMHs. We adapt a fuzzy connected algorithm to automate the segmentation of WMHs and use a demons-based image registration to automate the anatomic localization of the WMHs using the Johns Hopkins University White Matter Atlas. The method is validated using the brain MR images acquired from eleven elderly subjects with late-onset late-life depression (LLD) and eight elderly controls. This dataset was chosen because LLD subjects are known to have significant WMH burden. The volumes of WMH identified in our automated method are compared with the accepted gold standard (manual ratings). A significant correlation of the automated method and the manual ratings is found (P<0.0001), thus demonstrating similar WMH quantifications of both methods. As has been shown in other studies e.g. (Taylor, et al. 2003)), we found there was a significantly greater WMH burden in the LLD subjects versus the controls for both the manual and automated method. The effect size was greater for the automated method, suggesting that it is a more specific measure. Additionally, we describe the anatomic localization of the WMHs in LLD subjects as well as in the control subjects, and detect the regions of interest (ROIs) specific for the WMH burden of LLD patients. Given the emergence of large neuroimage databases, techniques, such as that described here, will allow for a better understanding of the relationship between WMHs and neuropsychiatric disorders. PMID:17097277

Mapping social behavior-induced brain activation at cellular resolution in the mouse

PubMed Central

Kim, Yongsoo; Venkataraju, Kannan Umadevi; Pradhan, Kith; Mende, Carolin; Taranda, Julian; Turaga, Srinivas C.; Arganda-Carreras, Ignacio; Ng, Lydia; Hawrylycz, Michael J.; Rockland, Kathleen; Seung, H. Sebastian; Osten, Pavel

2014-01-01

Understanding how brain activation mediates behaviors is a central goal of systems neuroscience. Here we apply an automated method for mapping brain activation in the mouse in order to probe how sex-specific social behaviors are represented in the male brain. Our method uses the immediate early gene c-fos, a marker of neuronal activation, visualized by serial two-photon tomography: the c-fos-GFP-positive neurons are computationally detected, their distribution is registered to a reference brain and a brain atlas, and their numbers are analyzed by statistical tests. Our results reveal distinct and shared female and male interaction-evoked patterns of male brain activation representing sex discrimination and social recognition. We also identify brain regions whose degree of activity correlates to specific features of social behaviors and estimate the total numbers and the densities of activated neurons per brain areas. Our study opens the door to automated screening of behavior-evoked brain activation in the mouse. PMID:25558063

Semi-automated and automated glioma grading using dynamic susceptibility-weighted contrast-enhanced perfusion MRI relative cerebral blood volume measurements.

PubMed

Friedman, S N; Bambrough, P J; Kotsarini, C; Khandanpour, N; Hoggard, N

2012-12-01

Despite the established role of MRI in the diagnosis of brain tumours, histopathological assessment remains the clinically used technique, especially for the glioma group. Relative cerebral blood volume (rCBV) is a dynamic susceptibility-weighted contrast-enhanced perfusion MRI parameter that has been shown to correlate to tumour grade, but assessment requires a specialist and is time consuming. We developed analysis software to determine glioma gradings from perfusion rCBV scans in a manner that is quick, easy and does not require a specialist operator. MRI perfusion data from 47 patients with different histopathological grades of glioma were analysed with custom-designed software. Semi-automated analysis was performed with a specialist and non-specialist operator separately determining the maximum rCBV value corresponding to the tumour. Automated histogram analysis was performed by calculating the mean, standard deviation, median, mode, skewness and kurtosis of rCBV values. All values were compared with the histopathologically assessed tumour grade. A strong correlation between specialist and non-specialist observer measurements was found. Significantly different values were obtained between tumour grades using both semi-automated and automated techniques, consistent with previous results. The raw (unnormalised) data single-pixel maximum rCBV semi-automated analysis value had the strongest correlation with glioma grade. Standard deviation of the raw data had the strongest correlation of the automated analysis. Semi-automated calculation of raw maximum rCBV value was the best indicator of tumour grade and does not require a specialist operator. Both semi-automated and automated MRI perfusion techniques provide viable non-invasive alternatives to biopsy for glioma tumour grading.

Putting the brain to work: neuroergonomics past, present, and future.

PubMed

Parasuraman, Raja; Wilson, Glenn F

2008-06-01

The authors describe research and applications in prominent areas of neuroergonomics. Because human factors/ergonomics examines behavior and mind at work, it should include the study of brain mechanisms underlying human performance. Neuroergonomic studies are reviewed in four areas: workload and vigilance, adaptive automation, neuroengineering, and molecular genetics and individual differences. Neuroimaging studies have helped identify the components of mental workload, workload assessment in complex tasks, and resource depletion in vigilance. Furthermore, real-time neurocognitive assessment of workload can trigger adaptive automation. Neural measures can also drive brain-computer interfaces to provide disabled users new communication channels. Finally, variants of particular genes can be associated with individual differences in specific cognitive functions. Neuroergonomics shows that considering what makes work possible - the human brain - can enrich understanding of the use of technology by humans and can inform technological design. Applications of neuroergonomics include the assessment of operator workload and vigilance, implementation of real-time adaptive automation, neuroengineering for people with disabilities, and design of selection and training methods.

Automated Whole Brain Tractography Affects Preoperative Surgical Decision Making.

PubMed

Zakaria, Hesham; Haider, Sameah; Lee, Ian

2017-09-06

Surgery in and around eloquent brain structures poses a technical challenge when the goal of surgery is maximal safe resection. Magnetic resonance imaging (MRI) has revolutionized the diagnosis and treatment of neurological disorders, but tractography still remains limited in terms of utility because of the requisite manual labor and time required combined with the high risk of bias and inaccuracy. Automated whole brain tractography (AWBT) has simplified this workflow, overcoming historical barriers, and allowing for integration into modern neuronavigation. However, current literature showing the usefulness of this new technology is limited. In this study, we aimed to illustrate the utility of AWBT during cranial surgery and its ability to affect presurgical and intraoperative clinical decision making. We performed a retrospective chart review of cases that underwent AWBT for one year from July 2016 to July 2017. All patients underwent conventional anatomic MRI with and without contrast sequences, in addition to diffusion tensor imaging (DTI) on a 3 Tesla MRI scanner (Ingenia 3.0T, Philips, Amsterdam NL). Post-hoc AWBT processing was performed on a separate workstation. Patients were subsequently grouped into those that had undergone either language or motor mapping and those that did not. We compared both sets of patients to see any differences in patient age, sex, laterality of surgery, depth of resection from cortical surface, and smallest distance between the lesion and adjacent eloquent white matter tracts. We identified illustrative cases which demonstrated the ability of AWBT to affect surgical decision making. In this single-center series, we identified 73 total patients who underwent AWBT for intracranial surgery, of which 28 patients underwent either speech or language mapping. When comparing mapping to non-mapping patients, we found no difference with respect to age, gender, laterality of surgery, or whether the surgery was a revision. The distance

Unsupervised Decoding of Long-Term, Naturalistic Human Neural Recordings with Automated Video and Audio Annotations

PubMed Central

Wang, Nancy X. R.; Olson, Jared D.; Ojemann, Jeffrey G.; Rao, Rajesh P. N.; Brunton, Bingni W.

2016-01-01

Fully automated decoding of human activities and intentions from direct neural recordings is a tantalizing challenge in brain-computer interfacing. Implementing Brain Computer Interfaces (BCIs) outside carefully controlled experiments in laboratory settings requires adaptive and scalable strategies with minimal supervision. Here we describe an unsupervised approach to decoding neural states from naturalistic human brain recordings. We analyzed continuous, long-term electrocorticography (ECoG) data recorded over many days from the brain of subjects in a hospital room, with simultaneous audio and video recordings. We discovered coherent clusters in high-dimensional ECoG recordings using hierarchical clustering and automatically annotated them using speech and movement labels extracted from audio and video. To our knowledge, this represents the first time techniques from computer vision and speech processing have been used for natural ECoG decoding. Interpretable behaviors were decoded from ECoG data, including moving, speaking and resting; the results were assessed by comparison with manual annotation. Discovered clusters were projected back onto the brain revealing features consistent with known functional areas, opening the door to automated functional brain mapping in natural settings. PMID:27148018

PaCER - A fully automated method for electrode trajectory and contact reconstruction in deep brain stimulation.

PubMed

Husch, Andreas; V Petersen, Mikkel; Gemmar, Peter; Goncalves, Jorge; Hertel, Frank

2018-01-01

Deep brain stimulation (DBS) is a neurosurgical intervention where electrodes are permanently implanted into the brain in order to modulate pathologic neural activity. The post-operative reconstruction of the DBS electrodes is important for an efficient stimulation parameter tuning. A major limitation of existing approaches for electrode reconstruction from post-operative imaging that prevents the clinical routine use is that they are manual or semi-automatic, and thus both time-consuming and subjective. Moreover, the existing methods rely on a simplified model of a straight line electrode trajectory, rather than the more realistic curved trajectory. The main contribution of this paper is that for the first time we present a highly accurate and fully automated method for electrode reconstruction that considers curved trajectories. The robustness of our proposed method is demonstrated using a multi-center clinical dataset consisting of N  = 44 electrodes. In all cases the electrode trajectories were successfully identified and reconstructed. In addition, the accuracy is demonstrated quantitatively using a high-accuracy phantom with known ground truth. In the phantom experiment, the method could detect individual electrode contacts with high accuracy and the trajectory reconstruction reached an error level below 100 μm (0.046 ± 0.025 mm). An implementation of the method is made publicly available such that it can directly be used by researchers or clinicians. This constitutes an important step towards future integration of lead reconstruction into standard clinical care.

Validated Automatic Brain Extraction of Head CT Images

PubMed Central

Muschelli, John; Ullman, Natalie L.; Mould, W. Andrew; Vespa, Paul; Hanley, Daniel F.; Crainiceanu, Ciprian M.

2015-01-01

Background X-ray Computed Tomography (CT) imaging of the brain is commonly used in diagnostic settings. Although CT scans are primarily used in clinical practice, they are increasingly used in research. A fundamental processing step in brain imaging research is brain extraction – the process of separating the brain tissue from all other tissues. Methods for brain extraction have either been 1) validated but not fully automated, or 2) fully automated and informally proposed, but never formally validated. Aim To systematically analyze and validate the performance of FSL's brain extraction tool (BET) on head CT images of patients with intracranial hemorrhage. This was done by comparing the manual gold standard with the results of several versions of automatic brain extraction and by estimating the reliability of automated segmentation of longitudinal scans. The effects of the choice of BET parameters and data smoothing is studied and reported. Methods All images were thresholded using a 0 – 100 Hounsfield units (HU) range. In one variant of the pipeline, data were smoothed using a 3-dimensional Gaussian kernel (σ = 1mm3) and re-thresholded to 0 – 100 HU; in the other, data were not smoothed. BET was applied using 1 of 3 fractional intensity (FI) thresholds: 0.01, 0.1, or 0.35 and any holes in the brain mask were filled. For validation against a manual segmentation, 36 images from patients with intracranial hemorrhage were selected from 19 different centers from the MISTIE (Minimally Invasive Surgery plus recombinant-tissue plasminogen activator for Intracerebral Evacuation) stroke trial. Intracranial masks of the brain were manually created by one expert CT reader. The resulting brain tissue masks were quantitatively compared to the manual segmentations using sensitivity, specificity, accuracy, and the Dice Similarity Index (DSI). Brain extraction performance across smoothing and FI thresholds was compared using the Wilcoxon signed-rank test. The intracranial

Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification.

PubMed

Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V; Robles, Montserrat; Aparici, F; Martí-Bonmatí, L; García-Gómez, Juan M

2015-01-01

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.

Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

PubMed Central

Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V.; Robles, Montserrat; Aparici, F.; Martí-Bonmatí, L.; García-Gómez, Juan M.

2015-01-01

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation. PMID:25978453

Parallel workflow tools to facilitate human brain MRI post-processing

PubMed Central

Cui, Zaixu; Zhao, Chenxi; Gong, Gaolang

2015-01-01

Multi-modal magnetic resonance imaging (MRI) techniques are widely applied in human brain studies. To obtain specific brain measures of interest from MRI datasets, a number of complex image post-processing steps are typically required. Parallel workflow tools have recently been developed, concatenating individual processing steps and enabling fully automated processing of raw MRI data to obtain the final results. These workflow tools are also designed to make optimal use of available computational resources and to support the parallel processing of different subjects or of independent processing steps for a single subject. Automated, parallel MRI post-processing tools can greatly facilitate relevant brain investigations and are being increasingly applied. In this review, we briefly summarize these parallel workflow tools and discuss relevant issues. PMID:26029043

An automated and fast approach to detect single-trial visual evoked potentials with application to brain-computer interface.

PubMed

Tu, Yiheng; Hung, Yeung Sam; Hu, Li; Huang, Gan; Hu, Yong; Zhang, Zhiguo

2014-12-01

This study aims (1) to develop an automated and fast approach for detecting visual evoked potentials (VEPs) in single trials and (2) to apply the single-trial VEP detection approach in designing a real-time and high-performance brain-computer interface (BCI) system. The single-trial VEP detection approach uses common spatial pattern (CSP) as a spatial filter and wavelet filtering (WF) a temporal-spectral filter to jointly enhance the signal-to-noise ratio (SNR) of single-trial VEPs. The performance of the joint spatial-temporal-spectral filtering approach was assessed in a four-command VEP-based BCI system. The offline classification accuracy of the BCI system was significantly improved from 67.6±12.5% (raw data) to 97.3±2.1% (data filtered by CSP and WF). The proposed approach was successfully implemented in an online BCI system, where subjects could make 20 decisions in one minute with classification accuracy of 90%. The proposed single-trial detection approach is able to obtain robust and reliable VEP waveform in an automatic and fast way and it is applicable in VEP based online BCI systems. This approach provides a real-time and automated solution for single-trial detection of evoked potentials or event-related potentials (EPs/ERPs) in various paradigms, which could benefit many applications such as BCI and intraoperative monitoring. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Electroencephalogram Signal Classification for Automated Epileptic Seizure Detection Using Genetic Algorithm

PubMed Central

Nanthini, B. Suguna; Santhi, B.

2017-01-01

Background: Epilepsy causes when the repeated seizure occurs in the brain. Electroencephalogram (EEG) test provides valuable information about the brain functions and can be useful to detect brain disorder, especially for epilepsy. In this study, application for an automated seizure detection model has been introduced successfully. Materials and Methods: The EEG signals are decomposed into sub-bands by discrete wavelet transform using db2 (daubechies) wavelet. The eight statistical features, the four gray level co-occurrence matrix and Renyi entropy estimation with four different degrees of order, are extracted from the raw EEG and its sub-bands. Genetic algorithm (GA) is used to select eight relevant features from the 16 dimension features. The model has been trained and tested using support vector machine (SVM) classifier successfully for EEG signals. The performance of the SVM classifier is evaluated for two different databases. Results: The study has been experimented through two different analyses and achieved satisfactory performance for automated seizure detection using relevant features as the input to the SVM classifier. Conclusion: Relevant features using GA give better accuracy performance for seizure detection. PMID:28781480

Pediatric Brain Extraction Using Learning-based Meta-algorithm

PubMed Central

Shi, Feng; Wang, Li; Dai, Yakang; Gilmore, John H.; Lin, Weili; Shen, Dinggang

2012-01-01

Magnetic resonance imaging of pediatric brain provides valuable information for early brain development studies. Automated brain extraction is challenging due to the small brain size and dynamic change of tissue contrast in the developing brains. In this paper, we propose a novel Learning Algorithm for Brain Extraction and Labeling (LABEL) specially for the pediatric MR brain images. The idea is to perform multiple complementary brain extractions on a given testing image by using a meta-algorithm, including BET and BSE, where the parameters of each run of the meta-algorithm are effectively learned from the training data. Also, the representative subjects are selected as exemplars and used to guide brain extraction of new subjects in different age groups. We further develop a level-set based fusion method to combine multiple brain extractions together with a closed smooth surface for obtaining the final extraction. The proposed method has been extensively evaluated in subjects of three representative age groups, such as neonate (less than 2 months), infant (1–2 years), and child (5–18 years). Experimental results show that, with 45 subjects for training (15 neonates, 15 infant, and 15 children), the proposed method can produce more accurate brain extraction results on 246 testing subjects (75 neonates, 126 infants, and 45 children), i.e., at average Jaccard Index of 0.953, compared to those by BET (0.918), BSE (0.902), ROBEX (0.901), GCUT (0.856), and other fusion methods such as Majority Voting (0.919) and STAPLE (0.941). Along with the largely-improved computational efficiency, the proposed method demonstrates its ability of automated brain extraction for pediatric MR images in a large age range. PMID:22634859

Human brain atlas for automated region of interest selection in quantitative susceptibility mapping: application to determine iron content in deep gray matter structures.

PubMed

Lim, Issel Anne L; Faria, Andreia V; Li, Xu; Hsu, Johnny T C; Airan, Raag D; Mori, Susumu; van Zijl, Peter C M

2013-11-15

The purpose of this paper is to extend the single-subject Eve atlas from Johns Hopkins University, which currently contains diffusion tensor and T1-weighted anatomical maps, by including contrast based on quantitative susceptibility mapping. The new atlas combines a "deep gray matter parcellation map" (DGMPM) derived from a single-subject quantitative susceptibility map with the previously established "white matter parcellation map" (WMPM) from the same subject's T1-weighted and diffusion tensor imaging data into an MNI coordinate map named the "Everything Parcellation Map in Eve Space," also known as the "EvePM." It allows automated segmentation of gray matter and white matter structures. Quantitative susceptibility maps from five healthy male volunteers (30 to 33 years of age) were coregistered to the Eve Atlas with AIR and Large Deformation Diffeomorphic Metric Mapping (LDDMM), and the transformation matrices were applied to the EvePM to produce automated parcellation in subject space. Parcellation accuracy was measured with a kappa analysis for the left and right structures of six deep gray matter regions. For multi-orientation QSM images, the Kappa statistic was 0.85 between automated and manual segmentation, with the inter-rater reproducibility Kappa being 0.89 for the human raters, suggesting "almost perfect" agreement between all segmentation methods. Segmentation seemed slightly more difficult for human raters on single-orientation QSM images, with the Kappa statistic being 0.88 between automated and manual segmentation, and 0.85 and 0.86 between human raters. Overall, this atlas provides a time-efficient tool for automated coregistration and segmentation of quantitative susceptibility data to analyze many regions of interest. These data were used to establish a baseline for normal magnetic susceptibility measurements for over 60 brain structures of 30- to 33-year-old males. Correlating the average susceptibility with age-based iron concentrations in gray

Human brain atlas for automated region of interest selection in quantitative susceptibility mapping: application to determine iron content in deep gray matter structures

PubMed Central

Lim, Issel Anne L.; Faria, Andreia V.; Li, Xu; Hsu, Johnny T.C.; Airan, Raag D.; Mori, Susumu; van Zijl, Peter C. M.

2013-01-01

The purpose of this paper is to extend the single-subject Eve atlas from Johns Hopkins University, which currently contains diffusion tensor and T1-weighted anatomical maps, by including contrast based on quantitative susceptibility mapping. The new atlas combines a “deep gray matter parcellation map” (DGMPM) derived from a single-subject quantitative susceptibility map with the previously established “white matter parcellation map” (WMPM) from the same subject’s T1-weighted and diffusion tensor imaging data into an MNI coordinate map named the “Everything Parcellation Map in Eve Space,” also known as the “EvePM.” It allows automated segmentation of gray matter and white matter structures. Quantitative susceptibility maps from five healthy male volunteers (30 to 33 years of age) were coregistered to the Eve Atlas with AIR and Large Deformation Diffeomorphic Metric Mapping (LDDMM), and the transformation matrices were applied to the EvePM to produce automated parcellation in subject space. Parcellation accuracy was measured with a kappa analysis for the left and right structures of six deep gray matter regions. For multi-orientation QSM images, the Kappa statistic was 0.85 between automated and manual segmentation, with the inter-rater reproducibility Kappa being 0.89 for the human raters, suggesting “almost perfect” agreement between all segmentation methods. Segmentation seemed slightly more difficult for human raters on single-orientation QSM images, with the Kappa statistic being 0.88 between automated and manual segmentation, and 0.85 and 0.86 between human raters. Overall, this atlas provides a time-efficient tool for automated coregistration and segmentation of quantitative susceptibility data to analyze many regions of interest. These data were used to establish a baseline for normal magnetic susceptibility measurements for over 60 brain structures of 30- to 33-year-old males. Correlating the average susceptibility with age-based iron

Association between fully automated MRI-based volumetry of different brain regions and neuropsychological test performance in patients with amnestic mild cognitive impairment and Alzheimer's disease.

PubMed

Arlt, Sönke; Buchert, Ralph; Spies, Lothar; Eichenlaub, Martin; Lehmbeck, Jan T; Jahn, Holger

2013-06-01

Fully automated magnetic resonance imaging (MRI)-based volumetry may serve as biomarker for the diagnosis in patients with mild cognitive impairment (MCI) or dementia. We aimed at investigating the relation between fully automated MRI-based volumetric measures and neuropsychological test performance in amnestic MCI and patients with mild dementia due to Alzheimer's disease (AD) in a cross-sectional and longitudinal study. In order to assess a possible prognostic value of fully automated MRI-based volumetry for future cognitive performance, the rate of change of neuropsychological test performance over time was also tested for its correlation with fully automated MRI-based volumetry at baseline. In 50 subjects, 18 with amnestic MCI, 21 with mild AD, and 11 controls, neuropsychological testing and T1-weighted MRI were performed at baseline and at a mean follow-up interval of 2.1 ± 0.5 years (n = 19). Fully automated MRI volumetry of the grey matter volume (GMV) was performed using a combined stereotactic normalisation and segmentation approach as provided by SPM8 and a set of pre-defined binary lobe masks. Left and right hippocampus masks were derived from probabilistic cytoarchitectonic maps. Volumes of the inner and outer liquor space were also determined automatically from the MRI. Pearson's test was used for the correlation analyses. Left hippocampal GMV was significantly correlated with performance in memory tasks, and left temporal GMV was related to performance in language tasks. Bilateral frontal, parietal and occipital GMVs were correlated to performance in neuropsychological tests comprising multiple domains. Rate of GMV change in the left hippocampus was correlated with decline of performance in the Boston Naming Test (BNT), Mini-Mental Status Examination, and trail making test B (TMT-B). The decrease of BNT and TMT-A performance over time correlated with the loss of grey matter in multiple brain regions. We conclude that fully automated MRI

Combined Diffusion Tensor and Magnetic Resonance Spectroscopic Imaging Methodology for Automated Regional Brain Analysis: Application in a Normal Pediatric Population.

PubMed

Ghosh, Nirmalya; Holshouser, Barbara; Oyoyo, Udo; Barnes, Stanley; Tong, Karen; Ashwal, Stephen

2017-01-01

During human brain development, anatomic regions mature at different rates. Quantitative anatomy-specific analysis of longitudinal diffusion tensor imaging (DTI) and magnetic resonance spectroscopic imaging (MRSI) data may improve our ability to quantify and categorize these maturational changes. Computational tools designed to quickly fuse and analyze imaging information from multiple, technically different datasets would facilitate research on changes during normal brain maturation and for comparison to disease states. In the current study, we developed a complete battery of computational tools to execute such data analyses that include data preprocessing, tract-based statistical analysis from DTI data, automated brain anatomy parsing from T1-weighted MR images, assignment of metabolite information from MRSI data, and co-alignment of these multimodality data streams for reporting of region-specific indices. We present statistical analyses of regional DTI and MRSI data in a cohort of normal pediatric subjects (n = 72; age range: 5-18 years; mean 12.7 ± 3.3 years) to establish normative data and evaluate maturational trends. Several regions showed significant maturational changes for several DTI parameters and MRSI ratios, but the percent change over the age range tended to be small. In the subcortical region (combined basal ganglia [BG], thalami [TH], and corpus callosum [CC]), the largest combined percent change was a 10% increase in fractional anisotropy (FA) primarily due to increases in the BG (12.7%) and TH (9%). The largest significant percent increase in N-acetylaspartate (NAA)/creatine (Cr) ratio was seen in the brain stem (BS) (18.8%) followed by the subcortical regions in the BG (11.9%), CC (8.9%), and TH (6.0%). We found consistent, significant (p < 0.01), but weakly positive correlations (r = 0.228-0.329) between NAA/Cr ratios and mean FA in the BS, BG, and CC regions. Age- and region-specific normative MR diffusion and spectroscopic metabolite ranges

Automation or De-automation

NASA Astrophysics Data System (ADS)

Gorlach, Igor; Wessel, Oliver

2008-09-01

In the global automotive industry, for decades, vehicle manufacturers have continually increased the level of automation of production systems in order to be competitive. However, there is a new trend to decrease the level of automation, especially in final car assembly, for reasons of economy and flexibility. In this research, the final car assembly lines at three production sites of Volkswagen are analysed in order to determine the best level of automation for each, in terms of manufacturing costs, productivity, quality and flexibility. The case study is based on the methodology proposed by the Fraunhofer Institute. The results of the analysis indicate that fully automated assembly systems are not necessarily the best option in terms of cost, productivity and quality combined, which is attributed to high complexity of final car assembly systems; some de-automation is therefore recommended. On the other hand, the analysis shows that low automation can result in poor product quality due to reasons related to plant location, such as inadequate workers' skills, motivation, etc. Hence, the automation strategy should be formulated on the basis of analysis of all relevant aspects of the manufacturing process, such as costs, quality, productivity and flexibility in relation to the local context. A more balanced combination of automated and manual assembly operations provides better utilisation of equipment, reduces production costs and improves throughput.

GMP-compliant automated synthesis of [(18)F]AV-45 (Florbetapir F 18) for imaging beta-amyloid plaques in human brain.

PubMed

Yao, Cheng-Hsiang; Lin, Kun-Ju; Weng, Chi-Chang; Hsiao, Ing-Tsung; Ting, Yi-Shu; Yen, Tzu-Chen; Jan, Tong-Rong; Skovronsky, Daniel; Kung, Mei-Ping; Wey, Shiaw-Pyng

2010-12-01

We report herein the Good Manufacturing Practice (GMP)-compliant automated synthesis of (18)F-labeled styrylpyridine, AV-45 (Florbetapir), a novel tracer for positron emission tomography (PET) imaging of beta-amyloid (Abeta) plaques in the brain of Alzheimer's disease patients. [(18)F]AV-45 was prepared in 105 min using a tosylate precursor with Sumitomo modules for radiosynthesis under GMP-compliant conditions. The overall yield was 25.4+/-7.7% with a final radiochemical purity of 95.3+/-2.2% (n=19). The specific activity of [(18)F]AV-45 reached as high as 470+/-135 TBq/mmol (n=19). The present studies show that [(18)F]AV-45 can be manufactured under GMP-compliant conditions and could be widely available for routine clinical use. Copyright 2010 Elsevier Ltd. All rights reserved.

Injured Brain Regions Associated with Anxiety in Vietnam Veterans

PubMed Central

Knutson, Kristine M.; Rakowsky, Shana T.; Solomon, Jeffrey; Krueger, Frank; Raymont, Vanessa; Tierney, Michael C.; Wassermann, Eric M.; Grafman, Jordan

2013-01-01

Anxiety negatively affects quality of life and psychosocial functioning. Previous research has shown that anxiety symptoms in healthy individuals are associated with variations in the volume of brain regions, such as the amygdala, hippocampus, and the bed nucleus of the stria terminalis. Brain lesion data also suggests the hemisphere damaged may affect levels of anxiety. We studied a sample of 182 male Vietnam War veterans with penetrating brain injuries, using a semi-automated voxel-based lesion-symptom mapping (VLSM) approach. VLSM reveals significant associations between a symptom such as anxiety and the location of brain lesions, and does not require a broad, subjective assignment of patients into categories based on lesion location. We found that lesioned brain regions in cortical and limbic areas of the left hemisphere, including middle, inferior and superior temporal lobe, hippocampus, and fusiform regions, along with smaller areas in the inferior occipital lobe, parahippocampus, amygdala, and insula, were associated with increased anxiety symptoms as measured by the Neurobehavioral Rating Scale (NRS). These results were corroborated by similar findings using Neuropsychiatric Inventory (NPI) anxiety scores, which supports these regions’ role in regulating anxiety. In summary, using a semi-automated analysis tool, we detected an effect of focal brain damage on the presentation of anxiety. We also separated the effects of brain injury and war experience by including a control group of combat veterans without brain injury. We compared this control group against veterans with brain lesions in areas associated with anxiety, and against veterans with lesions only in other brain areas. PMID:23328629

Automated Morphological Analysis of Microglia After Stroke.

PubMed

Heindl, Steffanie; Gesierich, Benno; Benakis, Corinne; Llovera, Gemma; Duering, Marco; Liesz, Arthur

2018-01-01

Microglia are the resident immune cells of the brain and react quickly to changes in their environment with transcriptional regulation and morphological changes. Brain tissue injury such as ischemic stroke induces a local inflammatory response encompassing microglial activation. The change in activation status of a microglia is reflected in its gradual morphological transformation from a highly ramified into a less ramified or amoeboid cell shape. For this reason, the morphological changes of microglia are widely utilized to quantify microglial activation and studying their involvement in virtually all brain diseases. However, the currently available methods, which are mainly based on manual rating of immunofluorescent microscopic images, are often inaccurate, rater biased, and highly time consuming. To address these issues, we created a fully automated image analysis tool, which enables the analysis of microglia morphology from a confocal Z-stack and providing up to 59 morphological features. We developed the algorithm on an exploratory dataset of microglial cells from a stroke mouse model and validated the findings on an independent data set. In both datasets, we could demonstrate the ability of the algorithm to sensitively discriminate between the microglia morphology in the peri-infarct and the contralateral, unaffected cortex. Dimensionality reduction by principal component analysis allowed to generate a highly sensitive compound score for microglial shape analysis. Finally, we tested for concordance of results between the novel automated analysis tool and the conventional manual analysis and found a high degree of correlation. In conclusion, our novel method for the fully automatized analysis of microglia morphology shows excellent accuracy and time efficacy compared to traditional analysis methods. This tool, which we make openly available, could find application to study microglia morphology using fluorescence imaging in a wide range of brain disease models.

Elaboration of a semi-automated algorithm for brain arteriovenous malformation segmentation: initial results.

PubMed

Clarençon, Frédéric; Maizeroi-Eugène, Franck; Bresson, Damien; Maingreaud, Flavien; Sourour, Nader; Couquet, Claude; Ayoub, David; Chiras, Jacques; Yardin, Catherine; Mounayer, Charbel

2015-02-01

The purpose of our study was to distinguish the different components of a brain arteriovenous malformation (bAVM) on 3D rotational angiography (3D-RA) using a semi-automated segmentation algorithm. Data from 3D-RA of 15 patients (8 males, 7 females; 14 supratentorial bAVMs, 1 infratentorial) were used to test the algorithm. Segmentation was performed in two steps: (1) nidus segmentation from propagation (vertical then horizontal) of tagging on the reference slice (i.e., the slice on which the nidus had the biggest surface); (2) contiguity propagation (based on density and variance) from tagging of arteries and veins distant from the nidus. Segmentation quality was evaluated by comparison with six frame/s DSA by two independent reviewers. Analysis of supraselective microcatheterisation was performed to dispel discrepancy. Mean duration for bAVM segmentation was 64 ± 26 min. Quality of segmentation was evaluated as good or fair in 93% of cases. Segmentation had better results than six frame/s DSA for the depiction of a focal ectasia on the main draining vein and for the evaluation of the venous drainage pattern. This segmentation algorithm is a promising tool that may help improve the understanding of bAVM angio-architecture, especially the venous drainage. • The segmentation algorithm allows for the distinction of the AVM's components • This algorithm helps to see the venous drainage of bAVMs more precisely • This algorithm may help to reduce the treatment-related complication rate.

Pulse Coupled Neural Networks for the Segmentation of Magnetic Resonance Brain Images.

DTIC Science & Technology

1996-12-01

PULSE COUPLED NEURAL NETWORKS FOR THE SEGMENTATION OF MAGNETIC RESONANCE BRAIN IMAGES THESIS Shane Lee Abrahamson First Lieutenant, USAF AFIT/GCS/ENG...COUPLED NEURAL NETWORKS FOR THE SEGMENTATION OF MAGNETIC RESONANCE BRAIN IMAGES THESIS Shane Lee Abrahamson First Lieutenant, USAF AFIT/GCS/ENG/96D-01...research develops an automated method for segmenting Magnetic Resonance (MR) brain images based on Pulse Coupled Neural Networks (PCNN). MR brain image

Whole-brain activity mapping onto a zebrafish brain atlas.

PubMed

Randlett, Owen; Wee, Caroline L; Naumann, Eva A; Nnaemeka, Onyeka; Schoppik, David; Fitzgerald, James E; Portugues, Ruben; Lacoste, Alix M B; Riegler, Clemens; Engert, Florian; Schier, Alexander F

2015-11-01

In order to localize the neural circuits involved in generating behaviors, it is necessary to assign activity onto anatomical maps of the nervous system. Using brain registration across hundreds of larval zebrafish, we have built an expandable open-source atlas containing molecular labels and definitions of anatomical regions, the Z-Brain. Using this platform and immunohistochemical detection of phosphorylated extracellular signal–regulated kinase (ERK) as a readout of neural activity, we have developed a system to create and contextualize whole-brain maps of stimulus- and behavior-dependent neural activity. This mitogen-activated protein kinase (MAP)-mapping assay is technically simple, and data analysis is completely automated. Because MAP-mapping is performed on freely swimming fish, it is applicable to studies of nearly any stimulus or behavior. Here we demonstrate our high-throughput approach using pharmacological, visual and noxious stimuli, as well as hunting and feeding. The resultant maps outline hundreds of areas associated with behaviors.

Whole-brain activity mapping onto a zebrafish brain atlas

PubMed Central

Randlett, Owen; Wee, Caroline L.; Naumann, Eva A.; Nnaemeka, Onyeka; Schoppik, David; Fitzgerald, James E.; Portugues, Ruben; Lacoste, Alix M.B.; Riegler, Clemens; Engert, Florian; Schier, Alexander F.

2015-01-01

In order to localize the neural circuits involved in generating behaviors, it is necessary to assign activity onto anatomical maps of the nervous system. Using brain registration across hundreds of larval zebrafish, we have built an expandable open source atlas containing molecular labels and anatomical region definitions, the Z-Brain. Using this platform and immunohistochemical detection of phosphorylated-Extracellular signal-regulated kinase (ERK/MAPK) as a readout of neural activity, we have developed a system to create and contextualize whole brain maps of stimulus- and behavior-dependent neural activity. This MAP-Mapping (Mitogen Activated Protein kinase – Mapping) assay is technically simple, fast, inexpensive, and data analysis is completely automated. Since MAP-Mapping is performed on fish that are freely swimming, it is applicable to nearly any stimulus or behavior. We demonstrate the utility of our high-throughput approach using hunting/feeding, pharmacological, visual and noxious stimuli. The resultant maps outline hundreds of areas associated with behaviors. PMID:26778924

In vivo robotics: the automation of neuroscience and other intact-system biological fields.

PubMed

Kodandaramaiah, Suhasa B; Boyden, Edward S; Forest, Craig R

2013-12-01

Robotic and automation technologies have played a huge role in in vitro biological science, having proved critical for scientific endeavors such as genome sequencing and high-throughput screening. Robotic and automation strategies are beginning to play a greater role in in vivo and in situ sciences, especially when it comes to the difficult in vivo experiments required for understanding the neural mechanisms of behavior and disease. In this perspective, we discuss the prospects for robotics and automation to influence neuroscientific and intact-system biology fields. We discuss how robotic innovations might be created to open up new frontiers in basic and applied neuroscience and present a concrete example with our recent automation of in vivo whole-cell patch clamp electrophysiology of neurons in the living mouse brain. © 2013 New York Academy of Sciences.

In vivo robotics: the automation of neuroscience and other intact-system biological fields

PubMed Central

Kodandaramaiah, Suhasa B.; Boyden, Edward S.; Forest, Craig R.

2013-01-01

Robotic and automation technologies have played a huge role in in vitro biological science, having proved critical for scientific endeavors such as genome sequencing and high-throughput screening. Robotic and automation strategies are beginning to play a greater role in in vivo and in situ sciences, especially when it comes to the difficult in vivo experiments required for understanding the neural mechanisms of behavior and disease. In this perspective, we discuss the prospects for robotics and automation to impact neuroscientific and intact-system biology fields. We discuss how robotic innovations might be created to open up new frontiers in basic and applied neuroscience, and present a concrete example with our recent automation of in vivo whole cell patch clamp electrophysiology of neurons in the living mouse brain. PMID:23841584

Semi-automated segmentation of a glioblastoma multiforme on brain MR images for radiotherapy planning.

PubMed

Hori, Daisuke; Katsuragawa, Shigehiko; Murakami, Ryuuji; Hirai, Toshinori

2010-04-20

We propose a computerized method for semi-automated segmentation of the gross tumor volume (GTV) of a glioblastoma multiforme (GBM) on brain MR images for radiotherapy planning (RTP). Three-dimensional (3D) MR images of 28 cases with a GBM were used in this study. First, a sphere volume of interest (VOI) including the GBM was selected by clicking a part of the GBM region in the 3D image. Then, the sphere VOI was transformed to a two-dimensional (2D) image by use of a spiral-scanning technique. We employed active contour models (ACM) to delineate an optimal outline of the GBM in the transformed 2D image. After inverse transform of the optimal outline to the 3D space, a morphological filter was applied to smooth the shape of the 3D segmented region. For evaluation of our computerized method, we compared the computer output with manually segmented regions, which were obtained by a therapeutic radiologist using a manual tracking method. In evaluating our segmentation method, we employed the Jaccard similarity coefficient (JSC) and the true segmentation coefficient (TSC) in volumes between the computer output and the manually segmented region. The mean and standard deviation of JSC and TSC were 74.2+/-9.8% and 84.1+/-7.1%, respectively. Our segmentation method provided a relatively accurate outline for GBM and would be useful for radiotherapy planning.

Metabolic modeling of dynamic 13C NMR isotopomer data in the brain in vivo: Fast screening of metabolic models using automated generation of differential equations

PubMed Central

Tiret, Brice; Shestov, Alexander A.; Valette, Julien; Henry, Pierre-Gilles

2017-01-01

Most current brain metabolic models are not capable of taking into account the dynamic isotopomer information available from fine structure multiplets in 13C spectra, due to the difficulty of implementing such models. Here we present a new approach that allows automatic implementation of multi-compartment metabolic models capable of fitting any number of 13C isotopomer curves in the brain. The new automated approach also makes it possible to quickly modify and test new models to best describe the experimental data. We demonstrate the power of the new approach by testing the effect of adding separate pyruvate pools in astrocytes and neurons, and adding a vesicular neuronal glutamate pool. Including both changes reduced the global fit residual by half and pointed to dilution of label prior to entry into the astrocytic TCA cycle as the main source of glutamine dilution. The glutamate-glutamine cycle rate was particularly sensitive to changes in the model. PMID:26553273

Neurodegenerative changes in Alzheimer's disease: a comparative study of manual, semi-automated, and fully automated assessment using MRI

NASA Astrophysics Data System (ADS)

Fritzsche, Klaus H.; Giesel, Frederik L.; Heimann, Tobias; Thomann, Philipp A.; Hahn, Horst K.; Pantel, Johannes; Schröder, Johannes; Essig, Marco; Meinzer, Hans-Peter

2008-03-01

Objective quantification of disease specific neurodegenerative changes can facilitate diagnosis and therapeutic monitoring in several neuropsychiatric disorders. Reproducibility and easy-to-perform assessment are essential to ensure applicability in clinical environments. Aim of this comparative study is the evaluation of a fully automated approach that assesses atrophic changes in Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI). 21 healthy volunteers (mean age 66.2), 21 patients with MCI (66.6), and 10 patients with AD (65.1) were enrolled. Subjects underwent extensive neuropsychological testing and MRI was conducted on a 1.5 Tesla clinical scanner. Atrophic changes were measured automatically by a series of image processing steps including state of the art brain mapping techniques. Results were compared with two reference approaches: a manual segmentation of the hippocampal formation and a semi-automated estimation of temporal horn volume, which is based upon interactive selection of two to six landmarks in the ventricular system. All approaches separated controls and AD patients significantly (10 -5 < p < 10 -4) and showed a slight but not significant increase of neurodegeneration for subjects with MCI compared to volunteers. The automated approach correlated significantly with the manual (r = -0.65, p < 10 -6) and semi automated (r = -0.83, p < 10 -13) measurements. It proved high accuracy and at the same time maximized observer independency, time reduction and thus usefulness for clinical routine.

BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities.

PubMed

Griffanti, Ludovica; Zamboni, Giovanna; Khan, Aamira; Li, Linxin; Bonifacio, Guendalina; Sundaresan, Vaanathi; Schulz, Ursula G; Kuker, Wilhelm; Battaglini, Marco; Rothwell, Peter M; Jenkinson, Mark

2016-11-01

Reliable quantification of white matter hyperintensities of presumed vascular origin (WMHs) is increasingly needed, given the presence of these MRI findings in patients with several neurological and vascular disorders, as well as in elderly healthy subjects. We present BIANCA (Brain Intensity AbNormality Classification Algorithm), a fully automated, supervised method for WMH detection, based on the k-nearest neighbour (k-NN) algorithm. Relative to previous k-NN based segmentation methods, BIANCA offers different options for weighting the spatial information, local spatial intensity averaging, and different options for the choice of the number and location of the training points. BIANCA is multimodal and highly flexible so that the user can adapt the tool to their protocol and specific needs. We optimised and validated BIANCA on two datasets with different MRI protocols and patient populations (a "predominantly neurodegenerative" and a "predominantly vascular" cohort). BIANCA was first optimised on a subset of images for each dataset in terms of overlap and volumetric agreement with a manually segmented WMH mask. The correlation between the volumes extracted with BIANCA (using the optimised set of options), the volumes extracted from the manual masks and visual ratings showed that BIANCA is a valid alternative to manual segmentation. The optimised set of options was then applied to the whole cohorts and the resulting WMH volume estimates showed good correlations with visual ratings and with age. Finally, we performed a reproducibility test, to evaluate the robustness of BIANCA, and compared BIANCA performance against existing methods. Our findings suggest that BIANCA, which will be freely available as part of the FSL package, is a reliable method for automated WMH segmentation in large cross-sectional cohort studies. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Automated and visual scoring methods of cerebral white matter hyperintensities: relation with age and cognitive function.

PubMed

Tiehuis, A M; Vincken, K L; Mali, W P T M; Kappelle, L J; Anbeek, P; Algra, A; Biessels, G J

2008-01-01

A reliable scoring method for ischemic cerebral white matter hyperintensities (WMH) will help to clarify the causes and consequences of these brain lesions. We compared an automated and two visual WMH scoring methods in their relations with age and cognitive function. MRI of the brain was performed on 154 participants of the Utrecht Diabetic Encephalopathy Study. WMH volumes were obtained with an automated segmentation method. Visual rating of deep and periventricular WMH (DWMH and PWMH) was performed with the Scheltens scale and the Rotterdam Scan Study (RSS) scale, respectively. Cognition was assessed with a battery of 11 tests. Within the whole study group, the association with age was most evident for the automated measured WMH volume (beta = 0.43, 95% CI = 0.29-0.57). With regard to cognition, automated measured WMH volume and Scheltens DWMH were significantly associated with information processing speed (beta = -0.22, 95% CI = -0.40 to -0.06; beta = -0.26, 95% CI = -0.42 to -0.10), whereas RSS PWMH were associated with attention and executive function (beta = -0.19, 95% CI = -0.36 to -0.02). Measurements of WMH with an automated quantitative segmentation method are comparable with visual rating scales and highly suitable for use in future studies to assess the relationship between WMH and subtle impairments in cognitive function. (c) 2007 S. Karger AG, Basel.

The Potential of Using Brain Images for Authentication

PubMed Central

Zhou, Zongtan; Shen, Hui; Hu, Dewen

2014-01-01

Biometric recognition (also known as biometrics) refers to the automated recognition of individuals based on their biological or behavioral traits. Examples of biometric traits include fingerprint, palmprint, iris, and face. The brain is the most important and complex organ in the human body. Can it be used as a biometric trait? In this study, we analyze the uniqueness of the brain and try to use the brain for identity authentication. The proposed brain-based verification system operates in two stages: gray matter extraction and gray matter matching. A modified brain segmentation algorithm is implemented for extracting gray matter from an input brain image. Then, an alignment-based matching algorithm is developed for brain matching. Experimental results on two data sets show that the proposed brain recognition system meets the high accuracy requirement of identity authentication. Though currently the acquisition of the brain is still time consuming and expensive, brain images are highly unique and have the potential possibility for authentication in view of pattern recognition. PMID:25126604

The potential of using brain images for authentication.

PubMed

Chen, Fanglin; Zhou, Zongtan; Shen, Hui; Hu, Dewen

2014-01-01

Biometric recognition (also known as biometrics) refers to the automated recognition of individuals based on their biological or behavioral traits. Examples of biometric traits include fingerprint, palmprint, iris, and face. The brain is the most important and complex organ in the human body. Can it be used as a biometric trait? In this study, we analyze the uniqueness of the brain and try to use the brain for identity authentication. The proposed brain-based verification system operates in two stages: gray matter extraction and gray matter matching. A modified brain segmentation algorithm is implemented for extracting gray matter from an input brain image. Then, an alignment-based matching algorithm is developed for brain matching. Experimental results on two data sets show that the proposed brain recognition system meets the high accuracy requirement of identity authentication. Though currently the acquisition of the brain is still time consuming and expensive, brain images are highly unique and have the potential possibility for authentication in view of pattern recognition.

Mapping Cortical Laminar Structure in the 3D BigBrain.

PubMed

Wagstyl, Konrad; Lepage, Claude; Bludau, Sebastian; Zilles, Karl; Fletcher, Paul C; Amunts, Katrin; Evans, Alan C

2018-07-01

Histological sections offer high spatial resolution to examine laminar architecture of the human cerebral cortex; however, they are restricted by being 2D, hence only regions with sufficiently optimal cutting planes can be analyzed. Conversely, noninvasive neuroimaging approaches are whole brain but have relatively low resolution. Consequently, correct 3D cross-cortical patterns of laminar architecture have never been mapped in histological sections. We developed an automated technique to identify and analyze laminar structure within the high-resolution 3D histological BigBrain. We extracted white matter and pial surfaces, from which we derived histologically verified surfaces at the layer I/II boundary and within layer IV. Layer IV depth was strongly predicted by cortical curvature but varied between areas. This fully automated 3D laminar analysis is an important requirement for bridging high-resolution 2D cytoarchitecture and in vivo 3D neuroimaging. It lays the foundation for in-depth, whole-brain analyses of cortical layering.

Automated detection of periventricular veins on 7 T brain MRI

NASA Astrophysics Data System (ADS)

Kuijf, Hugo J.; Bouvy, Willem H.; Zwanenburg, Jaco J. M.; Viergever, Max A.; Biessels, Geert Jan; Vincken, Koen L.

2015-03-01

Cerebral small vessel disease is common in elderly persons and a leading cause of cognitive decline, dementia, and acute stroke. With the introduction of ultra-high field strength 7.0T MRI, it is possible to visualize small vessels in the brain. In this work, a proof-of-principle study is conducted to assess the feasibility of automatically detecting periventricular veins. Periventricular veins are organized in a fan-pattern and drain venous blood from the brain towards the caudate vein of Schlesinger, which is situated along the lateral ventricles. Just outside this vein, a region-of- interest (ROI) through which all periventricular veins must cross is defined. Within this ROI, a combination of the vesselness filter, tubular tracking, and hysteresis thresholding is applied to locate periventricular veins. All detected locations were evaluated by an expert human observer. The results showed a positive predictive value of 88% and a sensitivity of 95% for detecting periventricular veins. The proposed method shows good results in detecting periventricular veins in the brain on 7.0T MR images. Compared to previous works, that only use a 1D or 2D ROI and limited image processing, our work presents a more comprehensive definition of the ROI, advanced image processing techniques to detect periventricular veins, and a quantitative analysis of the performance. The results of this proof-of-principle study are promising and will be used to assess periventricular veins on 7.0T brain MRI.

Altering user' acceptance of automation through prior automation exposure.

PubMed

Bekier, Marek; Molesworth, Brett R C

2017-06-01

Air navigation service providers worldwide see increased use of automation as one solution to overcome the capacity constraints imbedded in the present air traffic management (ATM) system. However, increased use of automation within any system is dependent on user acceptance. The present research sought to determine if the point at which an individual is no longer willing to accept or cooperate with automation can be manipulated. Forty participants underwent training on a computer-based air traffic control programme, followed by two ATM exercises (order counterbalanced), one with and one without the aid of automation. Results revealed after exposure to a task with automation assistance, user acceptance of high(er) levels of automation ('tipping point') decreased; suggesting it is indeed possible to alter automation acceptance. Practitioner Summary: This paper investigates whether the point at which a user of automation rejects automation (i.e. 'tipping point') is constant or can be manipulated. The results revealed after exposure to a task with automation assistance, user acceptance of high(er) levels of automation decreased; suggesting it is possible to alter automation acceptance.

Automated system for analyzing the activity of individual neurons

NASA Technical Reports Server (NTRS)

Bankman, Isaac N.; Johnson, Kenneth O.; Menkes, Alex M.; Diamond, Steve D.; Oshaughnessy, David M.

1993-01-01

This paper presents a signal processing system that: (1) provides an efficient and reliable instrument for investigating the activity of neuronal assemblies in the brain; and (2) demonstrates the feasibility of generating the command signals of prostheses using the activity of relevant neurons in disabled subjects. The system operates online, in a fully automated manner and can recognize the transient waveforms of several neurons in extracellular neurophysiological recordings. Optimal algorithms for detection, classification, and resolution of overlapping waveforms are developed and evaluated. Full automation is made possible by an algorithm that can set appropriate decision thresholds and an algorithm that can generate templates on-line. The system is implemented with a fast IBM PC compatible processor board that allows on-line operation.

Supervised learning technique for the automated identification of white matter hyperintensities in traumatic brain injury.

PubMed

Stone, James R; Wilde, Elisabeth A; Taylor, Brian A; Tate, David F; Levin, Harvey; Bigler, Erin D; Scheibel, Randall S; Newsome, Mary R; Mayer, Andrew R; Abildskov, Tracy; Black, Garrett M; Lennon, Michael J; York, Gerald E; Agarwal, Rajan; DeVillasante, Jorge; Ritter, John L; Walker, Peter B; Ahlers, Stephen T; Tustison, Nicholas J

2016-01-01

White matter hyperintensities (WMHs) are foci of abnormal signal intensity in white matter regions seen with magnetic resonance imaging (MRI). WMHs are associated with normal ageing and have shown prognostic value in neurological conditions such as traumatic brain injury (TBI). The impracticality of manually quantifying these lesions limits their clinical utility and motivates the utilization of machine learning techniques for automated segmentation workflows. This study develops a concatenated random forest framework with image features for segmenting WMHs in a TBI cohort. The framework is built upon the Advanced Normalization Tools (ANTs) and ANTsR toolkits. MR (3D FLAIR, T2- and T1-weighted) images from 24 service members and veterans scanned in the Chronic Effects of Neurotrauma Consortium's (CENC) observational study were acquired. Manual annotations were employed for both training and evaluation using a leave-one-out strategy. Performance measures include sensitivity, positive predictive value, [Formula: see text] score and relative volume difference. Final average results were: sensitivity = 0.68 ± 0.38, positive predictive value = 0.51 ± 0.40, [Formula: see text] = 0.52 ± 0.36, relative volume difference = 43 ± 26%. In addition, three lesion size ranges are selected to illustrate the variation in performance with lesion size. Paired with correlative outcome data, supervised learning methods may allow for identification of imaging features predictive of diagnosis and prognosis in individual TBI patients.

Complacency and Automation Bias in the Use of Imperfect Automation.

PubMed

Wickens, Christopher D; Clegg, Benjamin A; Vieane, Alex Z; Sebok, Angelia L

2015-08-01

We examine the effects of two different kinds of decision-aiding automation errors on human-automation interaction (HAI), occurring at the first failure following repeated exposure to correctly functioning automation. The two errors are incorrect advice, triggering the automation bias, and missing advice, reflecting complacency. Contrasts between analogous automation errors in alerting systems, rather than decision aiding, have revealed that alerting false alarms are more problematic to HAI than alerting misses are. Prior research in decision aiding, although contrasting the two aiding errors (incorrect vs. missing), has confounded error expectancy. Participants performed an environmental process control simulation with and without decision aiding. For those with the aid, automation dependence was created through several trials of perfect aiding performance, and an unexpected automation error was then imposed in which automation was either gone (one group) or wrong (a second group). A control group received no automation support. The correct aid supported faster and more accurate diagnosis and lower workload. The aid failure degraded all three variables, but "automation wrong" had a much greater effect on accuracy, reflecting the automation bias, than did "automation gone," reflecting the impact of complacency. Some complacency was manifested for automation gone, by a longer latency and more modest reduction in accuracy. Automation wrong, creating the automation bias, appears to be a more problematic form of automation error than automation gone, reflecting complacency. Decision-aiding automation should indicate its lower degree of confidence in uncertain environments to avoid the automation bias. © 2015, Human Factors and Ergonomics Society.

Automated processing for proton spectroscopic imaging using water reference deconvolution.

PubMed

Maudsley, A A; Wu, Z; Meyerhoff, D J; Weiner, M W

1994-06-01

Automated formation of MR spectroscopic images (MRSI) is necessary before routine application of these methods is possible for in vivo studies; however, this task is complicated by the presence of spatially dependent instrumental distortions and the complex nature of the MR spectrum. A data processing method is presented for completely automated formation of in vivo proton spectroscopic images, and applied for analysis of human brain metabolites. This procedure uses the water reference deconvolution method (G. A. Morris, J. Magn. Reson. 80, 547(1988)) to correct for line shape distortions caused by instrumental and sample characteristics, followed by parametric spectral analysis. Results for automated image formation were found to compare favorably with operator dependent spectral integration methods. While the water reference deconvolution processing was found to provide good correction of spatially dependent resonance frequency shifts, it was found to be susceptible to errors for correction of line shape distortions. These occur due to differences between the water reference and the metabolite distributions.

aMAP is a validated pipeline for registration and segmentation of high-resolution mouse brain data

PubMed Central

Niedworok, Christian J.; Brown, Alexander P. Y.; Jorge Cardoso, M.; Osten, Pavel; Ourselin, Sebastien; Modat, Marc; Margrie, Troy W.

2016-01-01

The validation of automated image registration and segmentation is crucial for accurate and reliable mapping of brain connectivity and function in three-dimensional (3D) data sets. While validation standards are necessarily high and routinely met in the clinical arena, they have to date been lacking for high-resolution microscopy data sets obtained from the rodent brain. Here we present a tool for optimized automated mouse atlas propagation (aMAP) based on clinical registration software (NiftyReg) for anatomical segmentation of high-resolution 3D fluorescence images of the adult mouse brain. We empirically evaluate aMAP as a method for registration and subsequent segmentation by validating it against the performance of expert human raters. This study therefore establishes a benchmark standard for mapping the molecular function and cellular connectivity of the rodent brain. PMID:27384127

A population MRI brain template and analysis tools for the macaque.

PubMed

Seidlitz, Jakob; Sponheim, Caleb; Glen, Daniel; Ye, Frank Q; Saleem, Kadharbatcha S; Leopold, David A; Ungerleider, Leslie; Messinger, Adam

2018-04-15

The use of standard anatomical templates is common in human neuroimaging, as it facilitates data analysis and comparison across subjects and studies. For non-human primates, previous in vivo templates have lacked sufficient contrast to reliably validate known anatomical brain regions and have not provided tools for automated single-subject processing. Here we present the "National Institute of Mental Health Macaque Template", or NMT for short. The NMT is a high-resolution in vivo MRI template of the average macaque brain generated from 31 subjects, as well as a neuroimaging tool for improved data analysis and visualization. From the NMT volume, we generated maps of tissue segmentation and cortical thickness. Surface reconstructions and transformations to previously published digital brain atlases are also provided. We further provide an analysis pipeline using the NMT that automates and standardizes the time-consuming processes of brain extraction, tissue segmentation, and morphometric feature estimation for anatomical scans of individual subjects. The NMT and associated tools thus provide a common platform for precise single-subject data analysis and for characterizations of neuroimaging results across subjects and studies. Copyright © 2017 ElsevierCompany. All rights reserved.

Improving the driver-automation interaction: an approach using automation uncertainty.

PubMed

Beller, Johannes; Heesen, Matthias; Vollrath, Mark

2013-12-01

The aim of this study was to evaluate whether communicating automation uncertainty improves the driver-automation interaction. A false system understanding of infallibility may provoke automation misuse and can lead to severe consequences in case of automation failure. The presentation of automation uncertainty may prevent this false system understanding and, as was shown by previous studies, may have numerous benefits. Few studies, however, have clearly shown the potential of communicating uncertainty information in driving. The current study fills this gap. We conducted a driving simulator experiment, varying the presented uncertainty information between participants (no uncertainty information vs. uncertainty information) and the automation reliability (high vs.low) within participants. Participants interacted with a highly automated driving system while engaging in secondary tasks and were required to cooperate with the automation to drive safely. Quantile regressions and multilevel modeling showed that the presentation of uncertainty information increases the time to collision in the case of automation failure. Furthermore, the data indicated improved situation awareness and better knowledge of fallibility for the experimental group. Consequently, the automation with the uncertainty symbol received higher trust ratings and increased acceptance. The presentation of automation uncertaintythrough a symbol improves overall driver-automation cooperation. Most automated systems in driving could benefit from displaying reliability information. This display might improve the acceptance of fallible systems and further enhances driver-automation cooperation.

Clinical brain MR imaging prescriptions in Talairach space: technologist- and computer-driven methods.

PubMed

Weiss, Kenneth L; Pan, Hai; Storrs, Judd; Strub, William; Weiss, Jane L; Jia, Li; Eldevik, O Petter

2003-05-01

Variability in patient head positioning may yield substantial interstudy image variance in the clinical setting. We describe and test three-step technologist and computer-automated algorithms designed to image the brain in a standard reference system and reduce variance. Triple oblique axial images obtained parallel to the Talairach anterior commissure (AC)-posterior commissure (PC) plane were reviewed in a prospective analysis of 126 consecutive patients. Requisite roll, yaw, and pitch correction, as three authors determined independently and subsequently by consensus, were compared with the technologists' actual graphical prescriptions and those generated by a novel computer automated three-step (CATS) program. Automated pitch determinations generated with Statistical Parametric Mapping '99 (SPM'99) were also compared. Requisite pitch correction (15.2 degrees +/- 10.2 degrees ) far exceeded that for roll (-0.6 degrees +/- 3.7 degrees ) and yaw (-0.9 degrees +/- 4.7 degrees ) in terms of magnitude and variance (P <.001). Technologist and computer-generated prescriptions substantially reduced interpatient image variance with regard to roll (3.4 degrees and 3.9 degrees vs 13.5 degrees ), yaw (0.6 degrees and 2.5 degrees vs 22.3 degrees ), and pitch (28.6 degrees, 18.5 degrees with CATS, and 59.3 degrees with SPM'99 vs 104 degrees ). CATS performed worse than the technologists in yaw prescription, and it was equivalent in roll and pitch prescriptions. Talairach prescriptions better approximated standard CT canthomeatal angulations (9 degrees vs 24 degrees ) and provided more efficient brain coverage than that of routine axial imaging. Brain MR prescriptions corrected for direct roll, yaw, and Talairach AC-PC pitch can be readily achieved by trained technologists or automated computer algorithms. This ability will substantially reduce interpatient variance, allow better approximation of standard CT angulation, and yield more efficient brain coverage than that of

A Fully Automated Approach to Spike Sorting.

PubMed

Chung, Jason E; Magland, Jeremy F; Barnett, Alex H; Tolosa, Vanessa M; Tooker, Angela C; Lee, Kye Y; Shah, Kedar G; Felix, Sarah H; Frank, Loren M; Greengard, Leslie F

2017-09-13

Understanding the detailed dynamics of neuronal networks will require the simultaneous measurement of spike trains from hundreds of neurons (or more). Currently, approaches to extracting spike times and labels from raw data are time consuming, lack standardization, and involve manual intervention, making it difficult to maintain data provenance and assess the quality of scientific results. Here, we describe an automated clustering approach and associated software package that addresses these problems and provides novel cluster quality metrics. We show that our approach has accuracy comparable to or exceeding that achieved using manual or semi-manual techniques with desktop central processing unit (CPU) runtimes faster than acquisition time for up to hundreds of electrodes. Moreover, a single choice of parameters in the algorithm is effective for a variety of electrode geometries and across multiple brain regions. This algorithm has the potential to enable reproducible and automated spike sorting of larger scale recordings than is currently possible. Copyright © 2017 Elsevier Inc. All rights reserved.

Cockpit automation

NASA Technical Reports Server (NTRS)

Wiener, Earl L.

1988-01-01

The aims and methods of aircraft cockpit automation are reviewed from a human-factors perspective. Consideration is given to the mixed pilot reception of increased automation, government concern with the safety and reliability of highly automated aircraft, the formal definition of automation, and the ground-proximity warning system and accidents involving controlled flight into terrain. The factors motivating automation include technology availability; safety; economy, reliability, and maintenance; workload reduction and two-pilot certification; more accurate maneuvering and navigation; display flexibility; economy of cockpit space; and military requirements.

Evaluation of software tools for automated identification of neuroanatomical structures in quantitative β-amyloid PET imaging to diagnose Alzheimer's disease.

PubMed

Tuszynski, Tobias; Rullmann, Michael; Luthardt, Julia; Butzke, Daniel; Tiepolt, Solveig; Gertz, Hermann-Josef; Hesse, Swen; Seese, Anita; Lobsien, Donald; Sabri, Osama; Barthel, Henryk

2016-06-01

For regional quantification of nuclear brain imaging data, defining volumes of interest (VOIs) by hand is still the gold standard. As this procedure is time-consuming and operator-dependent, a variety of software tools for automated identification of neuroanatomical structures were developed. As the quality and performance of those tools are poorly investigated so far in analyzing amyloid PET data, we compared in this project four algorithms for automated VOI definition (HERMES Brass, two PMOD approaches, and FreeSurfer) against the conventional method. We systematically analyzed florbetaben brain PET and MRI data of ten patients with probable Alzheimer's dementia (AD) and ten age-matched healthy controls (HCs) collected in a previous clinical study. VOIs were manually defined on the data as well as through the four automated workflows. Standardized uptake value ratios (SUVRs) with the cerebellar cortex as a reference region were obtained for each VOI. SUVR comparisons between ADs and HCs were carried out using Mann-Whitney-U tests, and effect sizes (Cohen's d) were calculated. SUVRs of automatically generated VOIs were correlated with SUVRs of conventionally derived VOIs (Pearson's tests). The composite neocortex SUVRs obtained by manually defined VOIs were significantly higher for ADs vs. HCs (p=0.010, d=1.53). This was also the case for the four tested automated approaches which achieved effect sizes of d=1.38 to d=1.62. SUVRs of automatically generated VOIs correlated significantly with those of the hand-drawn VOIs in a number of brain regions, with regional differences in the degree of these correlations. Best overall correlation was observed in the lateral temporal VOI for all tested software tools (r=0.82 to r=0.95, p<0.001). Automated VOI definition by the software tools tested has a great potential to substitute for the current standard procedure to manually define VOIs in β-amyloid PET data analysis.

Automated detection of extradural and subdural hematoma for contrast-enhanced CT images in emergency medical care

NASA Astrophysics Data System (ADS)

Hara, Takeshi; Matoba, Naoto; Zhou, Xiangrong; Yokoi, Shinya; Aizawa, Hiroaki; Fujita, Hiroshi; Sakashita, Keiji; Matsuoka, Tetsuya

2007-03-01

We have been developing the CAD scheme for head and abdominal injuries for emergency medical care. In this work, we have developed an automated method to detect typical head injuries, rupture or strokes of brain. Extradural and subdural hematoma region were detected by comparing technique after the brain areas were registered using warping. We employ 5 normal and 15 stroke cases to estimate the performance after creating the brain model with 50 normal cases. Some of the hematoma regions were detected correctly in all of the stroke cases with no false positive findings on normal cases.

Complex Genetics of Behavior: BXDs in the Automated Home-Cage.

PubMed

Loos, Maarten; Verhage, Matthijs; Spijker, Sabine; Smit, August B

2017-01-01

This chapter describes a use case for the genetic dissection and automated analysis of complex behavioral traits using the genetically diverse panel of BXD mouse recombinant inbred strains. Strains of the BXD resource differ widely in terms of gene and protein expression in the brain, as well as in their behavioral repertoire. A large mouse resource opens the possibility for gene finding studies underlying distinct behavioral phenotypes, however, such a resource poses a challenge in behavioral phenotyping. To address the specifics of large-scale screening we describe how to investigate: (1) how to assess mouse behavior systematically in addressing a large genetic cohort, (2) how to dissect automation-derived longitudinal mouse behavior into quantitative parameters, and (3) how to map these quantitative traits to the genome, deriving loci underlying aspects of behavior.

Neuromodulation as a Robot Controller: A Brain Inspired Strategy for Controlling Autonomous Robots

DTIC Science & Technology

2009-09-01

To Appear in IEEE Robotics and Automation Magazine PREPRINT 1 Neuromodulation as a Robot Controller: A Brain Inspired Strategy for Controlling...Introduction We present a strategy for controlling autonomous robots that is based on principles of neuromodulation in the mammalian brain...object, ignore irrelevant distractions, and respond quickly and appropriately to the event [1]. There are separate neuromodulators that alter responses to

Subtle In-Scanner Motion Biases Automated Measurement of Brain Anatomy From In Vivo MRI

PubMed Central

Alexander-Bloch, Aaron; Clasen, Liv; Stockman, Michael; Ronan, Lisa; Lalonde, Francois; Giedd, Jay; Raznahan, Armin

2016-01-01

While the potential for small amounts of motion in functional magnetic resonance imaging (fMRI) scans to bias the results of functional neuroimaging studies is well appreciated, the impact of in-scanner motion on morphological analysis of structural MRI is relatively under-studied. Even among “good quality” structural scans, there may be systematic effects of motion on measures of brain morphometry. In the present study, the subjects’ tendency to move during fMRI scans, acquired in the same scanning sessions as their structural scans, yielded a reliable, continuous estimate of in-scanner motion. Using this approach within a sample of 127 children, adolescents, and young adults, significant relationships were found between this measure and estimates of cortical gray matter volume and mean curvature, as well as trend-level relationships with cortical thickness. Specifically, cortical volume and thickness decreased with greater motion, and mean curvature increased. These effects of subtle motion were anatomically heterogeneous, were present across different automated imaging pipelines, showed convergent validity with effects of frank motion assessed in a separate sample of 274 scans, and could be demonstrated in both pediatric and adult populations. Thus, using different motion assays in two large non-overlapping sets of structural MRI scans, convergent evidence showed that in-scanner motion—even at levels which do not manifest in visible motion artifact—can lead to systematic and regionally specific biases in anatomical estimation. These findings have special relevance to structural neuroimaging in developmental and clinical datasets, and inform ongoing efforts to optimize neuroanatomical analysis of existing and future structural MRI datasets in non-sedated humans. PMID:27004471

A data mining system for providing analytical information on brain tumors to public health decision makers.

PubMed

Santos, R S; Malheiros, S M F; Cavalheiro, S; de Oliveira, J M Parente

2013-03-01

Cancer is the leading cause of death in economically developed countries and the second leading cause of death in developing countries. Malignant brain neoplasms are among the most devastating and incurable forms of cancer, and their treatment may be excessively complex and costly. Public health decision makers require significant amounts of analytical information to manage public treatment programs for these patients. Data mining, a technology that is used to produce analytically useful information, has been employed successfully with medical data. However, the large-scale adoption of this technique has been limited thus far because it is difficult to use, especially for non-expert users. One way to facilitate data mining by non-expert users is to automate the process. Our aim is to present an automated data mining system that allows public health decision makers to access analytical information regarding brain tumors. The emphasis in this study is the use of ontology in an automated data mining process. The non-experts who tried the system obtained useful information about the treatment of brain tumors. These results suggest that future work should be conducted in this area. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Semi-automated quantification and neuroanatomical mapping of heterogeneous cell populations.

PubMed

Mendez, Oscar A; Potter, Colin J; Valdez, Michael; Bello, Thomas; Trouard, Theodore P; Koshy, Anita A

2018-07-15

Our group studies the interactions between cells of the brain and the neurotropic parasite Toxoplasma gondii. Using an in vivo system that allows us to permanently mark and identify brain cells injected with Toxoplasma protein, we have identified that Toxoplasma-injected neurons (TINs) are heterogeneously distributed throughout the brain. Unfortunately, standard methods to quantify and map heterogeneous cell populations onto a reference brain atlas are time consuming and prone to user bias. We developed a novel MATLAB-based semi-automated quantification and mapping program to allow the rapid and consistent mapping of heterogeneously distributed cells on to the Allen Institute Mouse Brain Atlas. The system uses two-threshold background subtraction to identify and quantify cells of interest. We demonstrate that we reliably quantify and neuroanatomically localize TINs with low intra- or inter-observer variability. In a follow up experiment, we show that specific regions of the mouse brain are enriched with TINs. The procedure we use takes advantage of simple immunohistochemistry labeling techniques, use of a standard microscope with a motorized stage, and low cost computing that can be readily obtained at a research institute. To our knowledge there is no other program that uses such readily available techniques and equipment for mapping heterogeneous populations of cells across the whole mouse brain. The quantification method described here allows reliable visualization, quantification, and mapping of heterogeneous cell populations in immunolabeled sections across whole mouse brains. Copyright © 2018 Elsevier B.V. All rights reserved.

Autonomy and Automation

NASA Technical Reports Server (NTRS)

Shively, Jay

2017-01-01

A significant level of debate and confusion has surrounded the meaning of the terms autonomy and automation. Automation is a multi-dimensional concept, and we propose that Remotely Piloted Aircraft Systems (RPAS) automation should be described with reference to the specific system and task that has been automated, the context in which the automation functions, and other relevant dimensions. In this paper, we present definitions of automation, pilot in the loop, pilot on the loop and pilot out of the loop. We further propose that in future, the International Civil Aviation Organization (ICAO) RPAS Panel avoids the use of the terms autonomy and autonomous when referring to automated systems on board RPA. Work Group 7 proposes to develop, in consultation with other workgroups, a taxonomy of Levels of Automation for RPAS.

Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project.

PubMed

Bastiani, Matteo; Andersson, Jesper L R; Cordero-Grande, Lucilio; Murgasova, Maria; Hutter, Jana; Price, Anthony N; Makropoulos, Antonios; Fitzgibbon, Sean P; Hughes, Emer; Rueckert, Daniel; Victor, Suresh; Rutherford, Mary; Edwards, A David; Smith, Stephen M; Tournier, Jacques-Donald; Hajnal, Joseph V; Jbabdi, Saad; Sotiropoulos, Stamatios N

2018-05-28

The developing Human Connectome Project is set to create and make available to the scientific community a 4-dimensional map of functional and structural cerebral connectivity from 20 to 44 weeks post-menstrual age, to allow exploration of the genetic and environmental influences on brain development, and the relation between connectivity and neurocognitive function. A large set of multi-modal MRI data from fetuses and newborn infants is currently being acquired, along with genetic, clinical and developmental information. In this overview, we describe the neonatal diffusion MRI (dMRI) image processing pipeline and the structural connectivity aspect of the project. Neonatal dMRI data poses specific challenges, and standard analysis techniques used for adult data are not directly applicable. We have developed a processing pipeline that deals directly with neonatal-specific issues, such as severe motion and motion-related artefacts, small brain sizes, high brain water content and reduced anisotropy. This pipeline allows automated analysis of in-vivo dMRI data, probes tissue microstructure, reconstructs a number of major white matter tracts, and includes an automated quality control framework that identifies processing issues or inconsistencies. We here describe the pipeline and present an exemplar analysis of data from 140 infants imaged at 38-44 weeks post-menstrual age. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Automatically tracking neurons in a moving and deforming brain

PubMed Central

Nguyen, Jeffrey P.; Linder, Ashley N.; Plummer, George S.; Shaevitz, Joshua W.

2017-01-01

Advances in optical neuroimaging techniques now allow neural activity to be recorded with cellular resolution in awake and behaving animals. Brain motion in these recordings pose a unique challenge. The location of individual neurons must be tracked in 3D over time to accurately extract single neuron activity traces. Recordings from small invertebrates like C. elegans are especially challenging because they undergo very large brain motion and deformation during animal movement. Here we present an automated computer vision pipeline to reliably track populations of neurons with single neuron resolution in the brain of a freely moving C. elegans undergoing large motion and deformation. 3D volumetric fluorescent images of the animal’s brain are straightened, aligned and registered, and the locations of neurons in the images are found via segmentation. Each neuron is then assigned an identity using a new time-independent machine-learning approach we call Neuron Registration Vector Encoding. In this approach, non-rigid point-set registration is used to match each segmented neuron in each volume with a set of reference volumes taken from throughout the recording. The way each neuron matches with the references defines a feature vector which is clustered to assign an identity to each neuron in each volume. Finally, thin-plate spline interpolation is used to correct errors in segmentation and check consistency of assigned identities. The Neuron Registration Vector Encoding approach proposed here is uniquely well suited for tracking neurons in brains undergoing large deformations. When applied to whole-brain calcium imaging recordings in freely moving C. elegans, this analysis pipeline located 156 neurons for the duration of an 8 minute recording and consistently found more neurons more quickly than manual or semi-automated approaches. PMID:28545068

Automatically tracking neurons in a moving and deforming brain.

PubMed

Nguyen, Jeffrey P; Linder, Ashley N; Plummer, George S; Shaevitz, Joshua W; Leifer, Andrew M

2017-05-01

Advances in optical neuroimaging techniques now allow neural activity to be recorded with cellular resolution in awake and behaving animals. Brain motion in these recordings pose a unique challenge. The location of individual neurons must be tracked in 3D over time to accurately extract single neuron activity traces. Recordings from small invertebrates like C. elegans are especially challenging because they undergo very large brain motion and deformation during animal movement. Here we present an automated computer vision pipeline to reliably track populations of neurons with single neuron resolution in the brain of a freely moving C. elegans undergoing large motion and deformation. 3D volumetric fluorescent images of the animal's brain are straightened, aligned and registered, and the locations of neurons in the images are found via segmentation. Each neuron is then assigned an identity using a new time-independent machine-learning approach we call Neuron Registration Vector Encoding. In this approach, non-rigid point-set registration is used to match each segmented neuron in each volume with a set of reference volumes taken from throughout the recording. The way each neuron matches with the references defines a feature vector which is clustered to assign an identity to each neuron in each volume. Finally, thin-plate spline interpolation is used to correct errors in segmentation and check consistency of assigned identities. The Neuron Registration Vector Encoding approach proposed here is uniquely well suited for tracking neurons in brains undergoing large deformations. When applied to whole-brain calcium imaging recordings in freely moving C. elegans, this analysis pipeline located 156 neurons for the duration of an 8 minute recording and consistently found more neurons more quickly than manual or semi-automated approaches.

DCS-SVM: a novel semi-automated method for human brain MR image segmentation.

PubMed

Ahmadvand, Ali; Daliri, Mohammad Reza; Hajiali, Mohammadtaghi

2017-11-27

In this paper, a novel method is proposed which appropriately segments magnetic resonance (MR) brain images into three main tissues. This paper proposes an extension of our previous work in which we suggested a combination of multiple classifiers (CMC)-based methods named dynamic classifier selection-dynamic local training local Tanimoto index (DCS-DLTLTI) for MR brain image segmentation into three main cerebral tissues. This idea is used here and a novel method is developed that tries to use more complex and accurate classifiers like support vector machine (SVM) in the ensemble. This work is challenging because the CMC-based methods are time consuming, especially on huge datasets like three-dimensional (3D) brain MR images. Moreover, SVM is a powerful method that is used for modeling datasets with complex feature space, but it also has huge computational cost for big datasets, especially those with strong interclass variability problems and with more than two classes such as 3D brain images; therefore, we cannot use SVM in DCS-DLTLTI. Therefore, we propose a novel approach named "DCS-SVM" to use SVM in DCS-DLTLTI to improve the accuracy of segmentation results. The proposed method is applied on well-known datasets of the Internet Brain Segmentation Repository (IBSR) and promising results are obtained.

Automated 3D Ultrasound Image Segmentation to Aid Breast Cancer Image Interpretation

PubMed Central

Gu, Peng; Lee, Won-Mean; Roubidoux, Marilyn A.; Yuan, Jie; Wang, Xueding; Carson, Paul L.

2015-01-01

Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer. PMID:26547117

Automated 3D ultrasound image segmentation for assistant diagnosis of breast cancer

NASA Astrophysics Data System (ADS)

Wang, Yuxin; Gu, Peng; Lee, Won-Mean; Roubidoux, Marilyn A.; Du, Sidan; Yuan, Jie; Wang, Xueding; Carson, Paul L.

2016-04-01

Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer.

An Automated Motion Detection and Reward System for Animal Training.

PubMed

Miller, Brad; Lim, Audrey N; Heidbreder, Arnold F; Black, Kevin J

2015-12-04

A variety of approaches has been used to minimize head movement during functional brain imaging studies in awake laboratory animals. Many laboratories expend substantial effort and time training animals to remain essentially motionless during such studies. We could not locate an "off-the-shelf" automated training system that suited our needs.  We developed a time- and labor-saving automated system to train animals to hold still for extended periods of time. The system uses a personal computer and modest external hardware to provide stimulus cues, monitor movement using commercial video surveillance components, and dispense rewards. A custom computer program automatically increases the motionless duration required for rewards based on performance during the training session but allows changes during sessions. This system was used to train cynomolgus monkeys (Macaca fascicularis) for awake neuroimaging studies using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). The automated system saved the trainer substantial time, presented stimuli and rewards in a highly consistent manner, and automatically documented training sessions. We have limited data to prove the training system's success, drawn from the automated records during training sessions, but we believe others may find it useful. The system can be adapted to a range of behavioral training/recording activities for research or commercial applications, and the software is freely available for non-commercial use.

SU-G-BRC-06: Evaluation of a Novel Radiosurgery Software for Treating Multiple Brain Metastases Simultaneously in a Single Fraction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Levin, D; Shekel, E; Epstein, D

Purpose: To evaluate a new, automated brain metastases planning software designed to treat up to ten brain metastases simultaneously. Methods: We treated 61 patients with multiple brain metastases using the Elements software by BrainLab (Munich, Germany). Patients had between 2–10 metastases ranging from 0.01–8.64 cc. Dose prescription was 18–24 Gy. Plans use up to 5 non-coplanar arcs with a single isocenter at the metastases’ center of mass. The high degree of automation shortens the planning time to 15–20 minutes per patient.For comparison we planned 21 of the patients using Rapid Arc (Varian, Palo Alto CA) (RA). We used two coplanarmore » arcs so as to keep planning times comparable to the Elements. We also planned 8 patients using iPlan software (BrainLab). We compared conformity index (CI), volume of brain receiving over 12 Gy (V{sub 12}) and mean brain dose (MBD) for the three different planning systems (TPSs). Results: Plans from all TPSs were judged clinically acceptable. V{sub 12} and MBD were not statistically significantly different between TPSs.CI between RA and Elements was similar, however for iPlan CI was significantly worse compared to both RA and Elements (p<0.001). RA plans took approximately 40 minutes to plan (despite fusion and contouring being done in the Elements), and iPlan plans over an hour each. Delivery times were approximately 30 minutes for Elements, 10 minutes for RA, and up to 300 minutes for iPlan. Conclusion: Elements plans had good CI values and low brain doses. While treatment times for Elements were longer than for RA, 30 minutes is a significant improvement over conventional radiosurgery techniques where each metastasis is treated individually and delivery times to 10 metastases are close to 300 minutes.BrainLab Elements is a novel software allowing fast, automated planning and efficient irradiation of multiple brain metastases with minimal dose to healthy brain.« less

Brain tissues volume measurements from 2D MRI using parametric approach

NASA Astrophysics Data System (ADS)

L'vov, A. A.; Toropova, O. A.; Litovka, Yu. V.

2018-04-01

The purpose of the paper is to propose a fully automated method of volume assessment of structures within human brain. Our statistical approach uses maximum interdependency principle for decision making process of measurements consistency and unequal observations. Detecting outliers performed using maximum normalized residual test. We propose a statistical model which utilizes knowledge of tissues distribution in human brain and applies partial data restoration for precision improvement. The approach proposes completed computationally efficient and independent from segmentation algorithm used in the application.

Preprocessing film-copied MRI for studying morphological brain changes.

PubMed

Pham, Tuan D; Eisenblätter, Uwe; Baune, Bernhard T; Berger, Klaus

2009-06-15

The magnetic resonance imaging (MRI) of the brain is one of the important data items for studying memory and morbidity in elderly as these images can provide useful information through the quantitative measures of various regions of interest of the brain. As an effort to fully automate the biomedical analysis of the brain that can be combined with the genetic data of the same human population and where the records of the original MRI data are missing, this paper presents two effective methods for addressing this imaging problem. The first method handles the restoration of the film-copied MRI. The second method involves the segmentation of the image data. Experimental results and comparisons with other methods suggest the usefulness of the proposed image analysis methodology.

Three-dimensional, automated, real-time video system for tracking limb motion in brain-machine interface studies.

PubMed

Peikon, Ian D; Fitzsimmons, Nathan A; Lebedev, Mikhail A; Nicolelis, Miguel A L

2009-06-15

Collection and analysis of limb kinematic data are essential components of the study of biological motion, including research into biomechanics, kinesiology, neurophysiology and brain-machine interfaces (BMIs). In particular, BMI research requires advanced, real-time systems capable of sampling limb kinematics with minimal contact to the subject's body. To answer this demand, we have developed an automated video tracking system for real-time tracking of multiple body parts in freely behaving primates. The system employs high-contrast markers painted on the animal's joints to continuously track the three-dimensional positions of their limbs during activity. Two-dimensional coordinates captured by each video camera are combined and converted to three-dimensional coordinates using a quadratic fitting algorithm. Real-time operation of the system is accomplished using direct memory access (DMA). The system tracks the markers at a rate of 52 frames per second (fps) in real-time and up to 100fps if video recordings are captured to be later analyzed off-line. The system has been tested in several BMI primate experiments, in which limb position was sampled simultaneously with chronic recordings of the extracellular activity of hundreds of cortical cells. During these recordings, multiple computational models were employed to extract a series of kinematic parameters from neuronal ensemble activity in real-time. The system operated reliably under these experimental conditions and was able to compensate for marker occlusions that occurred during natural movements. We propose that this system could also be extended to applications that include other classes of biological motion.

MIMoSA: An Automated Method for Intermodal Segmentation Analysis of Multiple Sclerosis Brain Lesions.

PubMed

Valcarcel, Alessandra M; Linn, Kristin A; Vandekar, Simon N; Satterthwaite, Theodore D; Muschelli, John; Calabresi, Peter A; Pham, Dzung L; Martin, Melissa Lynne; Shinohara, Russell T

2018-03-08

Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WMLs) in multiple sclerosis. While WMLs have been studied for over two decades using MRI, automated segmentation remains challenging. Although the majority of statistical techniques for the automated segmentation of WMLs are based on single imaging modalities, recent advances have used multimodal techniques for identifying WMLs. Complementary modalities emphasize different tissue properties, which help identify interrelated features of lesions. Method for Inter-Modal Segmentation Analysis (MIMoSA), a fully automatic lesion segmentation algorithm that utilizes novel covariance features from intermodal coupling regression in addition to mean structure to model the probability lesion is contained in each voxel, is proposed. MIMoSA was validated by comparison with both expert manual and other automated segmentation methods in two datasets. The first included 98 subjects imaged at Johns Hopkins Hospital in which bootstrap cross-validation was used to compare the performance of MIMoSA against OASIS and LesionTOADS, two popular automatic segmentation approaches. For a secondary validation, a publicly available data from a segmentation challenge were used for performance benchmarking. In the Johns Hopkins study, MIMoSA yielded average Sørensen-Dice coefficient (DSC) of .57 and partial AUC of .68 calculated with false positive rates up to 1%. This was superior to performance using OASIS and LesionTOADS. The proposed method also performed competitively in the segmentation challenge dataset. MIMoSA resulted in statistically significant improvements in lesion segmentation performance compared with LesionTOADS and OASIS, and performed competitively in an additional validation study. Copyright © 2018 by the American Society of Neuroimaging.

A new method for automated high-dimensional lesion segmentation evaluated in vascular injury and applied to the human occipital lobe.

PubMed

Mah, Yee-Haur; Jager, Rolf; Kennard, Christopher; Husain, Masud; Nachev, Parashkev

2014-07-01

Making robust inferences about the functional neuroanatomy of the brain is critically dependent on experimental techniques that examine the consequences of focal loss of brain function. Unfortunately, the use of the most comprehensive such technique-lesion-function mapping-is complicated by the need for time-consuming and subjective manual delineation of the lesions, greatly limiting the practicability of the approach. Here we exploit a recently-described general measure of statistical anomaly, zeta, to devise a fully-automated, high-dimensional algorithm for identifying the parameters of lesions within a brain image given a reference set of normal brain images. We proceed to evaluate such an algorithm in the context of diffusion-weighted imaging of the commonest type of lesion used in neuroanatomical research: ischaemic damage. Summary performance metrics exceed those previously published for diffusion-weighted imaging and approach the current gold standard-manual segmentation-sufficiently closely for fully-automated lesion-mapping studies to become a possibility. We apply the new method to 435 unselected images of patients with ischaemic stroke to derive a probabilistic map of the pattern of damage in lesions involving the occipital lobe, demonstrating the variation of anatomical resolvability of occipital areas so as to guide future lesion-function studies of the region. Copyright © 2012 Elsevier Ltd. All rights reserved.

MRIVIEW: An interactive computational tool for investigation of brain structure and function

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ranken, D.; George, J.

MRIVIEW is a software system which uses image processing and visualization to provide neuroscience researchers with an integrated environment for combining functional and anatomical information. Key features of the software include semi-automated segmentation of volumetric head data and an interactive coordinate reconciliation method which utilizes surface visualization. The current system is a precursor to a computational brain atlas. We describe features this atlas will incorporate, including methods under development for visualizing brain functional data obtained from several different research modalities.

A high resolution spatiotemporal atlas of gene expression of the developing mouse brain

PubMed Central

Thompson, Carol L.; Ng, Lydia; Menon, Vilas; Martinez, Salvador; Lee, Chang-Kyu; Glattfelder, Katie; Sunkin, Susan M.; Henry, Alex; Lau, Christopher; Dang, Chinh; Garcia-Lopez, Raquel; Martinez-Ferre, Almudena; Pombero, Ana; Rubenstein, John L.R.; Wakeman, Wayne B.; Hohmann, John; Dee, Nick; Sodt, Andrew J.; Young, Rob; Smith, Kimberly; Nguyen, Thuc-Nghi; Kidney, Jolene; Kuan, Leonard; Jeromin, Andreas; Kaykas, Ajamete; Miller, Jeremy; Page, Damon; Orta, Geri; Bernard, Amy; Riley, Zackery; Smith, Simon; Wohnoutka, Paul; Hawrylycz, Mike; Puelles, Luis; Jones, Allan R.

2015-01-01

SUMMARY To provide a temporal framework for the genoarchitecture of brain development, in situ hybridization data were generated for embryonic and postnatal mouse brain at 7 developmental stages for ~2100 genes, processed with an automated informatics pipeline and manually annotated. This resource comprises 434,946 images, 7 reference atlases, an ontogenetic ontology, and tools to explore co-expression of genes across neurodevelopment. Gene sets coinciding with developmental phenomena were identified. A temporal shift in the principles governing the molecular organization of the brain was detected, with transient neuromeric, plate-based organization of the brain present at E11.5 and E13.5. Finally, these data provided a transcription factor code that discriminates brain structures and identifies the developmental age of a tissue, providing a foundation for eventual genetic manipulation or tracking of specific brain structures over development. The resource is available as the Allen Developing Mouse Brain Atlas (developingmouse.brain-map.org). PMID:24952961

Brief Report: CANTAB Performance and Brain Structure in Pediatric Patients with Asperger Syndrome

ERIC Educational Resources Information Center

Kaufmann, Liane; Zotter, Sibylle; Pixner, Silvia; Starke, Marc; Haberlandt, Edda; Steinmayr-Gensluckner, Maria; Egger, Karl; Schocke, Michael; Weiss, Elisabeth M.; Marksteiner, Josef

2013-01-01

By merging neuropsychological (CANTAB/Cambridge Neuropsychological Test Automated Battery) and structural brain imaging data (voxel-based-morphometry) the present study sought to identify the neurocognitive correlates of executive functions in individuals with Asperger syndrome (AS) compared to healthy controls. Results disclosed subtle group…

78 FR 66039 - Modification of National Customs Automation Program Test Concerning Automated Commercial...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-04

... Customs Automation Program Test Concerning Automated Commercial Environment (ACE) Cargo Release (Formerly... Simplified Entry functionality in the Automated Commercial Environment (ACE). Originally, the test was known...) test concerning Automated Commercial Environment (ACE) Simplified Entry (SE test) functionality is...

Corpus Callosum Area and Brain Volume in Autism Spectrum Disorder: Quantitative Analysis of Structural MRI from the ABIDE Database

ERIC Educational Resources Information Center

Kucharsky Hiess, R.; Alter, R.; Sojoudi, S.; Ardekani, B. A.; Kuzniecky, R.; Pardoe, H. R.

2015-01-01

Reduced corpus callosum area and increased brain volume are two commonly reported findings in autism spectrum disorder (ASD). We investigated these two correlates in ASD and healthy controls using T1-weighted MRI scans from the Autism Brain Imaging Data Exchange (ABIDE). Automated methods were used to segment the corpus callosum and intracranial…

Improved Detection of New MS Lesions during Follow-Up Using an Automated MR Coregistration-Fusion Method.

PubMed

Galletto Pregliasco, A; Collin, A; Guéguen, A; Metten, M A; Aboab, J; Deschamps, R; Gout, O; Duron, L; Sadik, J C; Savatovsky, J; Lecler, A

2018-06-07

MR imaging is the key examination in the follow-up of patients with MS, by identification of new high-signal T2 brain lesions. However, identifying new lesions when scrolling through 2 follow-up MR images can be difficult and time-consuming. Our aim was to compare an automated coregistration-fusion reading approach with the standard approach by identifying new high-signal T2 brain lesions in patients with multiple sclerosis during follow-up MR imaging. This prospective monocenter study included 94 patients (mean age, 38.9 years) treated for MS with dimethyl fumarate from January 2014 to August 2016. One senior neuroradiologist and 1 junior radiologist checked for new high-signal T2 brain lesions, independently analyzing blinded image datasets with automated coregistration-fusion or the standard scroll-through approach with a 3-week delay between the 2 readings. A consensus reading with a second senior neuroradiologist served as a criterion standard for analyses. A Poisson regression and logistic and γ regressions were used to compare the 2 methods. Intra- and interobserver agreement was assessed by the κ coefficient. There were significantly more new high-signal T2 lesions per patient detected with the coregistration-fusion method (7 versus 4, P < .001). The coregistration-fusion method detected significantly more patients with at least 1 new high-signal T2 lesion (59% versus 46%, P = .02) and was associated with significantly faster overall reading time (86 seconds faster, P < .001) and higher reader confidence (91% versus 40%, P < 1 × 10 -4 ). Inter- and intraobserver agreement was excellent for counting new high-signal T2 lesions. Our study showed that an automated coregistration-fusion method was more sensitive for detecting new high-signal T2 lesions in patients with MS and reducing reading time. This method could help to improve follow-up care. © 2018 by American Journal of Neuroradiology.

Role of home automation in distribution automation and automated meter reading. Tropical report, December 1994

DOE Office of Scientific and Technical Information (OSTI.GOV)

Davis, K.W.

1994-12-01

This is one of a series of topical reports dealing with the strategic, technical, and market development of home automation. Particular emphasis is placed upon identifying those aspects of home automation that will impact the gas industry and gas products. Communication standards, market drivers, key organizations, technical implementation, product opportunities, and market growth projects will all be addressed in this or subsequent reports. These reports will also discuss how the gas industry and gas-fired equipment can use home automation technology to benefit the consumer.

High-throughput 3D whole-brain quantitative histopathology in rodents

PubMed Central

Vandenberghe, Michel E.; Hérard, Anne-Sophie; Souedet, Nicolas; Sadouni, Elmahdi; Santin, Mathieu D.; Briet, Dominique; Carré, Denis; Schulz, Jocelyne; Hantraye, Philippe; Chabrier, Pierre-Etienne; Rooney, Thomas; Debeir, Thomas; Blanchard, Véronique; Pradier, Laurent; Dhenain, Marc; Delzescaux, Thierry

2016-01-01

Histology is the gold standard to unveil microscopic brain structures and pathological alterations in humans and animal models of disease. However, due to tedious manual interventions, quantification of histopathological markers is classically performed on a few tissue sections, thus restricting measurements to limited portions of the brain. Recently developed 3D microscopic imaging techniques have allowed in-depth study of neuroanatomy. However, quantitative methods are still lacking for whole-brain analysis of cellular and pathological markers. Here, we propose a ready-to-use, automated, and scalable method to thoroughly quantify histopathological markers in 3D in rodent whole brains. It relies on block-face photography, serial histology and 3D-HAPi (Three Dimensional Histology Analysis Pipeline), an open source image analysis software. We illustrate our method in studies involving mouse models of Alzheimer’s disease and show that it can be broadly applied to characterize animal models of brain diseases, to evaluate therapeutic interventions, to anatomically correlate cellular and pathological markers throughout the entire brain and to validate in vivo imaging techniques. PMID:26876372

PANDA: a pipeline toolbox for analyzing brain diffusion images.

PubMed

Cui, Zaixu; Zhong, Suyu; Xu, Pengfei; He, Yong; Gong, Gaolang

2013-01-01

Diffusion magnetic resonance imaging (dMRI) is widely used in both scientific research and clinical practice in in-vivo studies of the human brain. While a number of post-processing packages have been developed, fully automated processing of dMRI datasets remains challenging. Here, we developed a MATLAB toolbox named "Pipeline for Analyzing braiN Diffusion imAges" (PANDA) for fully automated processing of brain diffusion images. The processing modules of a few established packages, including FMRIB Software Library (FSL), Pipeline System for Octave and Matlab (PSOM), Diffusion Toolkit and MRIcron, were employed in PANDA. Using any number of raw dMRI datasets from different subjects, in either DICOM or NIfTI format, PANDA can automatically perform a series of steps to process DICOM/NIfTI to diffusion metrics [e.g., fractional anisotropy (FA) and mean diffusivity (MD)] that are ready for statistical analysis at the voxel-level, the atlas-level and the Tract-Based Spatial Statistics (TBSS)-level and can finish the construction of anatomical brain networks for all subjects. In particular, PANDA can process different subjects in parallel, using multiple cores either in a single computer or in a distributed computing environment, thus greatly reducing the time cost when dealing with a large number of datasets. In addition, PANDA has a friendly graphical user interface (GUI), allowing the user to be interactive and to adjust the input/output settings, as well as the processing parameters. As an open-source package, PANDA is freely available at http://www.nitrc.org/projects/panda/. This novel toolbox is expected to substantially simplify the image processing of dMRI datasets and facilitate human structural connectome studies.

PANDA: a pipeline toolbox for analyzing brain diffusion images

PubMed Central

Cui, Zaixu; Zhong, Suyu; Xu, Pengfei; He, Yong; Gong, Gaolang

2013-01-01

Diffusion magnetic resonance imaging (dMRI) is widely used in both scientific research and clinical practice in in-vivo studies of the human brain. While a number of post-processing packages have been developed, fully automated processing of dMRI datasets remains challenging. Here, we developed a MATLAB toolbox named “Pipeline for Analyzing braiN Diffusion imAges” (PANDA) for fully automated processing of brain diffusion images. The processing modules of a few established packages, including FMRIB Software Library (FSL), Pipeline System for Octave and Matlab (PSOM), Diffusion Toolkit and MRIcron, were employed in PANDA. Using any number of raw dMRI datasets from different subjects, in either DICOM or NIfTI format, PANDA can automatically perform a series of steps to process DICOM/NIfTI to diffusion metrics [e.g., fractional anisotropy (FA) and mean diffusivity (MD)] that are ready for statistical analysis at the voxel-level, the atlas-level and the Tract-Based Spatial Statistics (TBSS)-level and can finish the construction of anatomical brain networks for all subjects. In particular, PANDA can process different subjects in parallel, using multiple cores either in a single computer or in a distributed computing environment, thus greatly reducing the time cost when dealing with a large number of datasets. In addition, PANDA has a friendly graphical user interface (GUI), allowing the user to be interactive and to adjust the input/output settings, as well as the processing parameters. As an open-source package, PANDA is freely available at http://www.nitrc.org/projects/panda/. This novel toolbox is expected to substantially simplify the image processing of dMRI datasets and facilitate human structural connectome studies. PMID:23439846

Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving.

PubMed

Hergeth, Sebastian; Lorenz, Lutz; Vilimek, Roman; Krems, Josef F

2016-05-01

The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving simulator study. Earlier research from other domains indicates that drivers' automation trust might be inferred from gaze behavior, such as monitoring frequency. The gaze behavior and self-reported automation trust of 35 participants attending to a visually demanding non-driving-related task (NDRT) during highly automated driving was evaluated. The relationship between dispositional, situational, and learned automation trust with gaze behavior was compared. Overall, there was a consistent relationship between drivers' automation trust and gaze behavior. Participants reporting higher automation trust tended to monitor the automation less frequently. Further analyses revealed that higher automation trust was associated with lower monitoring frequency of the automation during NDRTs, and an increase in trust over the experimental session was connected with a decrease in monitoring frequency. We suggest that (a) the current results indicate a negative relationship between drivers' self-reported automation trust and monitoring frequency, (b) gaze behavior provides a more direct measure of automation trust than other behavioral measures, and (c) with further refinement, drivers' automation trust during highly automated driving might be inferred from gaze behavior. Potential applications of this research include the estimation of drivers' automation trust and reliance during highly automated driving. © 2016, Human Factors and Ergonomics Society.

Predicting Intracranial Pressure and Brain Tissue Oxygen Crises in Patients With Severe Traumatic Brain Injury.

PubMed

Myers, Risa B; Lazaridis, Christos; Jermaine, Christopher M; Robertson, Claudia S; Rusin, Craig G

2016-09-01

To develop computer algorithms that can recognize physiologic patterns in traumatic brain injury patients that occur in advance of intracranial pressure and partial brain tissue oxygenation crises. The automated early detection of crisis precursors can provide clinicians with time to intervene in order to prevent or mitigate secondary brain injury. A retrospective study was conducted from prospectively collected physiologic data. intracranial pressure, and partial brain tissue oxygenation crisis events were defined as intracranial pressure of greater than or equal to 20 mm Hg lasting at least 15 minutes and partial brain tissue oxygenation value of less than 10 mm Hg for at least 10 minutes, respectively. The physiologic data preceding each crisis event were used to identify precursors associated with crisis onset. Multivariate classification models were applied to recorded data in 30-minute epochs of time to predict crises between 15 and 360 minutes in the future. The neurosurgical unit of Ben Taub Hospital (Houston, TX). Our cohort consisted of 817 subjects with severe traumatic brain injury. Our algorithm can predict the onset of intracranial pressure crises with 30-minute advance warning with an area under the receiver operating characteristic curve of 0.86 using only intracranial pressure measurements and time since last crisis. An analogous algorithm can predict the start of partial brain tissue oxygenation crises with 30-minute advanced warning with an area under the receiver operating characteristic curve of 0.91. Our algorithms provide accurate and timely predictions of intracranial hypertension and tissue hypoxia crises in patients with severe traumatic brain injury. Almost all of the information needed to predict the onset of these events is contained within the signal of interest and the time since last crisis.

UBO Detector - A cluster-based, fully automated pipeline for extracting white matter hyperintensities.

PubMed

Jiang, Jiyang; Liu, Tao; Zhu, Wanlin; Koncz, Rebecca; Liu, Hao; Lee, Teresa; Sachdev, Perminder S; Wen, Wei

2018-07-01

We present 'UBO Detector', a cluster-based, fully automated pipeline for extracting and calculating variables for regions of white matter hyperintensities (WMH) (available for download at https://cheba.unsw.edu.au/group/neuroimaging-pipeline). It takes T1-weighted and fluid attenuated inversion recovery (FLAIR) scans as input, and SPM12 and FSL functions are utilised for pre-processing. The candidate clusters are then generated by FMRIB's Automated Segmentation Tool (FAST). A supervised machine learning algorithm, k-nearest neighbor (k-NN), is applied to determine whether the candidate clusters are WMH or non-WMH. UBO Detector generates both image and text (volumes and the number of WMH clusters) outputs for whole brain, periventricular, deep, and lobar WMH, as well as WMH in arterial territories. The computation time for each brain is approximately 15 min. We validated the performance of UBO Detector by showing a) high segmentation (similarity index (SI) = 0.848) and volumetric (intraclass correlation coefficient (ICC) = 0.985) agreement between the UBO Detector-derived and manually traced WMH; b) highly correlated (r 2  > 0.9) and a steady increase of WMH volumes over time; and c) significant associations of periventricular (t = 22.591, p < 0.001) and deep (t = 14.523, p < 0.001) WMH volumes generated by UBO Detector with Fazekas rating scores. With parallel computing enabled in UBO Detector, the processing can take advantage of multi-core CPU's that are commonly available on workstations. In conclusion, UBO Detector is a reliable, efficient and fully automated WMH segmentation pipeline. Copyright © 2018 Elsevier Inc. All rights reserved.

AUTOMATED CELL SEGMENTATION WITH 3D FLUORESCENCE MICROSCOPY IMAGES.

PubMed

Kong, Jun; Wang, Fusheng; Teodoro, George; Liang, Yanhui; Zhu, Yangyang; Tucker-Burden, Carol; Brat, Daniel J

2015-04-01

A large number of cell-oriented cancer investigations require an effective and reliable cell segmentation method on three dimensional (3D) fluorescence microscopic images for quantitative analysis of cell biological properties. In this paper, we present a fully automated cell segmentation method that can detect cells from 3D fluorescence microscopic images. Enlightened by fluorescence imaging techniques, we regulated the image gradient field by gradient vector flow (GVF) with interpolated and smoothed data volume, and grouped voxels based on gradient modes identified by tracking GVF field. Adaptive thresholding was then applied to voxels associated with the same gradient mode where voxel intensities were enhanced by a multiscale cell filter. We applied the method to a large volume of 3D fluorescence imaging data of human brain tumor cells with (1) small cell false detection and missing rates for individual cells; and (2) trivial over and under segmentation incidences for clustered cells. Additionally, the concordance of cell morphometry structure between automated and manual segmentation was encouraging. These results suggest a promising 3D cell segmentation method applicable to cancer studies.

The role of image registration in brain mapping

PubMed Central

Toga, A.W.; Thompson, P.M.

2008-01-01

Image registration is a key step in a great variety of biomedical imaging applications. It provides the ability to geometrically align one dataset with another, and is a prerequisite for all imaging applications that compare datasets across subjects, imaging modalities, or across time. Registration algorithms also enable the pooling and comparison of experimental findings across laboratories, the construction of population-based brain atlases, and the creation of systems to detect group patterns in structural and functional imaging data. We review the major types of registration approaches used in brain imaging today. We focus on their conceptual basis, the underlying mathematics, and their strengths and weaknesses in different contexts. We describe the major goals of registration, including data fusion, quantification of change, automated image segmentation and labeling, shape measurement, and pathology detection. We indicate that registration algorithms have great potential when used in conjunction with a digital brain atlas, which acts as a reference system in which brain images can be compared for statistical analysis. The resulting armory of registration approaches is fundamental to medical image analysis, and in a brain mapping context provides a means to elucidate clinical, demographic, or functional trends in the anatomy or physiology of the brain. PMID:19890483

Structural Image Analysis of the Brain in Neuropsychology Using Magnetic Resonance Imaging (MRI) Techniques.

PubMed

Bigler, Erin D

2015-09-01

Magnetic resonance imaging (MRI) of the brain provides exceptional image quality for visualization and neuroanatomical classification of brain structure. A variety of image analysis techniques provide both qualitative as well as quantitative methods to relate brain structure with neuropsychological outcome and are reviewed herein. Of particular importance are more automated methods that permit analysis of a broad spectrum of anatomical measures including volume, thickness and shape. The challenge for neuropsychology is which metric to use, for which disorder and the timing of when image analysis methods are applied to assess brain structure and pathology. A basic overview is provided as to the anatomical and pathoanatomical relations of different MRI sequences in assessing normal and abnormal findings. Some interpretive guidelines are offered including factors related to similarity and symmetry of typical brain development along with size-normalcy features of brain anatomy related to function. The review concludes with a detailed example of various quantitative techniques applied to analyzing brain structure for neuropsychological outcome studies in traumatic brain injury.

Current automated 3D cell detection methods are not a suitable replacement for manual stereologic cell counting

PubMed Central

Schmitz, Christoph; Eastwood, Brian S.; Tappan, Susan J.; Glaser, Jack R.; Peterson, Daniel A.; Hof, Patrick R.

2014-01-01

Stereologic cell counting has had a major impact on the field of neuroscience. A major bottleneck in stereologic cell counting is that the user must manually decide whether or not each cell is counted according to three-dimensional (3D) stereologic counting rules by visual inspection within hundreds of microscopic fields-of-view per investigated brain or brain region. Reliance on visual inspection forces stereologic cell counting to be very labor-intensive and time-consuming, and is the main reason why biased, non-stereologic two-dimensional (2D) “cell counting” approaches have remained in widespread use. We present an evaluation of the performance of modern automated cell detection and segmentation algorithms as a potential alternative to the manual approach in stereologic cell counting. The image data used in this study were 3D microscopic images of thick brain tissue sections prepared with a variety of commonly used nuclear and cytoplasmic stains. The evaluation compared the numbers and locations of cells identified unambiguously and counted exhaustively by an expert observer with those found by three automated 3D cell detection algorithms: nuclei segmentation from the FARSIGHT toolkit, nuclei segmentation by 3D multiple level set methods, and the 3D object counter plug-in for ImageJ. Of these methods, FARSIGHT performed best, with true-positive detection rates between 38 and 99% and false-positive rates from 3.6 to 82%. The results demonstrate that the current automated methods suffer from lower detection rates and higher false-positive rates than are acceptable for obtaining valid estimates of cell numbers. Thus, at present, stereologic cell counting with manual decision for object inclusion according to unbiased stereologic counting rules remains the only adequate method for unbiased cell quantification in histologic tissue sections. PMID:24847213

An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification.

PubMed

Siddiqui, Muhammad Faisal; Reza, Ahmed Wasif; Kanesan, Jeevan

2015-01-01

A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the

Process development for automated solar cell and module production. Task 4: Automated array assembly

NASA Technical Reports Server (NTRS)

1980-01-01

A process sequence which can be used in conjunction with automated equipment for the mass production of solar cell modules for terrestrial use was developed. The process sequence was then critically analyzed from a technical and economic standpoint to determine the technological readiness of certain process steps for implementation. The steps receiving analysis were: back contact metallization, automated cell array layup/interconnect, and module edge sealing. For automated layup/interconnect, both hard automation and programmable automation (using an industrial robot) were studied. The programmable automation system was then selected for actual hardware development.

Management Planning for Workplace Automation.

ERIC Educational Resources Information Center

McDole, Thomas L.

Several factors must be considered when implementing office automation. Included among these are whether or not to automate at all, the effects of automation on employees, requirements imposed by automation on the physical environment, effects of automation on the total organization, and effects on clientele. The reasons behind the success or…

Automated acoustic matrix deposition for MALDI sample preparation.

PubMed

Aerni, Hans-Rudolf; Cornett, Dale S; Caprioli, Richard M

2006-02-01

Novel high-throughput sample preparation strategies for MALDI imaging mass spectrometry (IMS) and profiling are presented. An acoustic reagent multispotter was developed to provide improved reproducibility for depositing matrix onto a sample surface, for example, such as a tissue section. The unique design of the acoustic droplet ejector and its optimization for depositing matrix solution are discussed. Since it does not contain a capillary or nozzle for fluid ejection, issues with clogging of these orifices are avoided. Automated matrix deposition provides better control of conditions affecting protein extraction and matrix crystallization with the ability to deposit matrix accurately onto small surface features. For tissue sections, matrix spots of 180-200 microm in diameter were obtained and a procedure is described for generating coordinate files readable by a mass spectrometer to permit automated profile acquisition. Mass spectral quality and reproducibility was found to be better than that obtained with manual pipet spotting. The instrument can also deposit matrix spots in a dense array pattern so that, after analysis in a mass spectrometer, two-dimensional ion images may be constructed. Example ion images from a mouse brain are presented.

Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA.

PubMed

Sai, Chong Yeh; Mokhtar, Norrima; Arof, Hamzah; Cumming, Paul; Iwahashi, Masahiro

2018-05-01

Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain-computer interface applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal. We now propose a novel approach for identifying artifactual components separated by wavelet-ICA using a pretrained support vector machine (SVM). Our method presents a robust and extendable system that enables fully automated identification and removal of artifacts from EEG signals, without applying any arbitrary thresholding. Using test data contaminated by eye blink artifacts, we show that our method performed better in identifying artifactual components than did existing thresholding methods. Furthermore, wavelet-ICA in conjunction with SVM successfully removed target artifacts, while largely retaining the EEG source signals of interest. We propose a set of features including kurtosis, variance, Shannon's entropy, and range of amplitude as training and test data of SVM to identify eye blink artifacts in EEG signals. This combinatorial method is also extendable to accommodate multiple types of artifacts present in multichannel EEG. We envision future research to explore other descriptive features corresponding to other types of artifactual components.

Automation in astronomy.

NASA Technical Reports Server (NTRS)

Wampler, E. J.

1972-01-01

Description and evaluation of the remotely operated Lick Observatory Cassegrain focus of the 120-inch telescope. The experience with this instrument has revealed that an automated system can profoundly change the observer's approach to his work. This makes it difficult to evaluate the 'advantage' of an automated telescope over a conventional instrument. Some of the problems arising with automation in astronomy are discussed.

Automated Computerized Analysis of Speechin Psychiatric Disorders

PubMed Central

Cohen, Alex S.; Elvevåg, Brita

2014-01-01

Purpose of Review Disturbances in communication are a hallmark of severe mental illnesses. Recent technological advances have paved the way for objectifying communication using automated computerized linguistic and acoustic analysis. We review recent studies applying various computer-based assessments to the natural language produced by adult patients with severe mental illness. Recent Findings Automated computerized methods afford tools with which it is possible to objectively evaluate patients in a reliable, valid and efficient manner that complements human ratings. Crucially, these measures correlate with important clinical measures. The clinical relevance of these novel metrics has been demonstrated by showing their relationship to functional outcome measures, their in vivo link to classic ‘language’ regions in the brain, and, in the case of linguistic analysis, their relationship to candidate genes for severe mental illness. Summary Computer based assessments of natural language afford a framework with which to measure communication disturbances in adults with SMI. Emerging evidence suggests that they can be reliable and valid, and overcome many practical limitations of more traditional assessment methods. The advancement of these technologies offers unprecedented potential for measuring and understanding some of the most crippling symptoms of some of the most debilitating illnesses known to humankind. PMID:24613984

Automation in Clinical Microbiology

PubMed Central

Ledeboer, Nathan A.

2013-01-01

Historically, the trend toward automation in clinical pathology laboratories has largely bypassed the clinical microbiology laboratory. In this article, we review the historical impediments to automation in the microbiology laboratory and offer insight into the reasons why we believe that we are on the cusp of a dramatic change that will sweep a wave of automation into clinical microbiology laboratories. We review the currently available specimen-processing instruments as well as the total laboratory automation solutions. Lastly, we outline the types of studies that will need to be performed to fully assess the benefits of automation in microbiology laboratories. PMID:23515547

Virtual automation.

PubMed

Casis, E; Garrido, A; Uranga, B; Vives, A; Zufiaurre, C

2001-01-01

Total laboratory automation (TLA) can be substituted in mid-size laboratories by a computer sample workflow control (virtual automation). Such a solution has been implemented in our laboratory using PSM, software developed in cooperation with Roche Diagnostics (Barcelona, Spain), to this purpose. This software is connected to the online analyzers and to the laboratory information system and is able to control and direct the samples working as an intermediate station. The only difference with TLA is the replacement of transport belts by personnel of the laboratory. The implementation of this virtual automation system has allowed us the achievement of the main advantages of TLA: workload increase (64%) with reduction in the cost per test (43%), significant reduction in the number of biochemistry primary tubes (from 8 to 2), less aliquoting (from 600 to 100 samples/day), automation of functional testing, drastic reduction of preanalytical errors (from 11.7 to 0.4% of the tubes) and better total response time for both inpatients (from up to 48 hours to up to 4 hours) and outpatients (from up to 10 days to up to 48 hours). As an additional advantage, virtual automation could be implemented without hardware investment and significant headcount reduction (15% in our lab).

Laboratory automation: total and subtotal.

PubMed

Hawker, Charles D

2007-12-01

Worldwide, perhaps 2000 or more clinical laboratories have implemented some form of laboratory automation, either a modular automation system, such as for front-end processing, or a total laboratory automation system. This article provides descriptions and examples of these various types of automation. It also presents an outline of how a clinical laboratory that is contemplating automation should approach its decision and the steps it should follow to ensure a successful implementation. Finally, the role of standards in automation is reviewed.

Automatic brain tumor detection in MRI: methodology and statistical validation

NASA Astrophysics Data System (ADS)

Iftekharuddin, Khan M.; Islam, Mohammad A.; Shaik, Jahangheer; Parra, Carlos; Ogg, Robert

2005-04-01

Automated brain tumor segmentation and detection are immensely important in medical diagnostics because it provides information associated to anatomical structures as well as potential abnormal tissue necessary to delineate appropriate surgical planning. In this work, we propose a novel automated brain tumor segmentation technique based on multiresolution texture information that combines fractal Brownian motion (fBm) and wavelet multiresolution analysis. Our wavelet-fractal technique combines the excellent multiresolution localization property of wavelets to texture extraction of fractal. We prove the efficacy of our technique by successfully segmenting pediatric brain MR images (MRIs) from St. Jude Children"s Research Hospital. We use self-organizing map (SOM) as our clustering tool wherein we exploit both pixel intensity and multiresolution texture features to obtain segmented tumor. Our test results show that our technique successfully segments abnormal brain tissues in a set of T1 images. In the next step, we design a classifier using Feed-Forward (FF) neural network to statistically validate the presence of tumor in MRI using both the multiresolution texture and the pixel intensity features. We estimate the corresponding receiver operating curve (ROC) based on the findings of true positive fractions and false positive fractions estimated from our classifier at different threshold values. An ROC, which can be considered as a gold standard to prove the competence of a classifier, is obtained to ascertain the sensitivity and specificity of our classifier. We observe that at threshold 0.4 we achieve true positive value of 1.0 (100%) sacrificing only 0.16 (16%) false positive value for the set of 50 T1 MRI analyzed in this experiment.

AutoQSAR: an automated machine learning tool for best-practice quantitative structure-activity relationship modeling.

PubMed

Dixon, Steven L; Duan, Jianxin; Smith, Ethan; Von Bargen, Christopher D; Sherman, Woody; Repasky, Matthew P

2016-10-01

We introduce AutoQSAR, an automated machine-learning application to build, validate and deploy quantitative structure-activity relationship (QSAR) models. The process of descriptor generation, feature selection and the creation of a large number of QSAR models has been automated into a single workflow within AutoQSAR. The models are built using a variety of machine-learning methods, and each model is scored using a novel approach. Effectiveness of the method is demonstrated through comparison with literature QSAR models using identical datasets for six end points: protein-ligand binding affinity, solubility, blood-brain barrier permeability, carcinogenicity, mutagenicity and bioaccumulation in fish. AutoQSAR demonstrates similar or better predictive performance as compared with published results for four of the six endpoints while requiring minimal human time and expertise.

Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System.

PubMed

Poon, Candice C; Ebacher, Vincent; Liu, Katherine; Yong, Voon Wee; Kelly, John James Patrick

2018-05-03

Automated slide scanning and segmentation of fluorescently-labeled tissues is the most efficient way to analyze whole slides or large tissue sections. Unfortunately, many researchers spend large amounts of time and resources developing and optimizing workflows that are only relevant to their own experiments. In this article, we describe a protocol that can be used by those with access to a widefield high-content analysis system (WHCAS) to image any slide-mounted tissue, with options for customization within pre-built modules found in the associated software. Not originally intended for slide scanning, the steps detailed in this article make it possible to acquire slide scanning images in the WHCAS which can be imported into the associated software. In this example, the automated segmentation of brain tumor slides is demonstrated, but the automated segmentation of any fluorescently-labeled nuclear or cytoplasmic marker is possible. Furthermore, there are a variety of other quantitative software modules including assays for protein localization/translocation, cellular proliferation/viability/apoptosis, and angiogenesis that can be run. This technique will save researchers time and effort and create an automated protocol for slide analysis.

A Hybrid Hierarchical Approach for Brain Tissue Segmentation by Combining Brain Atlas and Least Square Support Vector Machine

PubMed Central

Kasiri, Keyvan; Kazemi, Kamran; Dehghani, Mohammad Javad; Helfroush, Mohammad Sadegh

2013-01-01

In this paper, we present a new semi-automatic brain tissue segmentation method based on a hybrid hierarchical approach that combines a brain atlas as a priori information and a least-square support vector machine (LS-SVM). The method consists of three steps. In the first two steps, the skull is removed and the cerebrospinal fluid (CSF) is extracted. These two steps are performed using the toolbox FMRIB's automated segmentation tool integrated in the FSL software (FSL-FAST) developed in Oxford Centre for functional MRI of the brain (FMRIB). Then, in the third step, the LS-SVM is used to segment grey matter (GM) and white matter (WM). The training samples for LS-SVM are selected from the registered brain atlas. The voxel intensities and spatial positions are selected as the two feature groups for training and test. SVM as a powerful discriminator is able to handle nonlinear classification problems; however, it cannot provide posterior probability. Thus, we use a sigmoid function to map the SVM output into probabilities. The proposed method is used to segment CSF, GM and WM from the simulated magnetic resonance imaging (MRI) using Brainweb MRI simulator and real data provided by Internet Brain Segmentation Repository. The semi-automatically segmented brain tissues were evaluated by comparing to the corresponding ground truth. The Dice and Jaccard similarity coefficients, sensitivity and specificity were calculated for the quantitative validation of the results. The quantitative results show that the proposed method segments brain tissues accurately with respect to corresponding ground truth. PMID:24696800

Automated image segmentation using support vector machines

NASA Astrophysics Data System (ADS)

Powell, Stephanie; Magnotta, Vincent A.; Andreasen, Nancy C.

2007-03-01

Neurodegenerative and neurodevelopmental diseases demonstrate problems associated with brain maturation and aging. Automated methods to delineate brain structures of interest are required to analyze large amounts of imaging data like that being collected in several on going multi-center studies. We have previously reported on using artificial neural networks (ANN) to define subcortical brain structures including the thalamus (0.88), caudate (0.85) and the putamen (0.81). In this work, apriori probability information was generated using Thirion's demons registration algorithm. The input vector consisted of apriori probability, spherical coordinates, and an iris of surrounding signal intensity values. We have applied the support vector machine (SVM) machine learning algorithm to automatically segment subcortical and cerebellar regions using the same input vector information. SVM architecture was derived from the ANN framework. Training was completed using a radial-basis function kernel with gamma equal to 5.5. Training was performed using 15,000 vectors collected from 15 training images in approximately 10 minutes. The resulting support vectors were applied to delineate 10 images not part of the training set. Relative overlap calculated for the subcortical structures was 0.87 for the thalamus, 0.84 for the caudate, 0.84 for the putamen, and 0.72 for the hippocampus. Relative overlap for the cerebellar lobes ranged from 0.76 to 0.86. The reliability of the SVM based algorithm was similar to the inter-rater reliability between manual raters and can be achieved without rater intervention.

Effects of low-level exposure to sarin and cyclosarin during the 1991 Gulf War on brain function and brain structure in US veterans.

PubMed

Chao, Linda L; Rothlind, Johannes C; Cardenas, Valerie A; Meyerhoff, Dieter J; Weiner, Michael W

2010-09-01

Potentially more than 100,000 US troops may have been exposed to the organophosphate chemical warfare agents sarin (GB) and cyclosarin (GF) when a munitions dump at Khamisiyah, Iraq was destroyed during the Gulf War (GW) in 1991. Although little is known about the long-term neurobehavioral or neurophysiological effects of low-dose exposure to GB/GF in humans, recent studies of GW veterans from the Devens Cohort suggest decrements in certain cognitive domains and atrophy in brain white matter occur individuals with higher estimated levels of presumed GB/GF exposure. The goal of the current study is to determine the generalizability of these findings in another cohort of GW veterans with suspected GB/GF exposure. Neurobehavioral and imaging data collected in a study on Gulf War Illness between 2002 and 2007 were used in this study. We focused on the data of 40 GW-deployed veterans categorized as having been exposed to GB/GF at Khamisiyah, Iraq and 40 matched controls. Magnetic resonance images (MRI) of the brain were analyzed using automated and semi-automated image processing techniques that produced volumetric measurements of gray matter (GM), white matter (WM), cerebrospinal fluid (CSF) and hippocampus. GW veterans with suspected GB/GF exposure had reduced total GM and hippocampal volumes compared to their unexposed peers (p< or =0.01). Although there were no group differences in measures of cognitive function or total WM volume, there were significant, positive correlations between total WM volume and measures of executive function and visuospatial abilities in veterans with suspected GB/GF exposure. These findings suggest that low-level exposure to GB/GF can have deleterious effects on brain structure and brain function more than decade later. Copyright © 2010 Elsevier Inc. All rights reserved.

Effects of low-level exposure to sarin and cyclosarin during the 1991 Gulf War on brain function and brain structure in US veterans

PubMed Central

Chao, Linda L.; Rothlind, Johannes C.; Cardenas, Valerie A.; Meyerhoff, Dieter J.; Weiner, Michael W.

2010-01-01

Background Potentially more than 100,000 US troops may have been exposed to the organophosphate chemical warfare agents sarin (GB) and cyclosarin (GF) when a munitions dump at Khamisiyah, Iraq was destroyed during the Gulf War (GW) in 1991. Although little is known about the long-term neurobehavioral or neurophysiological effects of low-dose exposure to GB/GF in humans, recent studies of GW veterans from the Devens Cohort suggest decrements in certain cognitive domains and atrophy in brain white matter occur individuals with higher estimated levels of presumed GB/GF exposure. The goal of the current study is to determine the generalizability of these findings in another cohort of GW veterans with suspected GB/GF exposure. Methods Neurobehavioral and imaging data collected in a study on Gulf War Illness between 2002–2007 were used in this study. We focused on the data of 40 GW-deployed veterans categorized as having been exposed to GB/GF at Khamisiyah, Iraq and 40 matched controls. Magnetic resonance images (MRI) of the brain were analyzed using automated and semi-automated image processing techniques that produced volumetric measurements of gray matter (GM), white matter (WM), cerebrospinal fluid (CSF) and hippocampus. Results GW veterans with suspected GB/GF exposure had reduced total GM and hippocampal volumes compared to their unexposed peers (p≤0.01). Although there were no group differences in measures of cognitive function or total WM volume, there were significant, positive correlations between total WM volume and measures of executive function and visuospatial abilities in veterans with suspected GB/GF exposure. Conclusions These findings suggest that low-level exposure to GB/GF can have deleterious effects on brain structure and brain function more than decade later. PMID:20580739

Automation of Oklahoma School Library Media Centers: Automation at the Local Level.

ERIC Educational Resources Information Center

Oklahoma State Dept. of Education, Oklahoma City. Library and Learning Resources Section.

This document outlines a workshop for media specialists--"School Library Automation: Solving the Puzzle"--that is designed to reduce automation anxiety and give a broad overview of the concerns confronting school library media centers planning for or involved in automation. Issues are addressed under the following headings: (1) Levels of School…

Automated volumetry for unilateral hippocampal sclerosis detection in patients with temporal lobe epilepsy.

PubMed

Martins, Cristina; Moreira da Silva, Nadia; Silva, Guilherme; Rozanski, Verena E; Silva Cunha, Joao Paulo

2016-08-01

Hippocampal sclerosis (HS) is the most common cause of temporal lobe epilepsy (TLE) and can be identified in magnetic resonance imaging as hippocampal atrophy and subsequent volume loss. Detecting this kind of abnormalities through simple radiological assessment could be difficult, even for experienced radiologists. For that reason, hippocampal volumetry is generally used to support this kind of diagnosis. Manual volumetry is the traditional approach but it is time consuming and requires the physician to be familiar with neuroimaging software tools. In this paper, we propose an automated method, written as a script that uses FSL-FIRST, to perform hippocampal segmentation and compute an index to quantify hippocampi asymmetry (HAI). We compared the automated detection of HS (left or right) based on the HAI with the agreement of two experts in a group of 19 patients and 15 controls, achieving 84.2% sensitivity, 86.7% specificity and a Cohen's kappa coefficient of 0.704. The proposed method is integrated in the "Advanced Brain Imaging Lab" (ABrIL) cloud neurocomputing platform. The automated procedure is 77% (on average) faster to compute vs. the manual volumetry segmentation performed by an experienced physician.

Bioprocessing automation in cell therapy manufacturing: Outcomes of special interest group automation workshop.

PubMed

Ball, Oliver; Robinson, Sarah; Bure, Kim; Brindley, David A; Mccall, David

2018-04-01

Phacilitate held a Special Interest Group workshop event in Edinburgh, UK, in May 2017. The event brought together leading stakeholders in the cell therapy bioprocessing field to identify present and future challenges and propose potential solutions to automation in cell therapy bioprocessing. Here, we review and summarize discussions from the event. Deep biological understanding of a product, its mechanism of action and indication pathogenesis underpin many factors relating to bioprocessing and automation. To fully exploit the opportunities of bioprocess automation, therapeutics developers must closely consider whether an automation strategy is applicable, how to design an 'automatable' bioprocess and how to implement process modifications with minimal disruption. Major decisions around bioprocess automation strategy should involve all relevant stakeholders; communication between technical and business strategy decision-makers is of particular importance. Developers should leverage automation to implement in-process testing, in turn applicable to process optimization, quality assurance (QA)/ quality control (QC), batch failure control, adaptive manufacturing and regulatory demands, but a lack of precedent and technical opportunities can complicate such efforts. Sparse standardization across product characterization, hardware components and software platforms is perceived to complicate efforts to implement automation. The use of advanced algorithmic approaches such as machine learning may have application to bioprocess and supply chain optimization. Automation can substantially de-risk the wider supply chain, including tracking and traceability, cryopreservation and thawing and logistics. The regulatory implications of automation are currently unclear because few hardware options exist and novel solutions require case-by-case validation, but automation can present attractive regulatory incentives. Copyright © 2018 International Society for Cellular Therapy

Automation: triumph or trap?

PubMed

Smythe, M H

1997-01-01

Automation, a hot topic in the laboratory world today, can be a very expensive option. Those who are considering implementing automation can save time and money by examining the issues from the standpoint of an industrial/manufacturing engineer. The engineer not only asks what problems will be solved by automation, but what problems will be created. This article discusses questions that must be asked and answered to ensure that automation efforts will yield real and substantial payoffs.

Fuzzy Control/Space Station automation

NASA Technical Reports Server (NTRS)

Gersh, Mark

1990-01-01

Viewgraphs on fuzzy control/space station automation are presented. Topics covered include: Space Station Freedom (SSF); SSF evolution; factors pointing to automation & robotics (A&R); astronaut office inputs concerning A&R; flight system automation and ground operations applications; transition definition program; and advanced automation software tools.

Automated method to compute Evans index for diagnosis of idiopathic normal pressure hydrocephalus on brain CT images

NASA Astrophysics Data System (ADS)

Takahashi, Noriyuki; Kinoshita, Toshibumi; Ohmura, Tomomi; Matsuyama, Eri; Toyoshima, Hideto

2017-03-01

The early diagnosis of idiopathic normal pressure hydrocephalus (iNPH) considered as a treatable dementia is important. The iNPH causes enlargement of lateral ventricles (LVs). The degree of the enlargement of the LVs on CT or MR images is evaluated by using a diagnostic imaging criterion, Evans index. Evans index is defined as the ratio of the maximal width of frontal horns (FH) of the LVs to the maximal width of the inner skull (IS). Evans index is the most commonly used parameter for the evaluation of ventricular enlargement. However, manual measurement of Evans index is a time-consuming process. In this study, we present an automated method to compute Evans index on brain CT images. The algorithm of the method consisted of five major steps: standardization of CT data to an atlas, extraction of FH and IS regions, the search for the outmost points of bilateral FH regions, determination of the maximal widths of both the FH and the IS, and calculation of Evans index. The standardization to the atlas was performed by using linear affine transformation and non-linear wrapping techniques. The FH regions were segmented by using a three dimensional region growing technique. This scheme was applied to CT scans from 44 subjects, including 13 iNPH patients. The average difference in Evans index between the proposed method and manual measurement was 0.01 (1.6%), and the correlation coefficient of these data for the Evans index was 0.98. Therefore, this computerized method may have the potential to accurately compute Evans index for the diagnosis of iNPH on CT images.

Preprocessing and meta-classification for brain-computer interfaces.

PubMed

Hammon, Paul S; de Sa, Virginia R

2007-03-01

A brain-computer interface (BCI) is a system which allows direct translation of brain states into actions, bypassing the usual muscular pathways. A BCI system works by extracting user brain signals, applying machine learning algorithms to classify the user's brain state, and performing a computer-controlled action. Our goal is to improve brain state classification. Perhaps the most obvious way to improve classification performance is the selection of an advanced learning algorithm. However, it is now well known in the BCI community that careful selection of preprocessing steps is crucial to the success of any classification scheme. Furthermore, recent work indicates that combining the output of multiple classifiers (meta-classification) leads to improved classification rates relative to single classifiers (Dornhege et al., 2004). In this paper, we develop an automated approach which systematically analyzes the relative contributions of different preprocessing and meta-classification approaches. We apply this procedure to three data sets drawn from BCI Competition 2003 (Blankertz et al., 2004) and BCI Competition III (Blankertz et al., 2006), each of which exhibit very different characteristics. Our final classification results compare favorably with those from past BCI competitions. Additionally, we analyze the relative contributions of individual preprocessing and meta-classification choices and discuss which types of BCI data benefit most from specific algorithms.

Hyper- and viscoelastic modeling of needle and brain tissue interaction.

PubMed

Lehocky, Craig A; Yixing Shi; Riviere, Cameron N

2014-01-01

Deep needle insertion into brain is important for both diagnostic and therapeutic clinical interventions. We have developed an automated system for robotically steering flexible needles within the brain to improve targeting accuracy. In this work, we have developed a finite element needle-tissue interaction model that allows for the investigation of safe parameters for needle steering. The tissue model implemented contains both hyperelastic and viscoelastic properties to simulate the instantaneous and time-dependent responses of brain tissue. Several needle models were developed with varying parameters to study the effects of the parameters on tissue stress, strain and strain rate during needle insertion and rotation. The parameters varied include needle radius, bevel angle, bevel tip fillet radius, insertion speed, and rotation speed. The results will guide the design of safe needle tips and control systems for intracerebral needle steering.

Automated extraction of subdural electrode grid from post-implant MRI scans for epilepsy surgery

NASA Astrophysics Data System (ADS)

Pozdin, Maksym A.; Skrinjar, Oskar

2005-04-01

This paper presents an automated algorithm for extraction of Subdural Electrode Grid (SEG) from post-implant MRI scans for epilepsy surgery. Post-implant MRI scans are corrupted by the image artifacts caused by implanted electrodes. The artifacts appear as dark spherical voids and given that the cerebrospinal fluid is also dark in T1-weigthed MRI scans, it is a difficult and time-consuming task to manually locate SEG position relative to brain structures of interest. The proposed algorithm reliably and accurately extracts SEG from post-implant MRI scan, i.e. finds its shape and position relative to brain structures of interest. The algorithm was validated against manually determined electrode locations, and the average error was 1.6mm for the three tested subjects.

The Brain Is Faster than the Hand in Split-Second Intentions to Respond to an Impending Hazard: A Simulation of Neuroadaptive Automation to Speed Recovery to Perturbation in Flight Attitude.

PubMed

Callan, Daniel E; Terzibas, Cengiz; Cassel, Daniel B; Sato, Masa-Aki; Parasuraman, Raja

2016-01-01

The goal of this research is to test the potential for neuroadaptive automation to improve response speed to a hazardous event by using a brain-computer interface (BCI) to decode perceptual-motor intention. Seven participants underwent four experimental sessions while measuring brain activity with magnetoencephalograpy. The first three sessions were of a simple constrained task in which the participant was to pull back on the control stick to recover from a perturbation in attitude in one condition and to passively observe the perturbation in the other condition. The fourth session consisted of having to recover from a perturbation in attitude while piloting the plane through the Grand Canyon constantly maneuvering to track over the river below. Independent component analysis was used on the first two sessions to extract artifacts and find an event related component associated with the onset of the perturbation. These two sessions were used to train a decoder to classify trials in which the participant recovered from the perturbation (motor intention) vs. just passively viewing the perturbation. The BCI-decoder was tested on the third session of the same simple task and found to be able to significantly distinguish motor intention trials from passive viewing trials (mean = 69.8%). The same BCI-decoder was then used to test the fourth session on the complex task. The BCI-decoder significantly classified perturbation from no perturbation trials (73.3%) with a significant time savings of 72.3 ms (Original response time of 425.0-352.7 ms for BCI-decoder). The BCI-decoder model of the best subject was shown to generalize for both performance and time savings to the other subjects. The results of our off-line open loop simulation demonstrate that BCI based neuroadaptive automation has the potential to decode motor intention faster than manual control in response to a hazardous perturbation in flight attitude while ignoring ongoing motor and visual induced activity

The Brain Is Faster than the Hand in Split-Second Intentions to Respond to an Impending Hazard: A Simulation of Neuroadaptive Automation to Speed Recovery to Perturbation in Flight Attitude

PubMed Central

Callan, Daniel E.; Terzibas, Cengiz; Cassel, Daniel B.; Sato, Masa-aki; Parasuraman, Raja

2016-01-01

The goal of this research is to test the potential for neuroadaptive automation to improve response speed to a hazardous event by using a brain-computer interface (BCI) to decode perceptual-motor intention. Seven participants underwent four experimental sessions while measuring brain activity with magnetoencephalograpy. The first three sessions were of a simple constrained task in which the participant was to pull back on the control stick to recover from a perturbation in attitude in one condition and to passively observe the perturbation in the other condition. The fourth session consisted of having to recover from a perturbation in attitude while piloting the plane through the Grand Canyon constantly maneuvering to track over the river below. Independent component analysis was used on the first two sessions to extract artifacts and find an event related component associated with the onset of the perturbation. These two sessions were used to train a decoder to classify trials in which the participant recovered from the perturbation (motor intention) vs. just passively viewing the perturbation. The BCI-decoder was tested on the third session of the same simple task and found to be able to significantly distinguish motor intention trials from passive viewing trials (mean = 69.8%). The same BCI-decoder was then used to test the fourth session on the complex task. The BCI-decoder significantly classified perturbation from no perturbation trials (73.3%) with a significant time savings of 72.3 ms (Original response time of 425.0–352.7 ms for BCI-decoder). The BCI-decoder model of the best subject was shown to generalize for both performance and time savings to the other subjects. The results of our off-line open loop simulation demonstrate that BCI based neuroadaptive automation has the potential to decode motor intention faster than manual control in response to a hazardous perturbation in flight attitude while ignoring ongoing motor and visual induced activity

Quantitation of heavy ion damage to the mammalian brain - Some preliminary findings

NASA Technical Reports Server (NTRS)

Cox, A. B.; Kraft, L. M.

1984-01-01

For several years, studies have been conducted regarding late effects of particulate radiations in mammalian tissues, taking into account the brains of rodents and lagomorphs. Recently, it has become feasible to quantify pathological damage and morpho-physiologic alterations accurately in large numbers of histological specimens. New investigative procedures make use of computer-assisted automated image analysis systems. Details regarding the employed methodology are discussed along with the results of the information. The radiations of high linear energy transfer (LET) cause apparently earlier and more dramatic shrinkage of olfactory glomeruli in exposed rabbit brains than comparable doses of Co-60 gamma photons.

Asleep at the automated wheel-Sleepiness and fatigue during highly automated driving.

PubMed

Vogelpohl, Tobias; Kühn, Matthias; Hummel, Thomas; Vollrath, Mark

2018-03-20

Due to the lack of active involvement in the driving situation and due to monotonous driving environments drivers with automation may be prone to become fatigued faster than manual drivers (e.g. Schömig et al., 2015). However, little is known about the progression of fatigue during automated driving and its effects on the ability to take back manual control after a take-over request. In this driving simulator study with Nö=ö60 drivers we used a three factorial 2ö×ö2ö×ö12 mixed design to analyze the progression (12ö×ö5ömin; within subjects) of driver fatigue in drivers with automation compared to manual drivers (between subjects). Driver fatigue was induced as either mainly sleep related or mainly task related fatigue (between subjects). Additionally, we investigated the drivers' reactions to a take-over request in a critical driving scenario to gain insights into the ability of fatigued drivers to regain manual control and situation awareness after automated driving. Drivers in the automated driving condition exhibited facial indicators of fatigue after 15 to 35ömin of driving. Manual drivers only showed similar indicators of fatigue if they suffered from a lack of sleep and then only after a longer period of driving (approx. 40ömin). Several drivers in the automated condition closed their eyes for extended periods of time. In the driving with automation condition mean automation deactivation times after a take-over request were slower for a certain percentage (about 30%) of the drivers with a lack of sleep (Mö=ö3.2; SDö=ö2.1ös) compared to the reaction times after a long drive (Mö=ö2.4; SDö=ö0.9ös). Drivers with automation also took longer than manual drivers to first glance at the speed display after a take-over request and were more likely to stay behind a braking lead vehicle instead of overtaking it. Drivers are unable to stay alert during extended periods of automated driving without non-driving related tasks. Fatigued drivers could

A high-throughput semi-automated preparation for filtered synaptoneurosomes.

PubMed

Murphy, Kathryn M; Balsor, Justin; Beshara, Simon; Siu, Caitlin; Pinto, Joshua G A

2014-09-30

Synaptoneurosomes have become an important tool for studying synaptic proteins. The filtered synaptoneurosomes preparation originally developed by Hollingsworth et al. (1985) is widely used and is an easy method to prepare synaptoneurosomes. The hand processing steps in that preparation, however, are labor intensive and have become a bottleneck for current proteomic studies using synaptoneurosomes. For this reason, we developed new steps for tissue homogenization and filtration that transform the preparation of synaptoneurosomes to a high-throughput, semi-automated process. We implemented a standardized protocol with easy to follow steps for homogenizing multiple samples simultaneously using a FastPrep tissue homogenizer (MP Biomedicals, LLC) and then filtering all of the samples in centrifugal filter units (EMD Millipore, Corp). The new steps dramatically reduce the time to prepare synaptoneurosomes from hours to minutes, increase sample recovery, and nearly double enrichment for synaptic proteins. These steps are also compatible with biosafety requirements for working with pathogen infected brain tissue. The new high-throughput semi-automated steps to prepare synaptoneurosomes are timely technical advances for studies of low abundance synaptic proteins in valuable tissue samples. Copyright © 2014 Elsevier B.V. All rights reserved.

Elements of EAF automation processes

NASA Astrophysics Data System (ADS)

Ioana, A.; Constantin, N.; Dragna, E. C.

2017-01-01

Our article presents elements of Electric Arc Furnace (EAF) automation. So, we present and analyze detailed two automation schemes: the scheme of electrical EAF automation system; the scheme of thermic EAF automation system. The application results of these scheme of automation consists in: the sensitive reduction of specific consummation of electrical energy of Electric Arc Furnace, increasing the productivity of Electric Arc Furnace, increase the quality of the developed steel, increasing the durability of the building elements of Electric Arc Furnace.

Automated segmentation of the actively stained mouse brain using multi-spectral MR microscopy.

PubMed

Sharief, Anjum A; Badea, Alexandra; Dale, Anders M; Johnson, G Allan

2008-01-01

Magnetic resonance microscopy (MRM) has created new approaches for high-throughput morphological phenotyping of mouse models of diseases. Transgenic and knockout mice serve as a test bed for validating hypotheses that link genotype to the phenotype of diseases, as well as developing and tracking treatments. We describe here a Markov random fields based segmentation of the actively stained mouse brain, as a prerequisite for morphological phenotyping. Active staining achieves higher signal to noise ratio (SNR) thereby enabling higher resolution imaging per unit time than obtained in previous formalin-fixed mouse brain studies. The segmentation algorithm was trained on isotropic 43-mum T1- and T2-weighted MRM images. The mouse brain was segmented into 33 structures, including the hippocampus, amygdala, hypothalamus, thalamus, as well as fiber tracts and ventricles. Probabilistic information used in the segmentation consisted of (a) intensity distributions in the T1- and T2-weighted data, (b) location, and (c) contextual priors for incorporating spatial information. Validation using standard morphometric indices showed excellent consistency between automatically and manually segmented data. The algorithm has been tested on the widely used C57BL/6J strain, as well as on a selection of six recombinant inbred BXD strains, chosen especially for their largely variant hippocampus.

77 FR 48527 - National Customs Automation Program (NCAP) Test Concerning Automated Commercial Environment (ACE...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-14

... Program (NCAP) Test Concerning Automated Commercial Environment (ACE) Simplified Entry: Modification of... Automated Commercial Environment (ACE). The test's participant selection criteria are modified to reflect... (NCAP) test concerning Automated Commercial Environment (ACE) Simplified Entry functionality (Simplified...

Segmentation of Brain Lesions in MRI and CT Scan Images: A Hybrid Approach Using k-Means Clustering and Image Morphology

NASA Astrophysics Data System (ADS)

Agrawal, Ritu; Sharma, Manisha; Singh, Bikesh Kumar

2018-04-01

Manual segmentation and analysis of lesions in medical images is time consuming and subjected to human errors. Automated segmentation has thus gained significant attention in recent years. This article presents a hybrid approach for brain lesion segmentation in different imaging modalities by combining median filter, k means clustering, Sobel edge detection and morphological operations. Median filter is an essential pre-processing step and is used to remove impulsive noise from the acquired brain images followed by k-means segmentation, Sobel edge detection and morphological processing. The performance of proposed automated system is tested on standard datasets using performance measures such as segmentation accuracy and execution time. The proposed method achieves a high accuracy of 94% when compared with manual delineation performed by an expert radiologist. Furthermore, the statistical significance test between lesion segmented using automated approach and that by expert delineation using ANOVA and correlation coefficient achieved high significance values of 0.986 and 1 respectively. The experimental results obtained are discussed in lieu of some recently reported studies.

Comparing three-dimensional serial optical coherence tomography histology to MRI imaging in the entire mouse brain

NASA Astrophysics Data System (ADS)

Castonguay, Alexandre; Lefebvre, Joël; Pouliot, Philippe; Lesage, Frédéric

2018-01-01

An automated serial histology setup combining optical coherence tomography (OCT) imaging with vibratome sectioning was used to image eight wild type mouse brains. The datasets resulted in thousands of volumetric tiles resolved at a voxel size of (4.9×4.9×6.5) μm3 stitched back together to give a three-dimensional map of the brain from which a template OCT brain was obtained. To assess deformation caused by tissue sectioning, reconstruction algorithms, and fixation, OCT datasets were compared to both in vivo and ex vivo magnetic resonance imaging (MRI) imaging. The OCT brain template yielded a highly detailed map of the brain structure, with a high contrast in white matter fiber bundles and was highly resemblant to the in vivo MRI template. Brain labeling using the Allen brain framework showed little variation in regional brain volume among imaging modalities with no statistical differences. The high correspondence between the OCT template brain and its in vivo counterpart demonstrates the potential of whole brain histology to validate in vivo imaging.

Coadaptive aiding and automation enhance operator performance.

PubMed

Christensen, James C; Estepp, Justin R

2013-10-01

In this work, we expand on the theory of adaptive aiding by measuring the effectiveness of coadaptive aiding, wherein we explicitly allow for both system and user to adapt to each other. Adaptive aiding driven by psychophysiological monitoring has been demonstrated to be a highly effective means of controlling task allocation and system functioning. Psychophysiological monitoring is uniquely well suited for coadaptation, as malleable brain activity may be used as a continuous input to the adaptive system. To establish the efficacy of the coadaptive system, physiological activation of adaptation was directly compared with manual activation or no activation of the same automation and cuing systems. We used interface adaptations and automation that are plausible for real-world operations, presented in the context of a multi-remotely piloted aircraft control simulation. Each participant completed 3 days of testing during 1 week. Performance was assessed via proportion of targets successfully engaged. In the first 2 days of testing, there were no significant differences in performance between the conditions. However, in the third session, physiological adaptation produced the highest performance. By extending the data collection across multiple days, we offered enough time and repeated experience for user adaptation as well as online system adaptation, hence demonstrating coadaptive aiding. The results of this work may be employed to implement more effective adaptive workstations in a variety of work domains.

Automation in College Libraries.

ERIC Educational Resources Information Center

Werking, Richard Hume

1991-01-01

Reports the results of a survey of the "Bowdoin List" group of liberal arts colleges. The survey obtained information about (1) automation modules in place and when they had been installed; (2) financing of automation and its impacts on the library budgets; and (3) library director's views on library automation and the nature of the…

Automation: Decision Aid or Decision Maker?

NASA Technical Reports Server (NTRS)

Skitka, Linda J.

1998-01-01

This study clarified that automation bias is something unique to automated decision making contexts, and is not the result of a general tendency toward complacency. By comparing performance on exactly the same events on the same tasks with and without an automated decision aid, we were able to determine that at least the omission error part of automation bias is due to the unique context created by having an automated decision aid, and is not a phenomena that would occur even if people were not in an automated context. However, this study also revealed that having an automated decision aid did lead to modestly improved performance across all non-error events. Participants in the non- automated condition responded with 83.68% accuracy, whereas participants in the automated condition responded with 88.67% accuracy, across all events. Automated decision aids clearly led to better overall performance when they were accurate. People performed almost exactly at the level of reliability as the automation (which across events was 88% reliable). However, also clear, is that the presence of less than 100% accurate automated decision aids creates a context in which new kinds of errors in decision making can occur. Participants in the non-automated condition responded with 97% accuracy on the six "error" events, whereas participants in the automated condition had only a 65% accuracy rate when confronted with those same six events. In short, the presence of an AMA can lead to vigilance decrements that can lead to errors in decision making.

The standard-based open workflow system in GeoBrain (Invited)

NASA Astrophysics Data System (ADS)

Di, L.; Yu, G.; Zhao, P.; Deng, M.

2013-12-01

GeoBrain is an Earth science Web-service system developed and operated by the Center for Spatial Information Science and Systems, George Mason University. In GeoBrain, a standard-based open workflow system has been implemented to accommodate the automated processing of geospatial data through a set of complex geo-processing functions for advanced production generation. The GeoBrain models the complex geoprocessing at two levels, the conceptual and concrete. At the conceptual level, the workflows exist in the form of data and service types defined by ontologies. The workflows at conceptual level are called geo-processing models and cataloged in GeoBrain as virtual product types. A conceptual workflow is instantiated into a concrete, executable workflow when a user requests a product that matches a virtual product type. Both conceptual and concrete workflows are encoded in Business Process Execution Language (BPEL). A BPEL workflow engine, called BPELPower, has been implemented to execute the workflow for the product generation. A provenance capturing service has been implemented to generate the ISO 19115-compliant complete product provenance metadata before and after the workflow execution. The generation of provenance metadata before the workflow execution allows users to examine the usability of the final product before the lengthy and expensive execution takes place. The three modes of workflow executions defined in the ISO 19119, transparent, translucent, and opaque, are available in GeoBrain. A geoprocessing modeling portal has been developed to allow domain experts to develop geoprocessing models at the type level with the support of both data and service/processing ontologies. The geoprocessing models capture the knowledge of the domain experts and are become the operational offering of the products after a proper peer review of models is conducted. An automated workflow composition has been experimented successfully based on ontologies and artificial

Structural Brain Atlases: Design, Rationale, and Applications in Normal and Pathological Cohorts

PubMed Central

Mandal, Pravat K.; Mahajan, Rashima; Dinov, Ivo D.

2015-01-01

Structural magnetic resonance imaging (MRI) provides anatomical information about the brain in healthy as well as in diseased conditions. On the other hand, functional MRI (fMRI) provides information on the brain activity during performance of a specific task. Analysis of fMRI data requires the registration of the data to a reference brain template in order to identify the activated brain regions. Brain templates also find application in other neuroimaging modalities, such as diffusion tensor imaging and multi-voxel spectroscopy. Further, there are certain differences (e.g., brain shape and size) in the brains of populations of different origin and during diseased conditions like in Alzheimer’s disease (AD), population and disease-specific brain templates may be considered crucial for accurate registration and subsequent analysis of fMRI as well as other neuroimaging data. This manuscript provides a comprehensive review of the history, construction and application of brain atlases. A chronological outline of the development of brain template design, starting from the Talairach and Tournoux atlas to the Chinese brain template (to date), along with their respective detailed construction protocols provides the backdrop to this manuscript. The manuscript also provides the automated workflow-based protocol for designing a population-specific brain atlas from structural MRI data using LONI Pipeline graphical workflow environment. We conclude by discussing the scope of brain templates as a research tool and their application in various neuroimaging modalities. PMID:22647262

Voxel-based morphometry and automated lobar volumetry: The trade-off between spatial scale and statistical correction

PubMed Central

Voormolen, Eduard H.J.; Wei, Corie; Chow, Eva W.C.; Bassett, Anne S.; Mikulis, David J.; Crawley, Adrian P.

2011-01-01

Voxel-based morphometry (VBM) and automated lobar region of interest (ROI) volumetry are comprehensive and fast methods to detect differences in overall brain anatomy on magnetic resonance images. However, VBM and automated lobar ROI volumetry have detected dissimilar gray matter differences within identical image sets in our own experience and in previous reports. To gain more insight into how diverging results arise and to attempt to establish whether one method is superior to the other, we investigated how differences in spatial scale and in the need to statistically correct for multiple spatial comparisons influence the relative sensitivity of either technique to group differences in gray matter volumes. We assessed the performance of both techniques on a small dataset containing simulated gray matter deficits and additionally on a dataset of 22q11-deletion syndrome patients with schizophrenia (22q11DS-SZ) vs. matched controls. VBM was more sensitive to simulated focal deficits compared to automated ROI volumetry, and could detect global cortical deficits equally well. Moreover, theoretical calculations of VBM and ROI detection sensitivities to focal deficits showed that at increasing ROI size, ROI volumetry suffers more from loss in sensitivity than VBM. Furthermore, VBM and automated ROI found corresponding GM deficits in 22q11DS-SZ patients, except in the parietal lobe. Here, automated lobar ROI volumetry found a significant deficit only after a smaller subregion of interest was employed. Thus, sensitivity to focal differences is impaired relatively more by averaging over larger volumes in automated ROI methods than by the correction for multiple comparisons in VBM. These findings indicate that VBM is to be preferred over automated lobar-scale ROI volumetry for assessing gray matter volume differences between groups. PMID:19619660

Laboratory Automation and Middleware.

PubMed

Riben, Michael

2015-06-01

The practice of surgical pathology is under constant pressure to deliver the highest quality of service, reduce errors, increase throughput, and decrease turnaround time while at the same time dealing with an aging workforce, increasing financial constraints, and economic uncertainty. Although not able to implement total laboratory automation, great progress continues to be made in workstation automation in all areas of the pathology laboratory. This report highlights the benefits and challenges of pathology automation, reviews middleware and its use to facilitate automation, and reviews the progress so far in the anatomic pathology laboratory. Copyright © 2015 Elsevier Inc. All rights reserved.

Managing laboratory automation

PubMed Central

Saboe, Thomas J.

1995-01-01

This paper discusses the process of managing automated systems through their life cycles within the quality-control (QC) laboratory environment. The focus is on the process of directing and managing the evolving automation of a laboratory; system examples are given. The author shows how both task and data systems have evolved, and how they interrelate. A BIG picture, or continuum view, is presented and some of the reasons for success or failure of the various examples cited are explored. Finally, some comments on future automation need are discussed. PMID:18925018

Managing laboratory automation.

PubMed

Saboe, T J

1995-01-01

This paper discusses the process of managing automated systems through their life cycles within the quality-control (QC) laboratory environment. The focus is on the process of directing and managing the evolving automation of a laboratory; system examples are given. The author shows how both task and data systems have evolved, and how they interrelate. A BIG picture, or continuum view, is presented and some of the reasons for success or failure of the various examples cited are explored. Finally, some comments on future automation need are discussed.

Hedgehogs and foxes (and a bear)

NASA Astrophysics Data System (ADS)

Gibb, Bruce

2017-02-01

The chemical universe is big. Really big. You just won't believe how vastly, hugely, mind-bogglingly big it is. Bruce Gibb reminds us that it's somewhat messy too, and so we succeed by recognizing the limits of our knowledge.

The Science of Home Automation

NASA Astrophysics Data System (ADS)

Thomas, Brian Louis

Smart home technologies and the concept of home automation have become more popular in recent years. This popularity has been accompanied by social acceptance of passive sensors installed throughout the home. The subsequent increase in smart homes facilitates the creation of home automation strategies. We believe that home automation strategies can be generated intelligently by utilizing smart home sensors and activity learning. In this dissertation, we hypothesize that home automation can benefit from activity awareness. To test this, we develop our activity-aware smart automation system, CARL (CASAS Activity-aware Resource Learning). CARL learns the associations between activities and device usage from historical data and utilizes the activity-aware capabilities to control the devices. To help validate CARL we deploy and test three different versions of the automation system in a real-world smart environment. To provide a foundation of activity learning, we integrate existing activity recognition and activity forecasting into CARL home automation. We also explore two alternatives to using human-labeled data to train the activity learning models. The first unsupervised method is Activity Detection, and the second is a modified DBSCAN algorithm that utilizes Dynamic Time Warping (DTW) as a distance metric. We compare the performance of activity learning with human-defined labels and with automatically-discovered activity categories. To provide evidence in support of our hypothesis, we evaluate CARL automation in a smart home testbed. Our results indicate that home automation can be boosted through activity awareness. We also find that the resulting automation has a high degree of usability and comfort for the smart home resident.

A Natural Language Processing-based Model to Automate MRI Brain Protocol Selection and Prioritization.

PubMed

Brown, Andrew D; Marotta, Thomas R

2017-02-01

Incorrect imaging protocol selection can contribute to increased healthcare cost and waste. To help healthcare providers improve the quality and safety of medical imaging services, we developed and evaluated three natural language processing (NLP) models to determine whether NLP techniques could be employed to aid in clinical decision support for protocoling and prioritization of magnetic resonance imaging (MRI) brain examinations. To test the feasibility of using an NLP model to support clinical decision making for MRI brain examinations, we designed three different medical imaging prediction tasks, each with a unique outcome: selecting an examination protocol, evaluating the need for contrast administration, and determining priority. We created three models for each prediction task, each using a different classification algorithm-random forest, support vector machine, or k-nearest neighbor-to predict outcomes based on the narrative clinical indications and demographic data associated with 13,982 MRI brain examinations performed from January 1, 2013 to June 30, 2015. Test datasets were used to calculate the accuracy, sensitivity and specificity, predictive values, and the area under the curve. Our optimal results show an accuracy of 82.9%, 83.0%, and 88.2% for the protocol selection, contrast administration, and prioritization tasks, respectively, demonstrating that predictive algorithms can be used to aid in clinical decision support for examination protocoling. NLP models developed from the narrative clinical information provided by referring clinicians and demographic data are feasible methods to predict the protocol and priority of MRI brain examinations. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Automation in organizations: Eternal conflict

NASA Technical Reports Server (NTRS)

Dieterly, D. L.

1981-01-01

Some ideas on and insights into the problems associated with automation in organizations are presented with emphasis on the concept of automation, its relationship to the individual, and its impact on system performance. An analogy is drawn, based on an American folk hero, to emphasize the extent of the problems encountered when dealing with automation within an organization. A model is proposed to focus attention on a set of appropriate dimensions. The function allocation process becomes a prominent aspect of the model. The current state of automation research is mentioned in relation to the ideas introduced. Proposed directions for an improved understanding of automation's effect on the individual's efficiency are discussed. The importance of understanding the individual's perception of the system in terms of the degree of automation is highlighted.

Large-scale automated image analysis for computational profiling of brain tissue surrounding implanted neuroprosthetic devices using Python.

PubMed

Rey-Villamizar, Nicolas; Somasundar, Vinay; Megjhani, Murad; Xu, Yan; Lu, Yanbin; Padmanabhan, Raghav; Trett, Kristen; Shain, William; Roysam, Badri

2014-01-01

In this article, we describe the use of Python for large-scale automated server-based bio-image analysis in FARSIGHT, a free and open-source toolkit of image analysis methods for quantitative studies of complex and dynamic tissue microenvironments imaged by modern optical microscopes, including confocal, multi-spectral, multi-photon, and time-lapse systems. The core FARSIGHT modules for image segmentation, feature extraction, tracking, and machine learning are written in C++, leveraging widely used libraries including ITK, VTK, Boost, and Qt. For solving complex image analysis tasks, these modules must be combined into scripts using Python. As a concrete example, we consider the problem of analyzing 3-D multi-spectral images of brain tissue surrounding implanted neuroprosthetic devices, acquired using high-throughput multi-spectral spinning disk step-and-repeat confocal microscopy. The resulting images typically contain 5 fluorescent channels. Each channel consists of 6000 × 10,000 × 500 voxels with 16 bits/voxel, implying image sizes exceeding 250 GB. These images must be mosaicked, pre-processed to overcome imaging artifacts, and segmented to enable cellular-scale feature extraction. The features are used to identify cell types, and perform large-scale analysis for identifying spatial distributions of specific cell types relative to the device. Python was used to build a server-based script (Dell 910 PowerEdge servers with 4 sockets/server with 10 cores each, 2 threads per core and 1TB of RAM running on Red Hat Enterprise Linux linked to a RAID 5 SAN) capable of routinely handling image datasets at this scale and performing all these processing steps in a collaborative multi-user multi-platform environment. Our Python script enables efficient data storage and movement between computers and storage servers, logs all the processing steps, and performs full multi-threaded execution of all codes, including open and closed-source third party libraries.

Experience of automation failures in training: effects on trust, automation bias, complacency and performance.

PubMed

Sauer, Juergen; Chavaillaz, Alain; Wastell, David

2016-06-01

This work examined the effects of operators' exposure to various types of automation failures in training. Forty-five participants were trained for 3.5 h on a simulated process control environment. During training, participants either experienced a fully reliable, automatic fault repair facility (i.e. faults detected and correctly diagnosed), a misdiagnosis-prone one (i.e. faults detected but not correctly diagnosed) or a miss-prone one (i.e. faults not detected). One week after training, participants were tested for 3 h, experiencing two types of automation failures (misdiagnosis, miss). The results showed that automation bias was very high when operators trained on miss-prone automation encountered a failure of the diagnostic system. Operator errors resulting from automation bias were much higher when automation misdiagnosed a fault than when it missed one. Differences in trust levels that were instilled by the different training experiences disappeared during the testing session. Practitioner Summary: The experience of automation failures during training has some consequences. A greater potential for operator errors may be expected when an automatic system failed to diagnose a fault than when it failed to detect one.

Neuroanatomical phenotyping of the mouse brain with three-dimensional autofluorescence imaging

PubMed Central

Wong, Michael D.; Dazai, Jun; Altaf, Maliha; Mark Henkelman, R.; Lerch, Jason P.; Nieman, Brian J.

2012-01-01

The structural organization of the brain is important for normal brain function and is critical to understand in order to evaluate changes that occur during disease processes. Three-dimensional (3D) imaging of the mouse brain is necessary to appreciate the spatial context of structures within the brain. In addition, the small scale of many brain structures necessitates resolution at the ∼10 μm scale. 3D optical imaging techniques, such as optical projection tomography (OPT), have the ability to image intact large specimens (1 cm3) with ∼5 μm resolution. In this work we assessed the potential of autofluorescence optical imaging methods, and specifically OPT, for phenotyping the mouse brain. We found that both specimen size and fixation methods affected the quality of the OPT image. Based on these findings we developed a specimen preparation method to improve the images. Using this method we assessed the potential of optical imaging for phenotyping. Phenotypic differences between wild-type male and female mice were quantified using computer-automated methods. We found that optical imaging of the endogenous autofluorescence in the mouse brain allows for 3D characterization of neuroanatomy and detailed analysis of brain phenotypes. This will be a powerful tool for understanding mouse models of disease and development and is a technology that fits easily within the workflow of biology and neuroscience labs. PMID:22718750

Automated Engineering Design (AED); An approach to automated documentation

NASA Technical Reports Server (NTRS)

Mcclure, C. W.

1970-01-01

The automated engineering design (AED) is reviewed, consisting of a high level systems programming language, a series of modular precoded subroutines, and a set of powerful software machine tools that effectively automate the production and design of new languages. AED is used primarily for development of problem and user-oriented languages. Software production phases are diagramed, and factors which inhibit effective documentation are evaluated.

The Automated Office.

ERIC Educational Resources Information Center

Naclerio, Nick

1979-01-01

Clerical personnel may be able to climb career ladders as a result of office automation and expanded job opportunities in the word processing area. Suggests opportunities in an automated office system and lists books and periodicals on word processing for counselors and teachers. (MF)

Sub-Network Kernels for Measuring Similarity of Brain Connectivity Networks in Disease Diagnosis.

PubMed

Jie, Biao; Liu, Mingxia; Zhang, Daoqiang; Shen, Dinggang

2018-05-01

As a simple representation of interactions among distributed brain regions, brain networks have been widely applied to automated diagnosis of brain diseases, such as Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment (MCI). In brain network analysis, a challenging task is how to measure the similarity between a pair of networks. Although many graph kernels (i.e., kernels defined on graphs) have been proposed for measuring the topological similarity of a pair of brain networks, most of them are defined using general graphs, thus ignoring the uniqueness of each node in brain networks. That is, each node in a brain network denotes a particular brain region, which is a specific characteristics of brain networks. Accordingly, in this paper, we construct a novel sub-network kernel for measuring the similarity between a pair of brain networks and then apply it to brain disease classification. Different from current graph kernels, our proposed sub-network kernel not only takes into account the inherent characteristic of brain networks, but also captures multi-level (from local to global) topological properties of nodes in brain networks, which are essential for defining the similarity measure of brain networks. To validate the efficacy of our method, we perform extensive experiments on subjects with baseline functional magnetic resonance imaging data obtained from the Alzheimer's disease neuroimaging initiative database. Experimental results demonstrate that the proposed method outperforms several state-of-the-art graph-based methods in MCI classification.

An automated detection for axonal boutons in vivo two-photon imaging of mouse

NASA Astrophysics Data System (ADS)

Li, Weifu; Zhang, Dandan; Xie, Qiwei; Chen, Xi; Han, Hua

2017-02-01

Activity-dependent changes in the synaptic connections of the brain are tightly related to learning and memory. Previous studies have shown that essentially all new synaptic contacts were made by adding new partners to existing synaptic elements. To further explore synaptic dynamics in specific pathways, concurrent imaging of pre and postsynaptic structures in identified connections is required. Consequently, considerable attention has been paid for the automated detection of axonal boutons. Different from most previous methods proposed in vitro data, this paper considers a more practical case in vivo neuron images which can provide real time information and direct observation of the dynamics of a disease process in mouse. Additionally, we present an automated approach for detecting axonal boutons by starting with deconvolving the original images, then thresholding the enhanced images, and reserving the regions fulfilling a series of criteria. Experimental result in vivo two-photon imaging of mouse demonstrates the effectiveness of our proposed method.

Systematic review automation technologies

PubMed Central

2014-01-01

Systematic reviews, a cornerstone of evidence-based medicine, are not produced quickly enough to support clinical practice. The cost of production, availability of the requisite expertise and timeliness are often quoted as major contributors for the delay. This detailed survey of the state of the art of information systems designed to support or automate individual tasks in the systematic review, and in particular systematic reviews of randomized controlled clinical trials, reveals trends that see the convergence of several parallel research projects. We surveyed literature describing informatics systems that support or automate the processes of systematic review or each of the tasks of the systematic review. Several projects focus on automating, simplifying and/or streamlining specific tasks of the systematic review. Some tasks are already fully automated while others are still largely manual. In this review, we describe each task and the effect that its automation would have on the entire systematic review process, summarize the existing information system support for each task, and highlight where further research is needed for realizing automation for the task. Integration of the systems that automate systematic review tasks may lead to a revised systematic review workflow. We envisage the optimized workflow will lead to system in which each systematic review is described as a computer program that automatically retrieves relevant trials, appraises them, extracts and synthesizes data, evaluates the risk of bias, performs meta-analysis calculations, and produces a report in real time. PMID:25005128

Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general.

PubMed

Zander, Thorsten O; Kothe, Christian

2011-04-01

Cognitive monitoring is an approach utilizing realtime brain signal decoding (RBSD) for gaining information on the ongoing cognitive user state. In recent decades this approach has brought valuable insight into the cognition of an interacting human. Automated RBSD can be used to set up a brain-computer interface (BCI) providing a novel input modality for technical systems solely based on brain activity. In BCIs the user usually sends voluntary and directed commands to control the connected computer system or to communicate through it. In this paper we propose an extension of this approach by fusing BCI technology with cognitive monitoring, providing valuable information about the users' intentions, situational interpretations and emotional states to the technical system. We call this approach passive BCI. In the following we give an overview of studies which utilize passive BCI, as well as other novel types of applications resulting from BCI technology. We especially focus on applications for healthy users, and the specific requirements and demands of this user group. Since the presented approach of combining cognitive monitoring with BCI technology is very similar to the concept of BCIs itself we propose a unifying categorization of BCI-based applications, including the novel approach of passive BCI.

Understanding human management of automation errors.

PubMed

McBride, Sara E; Rogers, Wendy A; Fisk, Arthur D

2014-01-01

Automation has the potential to aid humans with a diverse set of tasks and support overall system performance. Automated systems are not always reliable, and when automation errs, humans must engage in error management, which is the process of detecting, understanding, and correcting errors. However, this process of error management in the context of human-automation interaction is not well understood. Therefore, we conducted a systematic review of the variables that contribute to error management. We examined relevant research in human-automation interaction and human error to identify critical automation, person, task, and emergent variables. We propose a framework for management of automation errors to incorporate and build upon previous models. Further, our analysis highlights variables that may be addressed through design and training to positively influence error management. Additional efforts to understand the error management process will contribute to automation designed and implemented to support safe and effective system performance.

Identifying relevant biomarkers of brain injury from structural MRI: Validation using automated approaches in children with unilateral cerebral palsy

PubMed Central

Dowson, Nicholas; Doecke, James; Fiori, Simona; Bradley, Andrew P.; Boyd, Roslyn N.; Rose, Stephen

2017-01-01

Previous studies have proposed that the early elucidation of brain injury from structural Magnetic Resonance Images (sMRI) is critical for the clinical assessment of children with cerebral palsy (CP). Although distinct aetiologies, including cortical maldevelopments, white and grey matter lesions and ventricular enlargement, have been categorised, these injuries are commonly only assessed in a qualitative fashion. As a result, sMRI remains relatively underexploited for clinical assessments, despite its widespread use. In this study, several automated and validated techniques to automatically quantify these three classes of injury were generated in a large cohort of children (n = 139) aged 5–17, including 95 children diagnosed with unilateral CP. Using a feature selection approach on a training data set (n = 97) to find severity of injury biomarkers predictive of clinical function (motor, cognitive, communicative and visual function), cortical shape and regional lesion burden were most often chosen associated with clinical function. Validating the best models on the unseen test data (n = 42), correlation values ranged between 0.545 and 0.795 (p<0.008), indicating significant associations with clinical function. The measured prevalence of injury, including ventricular enlargement (70%), white and grey matter lesions (55%) and cortical malformations (30%), were similar to the prevalence observed in other cohorts of children with unilateral CP. These findings support the early characterisation of injury from sMRI into previously defined aetiologies as part of standard clinical assessment. Furthermore, the strong and significant association between quantifications of injury observed on structural MRI and multiple clinical scores accord with empirically established structure-function relationships. PMID:28763455

The Automation-by-Expertise-by-Training Interaction.

PubMed

Strauch, Barry

2017-03-01

I introduce the automation-by-expertise-by-training interaction in automated systems and discuss its influence on operator performance. Transportation accidents that, across a 30-year interval demonstrated identical automation-related operator errors, suggest a need to reexamine traditional views of automation. I review accident investigation reports, regulator studies, and literature on human computer interaction, expertise, and training and discuss how failing to attend to the interaction of automation, expertise level, and training has enabled operators to commit identical automation-related errors. Automated systems continue to provide capabilities exceeding operators' need for effective system operation and provide interfaces that can hinder, rather than enhance, operator automation-related situation awareness. Because of limitations in time and resources, training programs do not provide operators the expertise needed to effectively operate these automated systems, requiring them to obtain the expertise ad hoc during system operations. As a result, many do not acquire necessary automation-related system expertise. Integrating automation with expected operator expertise levels, and within training programs that provide operators the necessary automation expertise, can reduce opportunities for automation-related operator errors. Research to address the automation-by-expertise-by-training interaction is needed. However, such research must meet challenges inherent to examining realistic sociotechnical system automation features with representative samples of operators, perhaps by using observational and ethnographic research. Research in this domain should improve the integration of design and training and, it is hoped, enhance operator performance.

Automated Microwave Dielectric Constant Measurement

DTIC Science & Technology

1987-03-01

IJSWC TR 86-46 AD.-A 184 182 AUTOMATED MICROWAVE DIELECTRIC CONSTANT MEASUREMENT SYTIEM BY B. C. GLANCY A. KRALL PESEARCH AND TECHNOLOGY DEPARTMENT...NO0. NO. ACCESSION NO. Silver Spring, Maryland 20903-500061152N ZROO1 ZRO131 R1AA29 11. TITLE (Include Security Classification) AUTOMATED MICROWAVE ...constants as a funct on of microwave frequency has been simplified using an automated testing apparatus. This automated procedure is based on the use of a

Repairing the brain with physical exercise: Cortical thickness and brain volume increases in long-term pediatric brain tumor survivors in response to a structured exercise intervention.

PubMed

Szulc-Lerch, Kamila U; Timmons, Brian W; Bouffet, Eric; Laughlin, Suzanne; de Medeiros, Cynthia B; Skocic, Jovanka; Lerch, Jason P; Mabbott, Donald J

2018-01-01

There is growing evidence that exercise induced experience dependent plasticity may foster structural and functional recovery following brain injury. We examined the efficacy of exercise training for neural and cognitive recovery in long-term pediatric brain tumor survivors treated with radiation. We conducted a controlled clinical trial with crossover of exercise training (vs. no training) in a volunteer sample of 28 children treated with cranial radiation for brain tumors (mean age = 11.5 yrs.; mean time since diagnosis = 5.7 yrs). The endpoints were anatomical T1 MRI data and multiple behavioral outcomes presenting a broader analysis of structural MRI data across the entire brain. This included an analysis of changes in cortical thickness and brain volume using automated, user unbiased approaches. A series of general linear mixed effects models evaluating the effects of exercise training on cortical thickness were performed in a voxel and vertex-wise manner, as well as for specific regions of interest. In exploratory analyses, we evaluated the relationship between changes in cortical thickness after exercise with multiple behavioral outcomes, as well as the relation of these measures at baseline. Exercise was associated with increases in cortical thickness within the right pre and postcentral gyri. Other notable areas of increased thickness related to training were present in the left pre and postcentral gyri, left temporal pole, left superior temporal gyrus, and left parahippocampal gyrus. Further, we observed that compared to a separate cohort of healthy children, participants displayed multiple areas with a significantly thinner cortex prior to training and fewer differences following training, indicating amelioration of anatomical deficits. Partial least squares analysis (PLS) revealed specific patterns of relations between cortical thickness and various behavioral outcomes both after training and at baseline. Overall, our results indicate that

Brain tumor image segmentation using kernel dictionary learning.

PubMed

Jeon Lee; Seung-Jun Kim; Rong Chen; Herskovits, Edward H

2015-08-01

Automated brain tumor image segmentation with high accuracy and reproducibility holds a big potential to enhance the current clinical practice. Dictionary learning (DL) techniques have been applied successfully to various image processing tasks recently. In this work, kernel extensions of the DL approach are adopted. Both reconstructive and discriminative versions of the kernel DL technique are considered, which can efficiently incorporate multi-modal nonlinear feature mappings based on the kernel trick. Our novel discriminative kernel DL formulation allows joint learning of a task-driven kernel-based dictionary and a linear classifier using a K-SVD-type algorithm. The proposed approaches were tested using real brain magnetic resonance (MR) images of patients with high-grade glioma. The obtained preliminary performances are competitive with the state of the art. The discriminative kernel DL approach is seen to reduce computational burden without much sacrifice in performance.

Understanding human management of automation errors

PubMed Central

McBride, Sara E.; Rogers, Wendy A.; Fisk, Arthur D.

2013-01-01

Automation has the potential to aid humans with a diverse set of tasks and support overall system performance. Automated systems are not always reliable, and when automation errs, humans must engage in error management, which is the process of detecting, understanding, and correcting errors. However, this process of error management in the context of human-automation interaction is not well understood. Therefore, we conducted a systematic review of the variables that contribute to error management. We examined relevant research in human-automation interaction and human error to identify critical automation, person, task, and emergent variables. We propose a framework for management of automation errors to incorporate and build upon previous models. Further, our analysis highlights variables that may be addressed through design and training to positively influence error management. Additional efforts to understand the error management process will contribute to automation designed and implemented to support safe and effective system performance. PMID:25383042

Cockpit Adaptive Automation and Pilot Performance

NASA Technical Reports Server (NTRS)

Parasuraman, Raja

2001-01-01

The introduction of high-level automated systems in the aircraft cockpit has provided several benefits, e.g., new capabilities, enhanced operational efficiency, and reduced crew workload. At the same time, conventional 'static' automation has sometimes degraded human operator monitoring performance, increased workload, and reduced situation awareness. Adaptive automation represents an alternative to static automation. In this approach, task allocation between human operators and computer systems is flexible and context-dependent rather than static. Adaptive automation, or adaptive task allocation, is thought to provide for regulation of operator workload and performance, while preserving the benefits of static automation. In previous research we have reported beneficial effects of adaptive automation on the performance of both pilots and non-pilots of flight-related tasks. For adaptive systems to be viable, however, such benefits need to be examined jointly in the context of a single set of tasks. The studies carried out under this project evaluated a systematic method for combining different forms of adaptive automation. A model for effective combination of different forms of adaptive automation, based on matching adaptation to operator workload was proposed and tested. The model was evaluated in studies using IFR-rated pilots flying a general-aviation simulator. Performance, subjective, and physiological (heart rate variability, eye scan-paths) measures of workload were recorded. The studies compared workload-based adaptation to to non-adaptive control conditions and found evidence for systematic benefits of adaptive automation. The research provides an empirical basis for evaluating the effectiveness of adaptive automation in the cockpit. The results contribute to the development of design principles and guidelines for the implementation of adaptive automation in the cockpit, particularly in general aviation, and in other human-machine systems. Project goals

An automated metrics system to measure and improve the success of laboratory automation implementation.

PubMed

Benn, Neil; Turlais, Fabrice; Clark, Victoria; Jones, Mike; Clulow, Stephen

2007-03-01

The authors describe a system for collecting usage metrics from widely distributed automation systems. An application that records and stores usage data centrally, calculates run times, and charts the data was developed. Data were collected over 20 months from at least 28 workstations. The application was used to plot bar charts of date versus run time for individual workstations, the automation in a specific laboratory, or automation of a specified type. The authors show that revised user training, redeployment of equipment, and running complimentary processes on one workstation can increase the average number of runs by up to 20-fold and run times by up to 450%. Active monitoring of usage leads to more effective use of automation. Usage data could be used to determine whether purchasing particular automation was a good investment.

Diagnostic Validity of an Automated Probabilistic Tractography in Amnestic Mild Cognitive Impairment

PubMed Central

Jung, Won Sang; Um, Yoo Hyun; Kang, Dong Woo; Lee, Chang Uk; Woo, Young Sup; Bahk, Won-Myong

2018-01-01

Objective Although several prior works showed the white matter (WM) integrity changes in amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease, it is still unclear the diagnostic accuracy of the WM integrity measurements using diffusion tensor imaging (DTI) in discriminating aMCI from normal controls. The aim of this study is to explore diagnostic validity of whole brain automated probabilistic tractography in discriminating aMCI from normal controls. Methods One hundred-two subjects (50 aMCI and 52 normal controls) were included and underwent DTI scans. Whole brain WM tracts were reconstructed with automated probabilistic tractography. Fractional anisotropy (FA) and mean diffusivity (MD) values of the memory related WM tracts were measured and compared between the aMCI and the normal control groups. In addition, the diagnostic validities of these WM tracts were evaluated. Results Decreased FA and increased MD values of memory related WM tracts were observed in the aMCI group compared with the control group. Among FA and MD value of each tract, the FA value of left cingulum angular bundle showed the highest area under the curve (AUC) of 0.85 with a sensitivity of 88.2%, a specificity of 76.9% in differentiating MCI patients from control subjects. Furthermore, the combination FA values of WM integrity measures of memory related WM tracts showed AUC value of 0.98, a sensitivity of 96%, a specificity of 94.2%. Conclusion Our results with good diagnostic validity of WM integrity measurements suggest DTI might be promising neuroimaging tool for early detection of aMCI and AD patients. PMID:29739127

Multiresolution texture models for brain tumor segmentation in MRI.

PubMed

Iftekharuddin, Khan M; Ahmed, Shaheen; Hossen, Jakir

2011-01-01

In this study we discuss different types of texture features such as Fractal Dimension (FD) and Multifractional Brownian Motion (mBm) for estimating random structures and varying appearance of brain tissues and tumors in magnetic resonance images (MRI). We use different selection techniques including KullBack - Leibler Divergence (KLD) for ranking different texture and intensity features. We then exploit graph cut, self organizing maps (SOM) and expectation maximization (EM) techniques to fuse selected features for brain tumors segmentation in multimodality T1, T2, and FLAIR MRI. We use different similarity metrics to evaluate quality and robustness of these selected features for tumor segmentation in MRI for real pediatric patients. We also demonstrate a non-patient-specific automated tumor prediction scheme by using improved AdaBoost classification based on these image features.

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles.

PubMed

Barker, Jocelyn; Hoogi, Assaf; Depeursinge, Adrien; Rubin, Daniel L

2016-05-01

Computerized analysis of digital pathology images offers the potential of improving clinical care (e.g. automated diagnosis) and catalyzing research (e.g. discovering disease subtypes). There are two key challenges thwarting computerized analysis of digital pathology images: first, whole slide pathology images are massive, making computerized analysis inefficient, and second, diverse tissue regions in whole slide images that are not directly relevant to the disease may mislead computerized diagnosis algorithms. We propose a method to overcome both of these challenges that utilizes a coarse-to-fine analysis of the localized characteristics in pathology images. An initial surveying stage analyzes the diversity of coarse regions in the whole slide image. This includes extraction of spatially localized features of shape, color and texture from tiled regions covering the slide. Dimensionality reduction of the features assesses the image diversity in the tiled regions and clustering creates representative groups. A second stage provides a detailed analysis of a single representative tile from each group. An Elastic Net classifier produces a diagnostic decision value for each representative tile. A weighted voting scheme aggregates the decision values from these tiles to obtain a diagnosis at the whole slide level. We evaluated our method by automatically classifying 302 brain cancer cases into two possible diagnoses (glioblastoma multiforme (N = 182) versus lower grade glioma (N = 120)) with an accuracy of 93.1% (p < 0.001). We also evaluated our method in the dataset provided for the 2014 MICCAI Pathology Classification Challenge, in which our method, trained and tested using 5-fold cross validation, produced a classification accuracy of 100% (p < 0.001). Our method showed high stability and robustness to parameter variation, with accuracy varying between 95.5% and 100% when evaluated for a wide range of parameters. Our approach may be useful to automatically

Automated assessment of early hypoxic brain edema in non-enhanced CT predicts outcome in patients after cardiac arrest.

PubMed

Hanning, Uta; Sporns, Peter Bernhard; Lebiedz, Pia; Niederstadt, Thomas; Zoubi, Tarek; Schmidt, Rene; Knecht, Stefan; Heindel, Walter; Kemmling, André

2016-07-01

Early prediction of potential neurological recovery in patients after cardiac arrest is challenging. Recent studies suggest that the densitrometic gray-white matter ratio (GWR) determined from cranial computed tomography (CT) scans may be a reliable predictor of poor outcome. We evaluated an automated, rater independent method to determine GWR in CT as an early objective imaging predictor of clinical outcome. We analyzed imaging data of 84 patients after cardiac arrest that underwent noncontrast CT within 24h after arrest. To determine GWR in CT we applied two methods using a recently published automated probabilistic gray-white matter segmentation algorithm (GWR_aut) and conventional manual measurements within gray-white regions of interest (GWR_man). Neurological outcome was graded by the cerebral performance category (CPC). As part of standard routine CPC was assessed by the treating physician in the intensive care unit at admission and at discharge to normal ward. The performance of GWR measures (automated and manual) to predict the binary clinical endpoints of poor (CPC3-5) and good outcome (CPC1-2) was assessed by ROC analysis with increasing discrimination thresholds. Results of GWR_aut were compared to GWR_man of two raters. Of 84 patients, 55 (65%) showed a poor outcome. ROC curve analysis revealed reliable outcome prediction of GWR_aut (AUC 0.860) and GWR_man (AUC 0.707 and 0.699, respectively). Predictive power of GWR_aut was higher than GWR_man by each rater (p=0.019 and p=0.021, respectively) at an optimal cut-off of 1.084 to predict poor outcome (optimal criterion with 92.7% sensitivity, 72.4% specificity). Interrater reliability of GWR_man by intra-class correlation coefficient (ICC) was moderate (0.551). Automated quantification of GWR in CT may be used as an objective observer-independent imaging marker for outcome in patients after cardiac arrest. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Team-Centered Perspective for Adaptive Automation Design

NASA Technical Reports Server (NTRS)

Prinzel, Lawrence J., III

2003-01-01

Automation represents a very active area of human factors research. The journal, Human Factors, published a special issue on automation in 1985. Since then, hundreds of scientific studies have been published examining the nature of automation and its interaction with human performance. However, despite a dramatic increase in research investigating human factors issues in aviation automation, there remain areas that need further exploration. This NASA Technical Memorandum describes a new area of automation design and research, called adaptive automation. It discusses the concepts and outlines the human factors issues associated with the new method of adaptive function allocation. The primary focus is on human-centered design, and specifically on ensuring that adaptive automation is from a team-centered perspective. The document shows that adaptive automation has many human factors issues common to traditional automation design. Much like the introduction of other new technologies and paradigm shifts, adaptive automation presents an opportunity to remediate current problems but poses new ones for human-automation interaction in aerospace operations. The review here is intended to communicate the philosophical perspective and direction of adaptive automation research conducted under the Aerospace Operations Systems (AOS), Physiological and Psychological Stressors and Factors (PPSF) project.

Toward designing for trust in database automation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Duez, P. P.; Jamieson, G. A.

Appropriate reliance on system automation is imperative for safe and productive work, especially in safety-critical systems. It is unsafe to rely on automation beyond its designed use; conversely, it can be both unproductive and unsafe to manually perform tasks that are better relegated to automated tools. Operator trust in automated tools mediates reliance, and trust appears to affect how operators use technology. As automated agents become more complex, the question of trust in automation is increasingly important. In order to achieve proper use of automation, we must engender an appropriate degree of trust that is sensitive to changes in operatingmore » functions and context. In this paper, we present research concerning trust in automation in the domain of automated tools for relational databases. Lee and See have provided models of trust in automation. One model developed by Lee and See identifies three key categories of information about the automation that lie along a continuum of attributional abstraction. Purpose-, process-and performance-related information serve, both individually and through inferences between them, to describe automation in such a way as to engender r properly-calibrated trust. Thus, one can look at information from different levels of attributional abstraction as a general requirements analysis for information key to appropriate trust in automation. The model of information necessary to engender appropriate trust in automation [1] is a general one. Although it describes categories of information, it does not provide insight on how to determine the specific information elements required for a given automated tool. We have applied the Abstraction Hierarchy (AH) to this problem in the domain of relational databases. The AH serves as a formal description of the automation at several levels of abstraction, ranging from a very abstract purpose-oriented description to a more concrete description of the resources involved in the automated

The 'problem' with automation - Inappropriate feedback and interaction, not 'over-automation'

NASA Technical Reports Server (NTRS)

Norman, D. A.

1990-01-01

Automation in high-risk industry is often blamed for causing harm and increasing the chance of human error when failures occur. It is proposed that the problem is not the presence of automation, but rather its inappropriate design. The problem is that the operations are performed appropriately under normal conditions, but there is inadequate feedback and interaction with the humans who must control the overall conduct of the task. The problem is that the automation is at an intermediate level of intelligence, powerful enough to take over control which used to be done by people, but not powerful enough to handle all abnormalities. Moreover, its level of intelligence is insufficient to provide the continual, appropriate feedback that occurs naturally among human operators. To solve this problem, the automation should either be made less intelligent or more so, but the current level is quite inappropriate. The overall message is that it is possible to reduce error through appropriate design considerations.

Automated fluorescent miscroscopic image analysis of PTBP1 expression in glioma

PubMed Central

Becker, Aline; Elder, Brad; Puduvalli, Vinay; Winter, Jessica; Gurcan, Metin

2017-01-01

Multiplexed immunofluorescent testing has not entered into diagnostic neuropathology due to the presence of several technical barriers, amongst which includes autofluorescence. This study presents the implementation of a methodology capable of overcoming the visual challenges of fluorescent microscopy for diagnostic neuropathology by using automated digital image analysis, with long term goal of providing unbiased quantitative analyses of multiplexed biomarkers for solid tissue neuropathology. In this study, we validated PTBP1, a putative biomarker for glioma, and tested the extent to which immunofluorescent microscopy combined with automated and unbiased image analysis would permit the utility of PTBP1 as a biomarker to distinguish diagnostically challenging surgical biopsies. As a paradigm, we utilized second resections from patients diagnosed either with reactive brain changes (pseudoprogression) and recurrent glioblastoma (true progression). Our image analysis workflow was capable of removing background autofluorescence and permitted quantification of DAPI-PTBP1 positive cells. PTBP1-positive nuclei, and the mean intensity value of PTBP1 signal in cells. Traditional pathological interpretation was unable to distinguish between groups due to unacceptably high discordance rates amongst expert neuropathologists. Our data demonstrated that recurrent glioblastoma showed more DAPI-PTBP1 positive cells and a higher mean intensity value of PTBP1 signal compared to resections from second surgeries that showed only reactive gliosis. Our work demonstrates the potential of utilizing automated image analysis to overcome the challenges of implementing fluorescent microscopy in diagnostic neuropathology. PMID:28282372

The Automation of Reserve Processing.

ERIC Educational Resources Information Center

Self, James

1985-01-01

Describes an automated reserve processing system developed locally at Clemons Library, University of Virginia. Discussion covers developments in the reserve operation at Clemons Library, automation of the processing and circulation functions of reserve collections, and changes in reserve operation performance and staffing needs due to automation.…

An Automated Platform for High-Resolution Tissue Imaging Using Nanospray Desorption Electrospray Ionization Mass Spectrometry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lanekoff, Ingela T.; Heath, Brandi S.; Liyu, Andrey V.

2012-10-02

An automated platform has been developed for acquisition and visualization of mass spectrometry imaging (MSI) data using nanospray desorption electrospray ionization (nano-DESI). The new system enables robust operation of the nano-DESI imaging source over many hours. This is achieved by controlling the distance between the sample and the probe by mounting the sample holder onto an automated XYZ stage and defining the tilt of the sample plane. This approach is useful for imaging of relatively flat samples such as thin tissue sections. Custom software called MSI QuickView was developed for visualization of large data sets generated in imaging experiments. MSImore » QuickView enables fast visualization of the imaging data during data acquisition and detailed processing after the entire image is acquired. The performance of the system is demonstrated by imaging rat brain tissue sections. High resolution mass analysis combined with MS/MS experiments enabled identification of lipids and metabolites in the tissue section. In addition, high dynamic range and sensitivity of the technique allowed us to generate ion images of low-abundance isobaric lipids. High-spatial resolution image acquired over a small region of the tissue section revealed the spatial distribution of an abundant brain metabolite, creatine, in the white and gray matter that is consistent with the literature data obtained using magnetic resonance spectroscopy.« less

Integration of Sparse Multi-modality Representation and Anatomical Constraint for Isointense Infant Brain MR Image Segmentation

PubMed Central

Wang, Li; Shi, Feng; Gao, Yaozong; Li, Gang; Gilmore, John H.; Lin, Weili; Shen, Dinggang

2014-01-01

Segmentation of infant brain MR images is challenging due to poor spatial resolution, severe partial volume effect, and the ongoing maturation and myelination process. During the first year of life, the brain image contrast between white and gray matters undergoes dramatic changes. In particular, the image contrast inverses around 6–8 months of age, where the white and gray matter tissues are isointense in T1 and T2 weighted images and hence exhibit the extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a general framework that adopts sparse representation to fuse the multi-modality image information and further incorporate the anatomical constraints for brain tissue segmentation. Specifically, we first derive an initial segmentation from a library of aligned images with ground-truth segmentations by using sparse representation in a patch-based fashion for the multi-modality T1, T2 and FA images. The segmentation result is further iteratively refined by integration of the anatomical constraint. The proposed method was evaluated on 22 infant brain MR images acquired at around 6 months of age by using a leave-one-out cross-validation, as well as other 10 unseen testing subjects. Our method achieved a high accuracy for the Dice ratios that measure the volume overlap between automated and manual segmentations, i.e., 0.889±0.008 for white matter and 0.870±0.006 for gray matter. PMID:24291615

Modeling Increased Complexity and the Reliance on Automation: FLightdeck Automation Problems (FLAP) Model

NASA Technical Reports Server (NTRS)

Ancel, Ersin; Shih, Ann T.

2014-01-01

This paper highlights the development of a model that is focused on the safety issue of increasing complexity and reliance on automation systems in transport category aircraft. Recent statistics show an increase in mishaps related to manual handling and automation errors due to pilot complacency and over-reliance on automation, loss of situational awareness, automation system failures and/or pilot deficiencies. Consequently, the aircraft can enter a state outside the flight envelope and/or air traffic safety margins which potentially can lead to loss-of-control (LOC), controlled-flight-into-terrain (CFIT), or runway excursion/confusion accidents, etc. The goal of this modeling effort is to provide NASA's Aviation Safety Program (AvSP) with a platform capable of assessing the impacts of AvSP technologies and products towards reducing the relative risk of automation related accidents and incidents. In order to do so, a generic framework, capable of mapping both latent and active causal factors leading to automation errors, is developed. Next, the framework is converted into a Bayesian Belief Network model and populated with data gathered from Subject Matter Experts (SMEs). With the insertion of technologies and products, the model provides individual and collective risk reduction acquired by technologies and methodologies developed within AvSP.

BrainMap VBM: An environment for structural meta-analysis.

PubMed

Vanasse, Thomas J; Fox, P Mickle; Barron, Daniel S; Robertson, Michaela; Eickhoff, Simon B; Lancaster, Jack L; Fox, Peter T

2018-05-02

The BrainMap database is a community resource that curates peer-reviewed, coordinate-based human neuroimaging literature. By pairing the results of neuroimaging studies with their relevant meta-data, BrainMap facilitates coordinate-based meta-analysis (CBMA) of the neuroimaging literature en masse or at the level of experimental paradigm, clinical disease, or anatomic location. Initially dedicated to the functional, task-activation literature, BrainMap is now expanding to include voxel-based morphometry (VBM) studies in a separate sector, titled: BrainMap VBM. VBM is a whole-brain, voxel-wise method that measures significant structural differences between or within groups which are reported as standardized, peak x-y-z coordinates. Here we describe BrainMap VBM, including the meta-data structure, current data volume, and automated reverse inference functions (region-to-disease profile) of this new community resource. CBMA offers a robust methodology for retaining true-positive and excluding false-positive findings across studies in the VBM literature. As with BrainMap's functional database, BrainMap VBM may be synthesized en masse or at the level of clinical disease or anatomic location. As a use-case scenario for BrainMap VBM, we illustrate a trans-diagnostic data-mining procedure wherein we explore the underlying network structure of 2,002 experiments representing over 53,000 subjects through independent components analysis (ICA). To reduce data-redundancy effects inherent to any database, we demonstrate two data-filtering approaches that proved helpful to ICA. Finally, we apply hierarchical clustering analysis (HCA) to measure network- and disease-specificity. This procedure distinguished psychiatric from neurological diseases. We invite the neuroscientific community to further exploit BrainMap VBM with other modeling approaches. © 2018 Wiley Periodicals, Inc.

Trust in automation: designing for appropriate reliance.

PubMed

Lee, John D; See, Katrina A

2004-01-01

Automation is often problematic because people fail to rely upon it appropriately. Because people respond to technology socially, trust influences reliance on automation. In particular, trust guides reliance when complexity and unanticipated situations make a complete understanding of the automation impractical. This review considers trust from the organizational, sociological, interpersonal, psychological, and neurological perspectives. It considers how the context, automation characteristics, and cognitive processes affect the appropriateness of trust. The context in which the automation is used influences automation performance and provides a goal-oriented perspective to assess automation characteristics along a dimension of attributional abstraction. These characteristics can influence trust through analytic, analogical, and affective processes. The challenges of extrapolating the concept of trust in people to trust in automation are discussed. A conceptual model integrates research regarding trust in automation and describes the dynamics of trust, the role of context, and the influence of display characteristics. Actual or potential applications of this research include improved designs of systems that require people to manage imperfect automation.

Mobile Collection and Automated Interpretation of EEG Data

NASA Technical Reports Server (NTRS)

Mintz, Frederick; Moynihan, Philip

2007-01-01

A system that would comprise mobile and stationary electronic hardware and software subsystems has been proposed for collection and automated interpretation of electroencephalographic (EEG) data from subjects in everyday activities in a variety of environments. By enabling collection of EEG data from mobile subjects engaged in ordinary activities (in contradistinction to collection from immobilized subjects in clinical settings), the system would expand the range of options and capabilities for performing diagnoses. Each subject would be equipped with one of the mobile subsystems, which would include a helmet that would hold floating electrodes (see figure) in those positions on the patient s head that are required in classical EEG data-collection techniques. A bundle of wires would couple the EEG signals from the electrodes to a multi-channel transmitter also located in the helmet. Electronic circuitry in the helmet transmitter would digitize the EEG signals and transmit the resulting data via a multidirectional RF patch antenna to a remote location. At the remote location, the subject s EEG data would be processed and stored in a database that would be auto-administered by a newly designed relational database management system (RDBMS). In this RDBMS, in nearly real time, the newly stored data would be subjected to automated interpretation that would involve comparison with other EEG data and concomitant peer-reviewed diagnoses stored in international brain data bases administered by other similar RDBMSs.

Automation and Cataloging.

ERIC Educational Resources Information Center

Furuta, Kenneth; And Others

1990-01-01

These three articles address issues in library cataloging that are affected by automation: (1) the impact of automation and bibliographic utilities on professional catalogers; (2) the effect of the LASS microcomputer software on the cost of authority work in cataloging at the University of Arizona; and (3) online subject heading and classification…

Automatic MRI 2D brain segmentation using graph searching technique.

PubMed

Pedoia, Valentina; Binaghi, Elisabetta

2013-09-01

Accurate and efficient segmentation of the whole brain in magnetic resonance (MR) images is a key task in many neuroscience and medical studies either because the whole brain is the final anatomical structure of interest or because the automatic extraction facilitates further analysis. The problem of segmenting brain MRI images has been extensively addressed by many researchers. Despite the relevant achievements obtained, automated segmentation of brain MRI imagery is still a challenging problem whose solution has to cope with critical aspects such as anatomical variability and pathological deformation. In the present paper, we describe and experimentally evaluate a method for segmenting brain from MRI images basing on two-dimensional graph searching principles for border detection. The segmentation of the whole brain over the entire volume is accomplished slice by slice, automatically detecting frames including eyes. The method is fully automatic and easily reproducible by computing the internal main parameters directly from the image data. The segmentation procedure is conceived as a tool of general applicability, although design requirements are especially commensurate with the accuracy required in clinical tasks such as surgical planning and post-surgical assessment. Several experiments were performed to assess the performance of the algorithm on a varied set of MRI images obtaining good results in terms of accuracy and stability. Copyright © 2012 John Wiley & Sons, Ltd.

Human-centered aircraft automation: A concept and guidelines

NASA Technical Reports Server (NTRS)

Billings, Charles E.

1991-01-01

Aircraft automation is examined and its effects on flight crews. Generic guidelines are proposed for the design and use of automation in transport aircraft, in the hope of stimulating increased and more effective dialogue among designers of automated cockpits, purchasers of automated aircraft, and the pilots who must fly those aircraft in line operations. The goal is to explore the means whereby automation may be a maximally effective tool or resource for pilots without compromising human authority and with an increase in system safety. After definition of the domain of the aircraft pilot and brief discussion of the history of aircraft automation, a concept of human centered automation is presented and discussed. Automated devices are categorized as a control automation, information automation, and management automation. The environment and context of aircraft automation are then considered, followed by thoughts on the likely future of automation of that category.

Classification of Automated Search Traffic

NASA Astrophysics Data System (ADS)

Buehrer, Greg; Stokes, Jack W.; Chellapilla, Kumar; Platt, John C.

As web search providers seek to improve both relevance and response times, they are challenged by the ever-increasing tax of automated search query traffic. Third party systems interact with search engines for a variety of reasons, such as monitoring a web site’s rank, augmenting online games, or possibly to maliciously alter click-through rates. In this paper, we investigate automated traffic (sometimes referred to as bot traffic) in the query stream of a large search engine provider. We define automated traffic as any search query not generated by a human in real time. We first provide examples of different categories of query logs generated by automated means. We then develop many different features that distinguish between queries generated by people searching for information, and those generated by automated processes. We categorize these features into two classes, either an interpretation of the physical model of human interactions, or as behavioral patterns of automated interactions. Using the these detection features, we next classify the query stream using multiple binary classifiers. In addition, a multiclass classifier is then developed to identify subclasses of both normal and automated traffic. An active learning algorithm is used to suggest which user sessions to label to improve the accuracy of the multiclass classifier, while also seeking to discover new classes of automated traffic. Performance analysis are then provided. Finally, the multiclass classifier is used to predict the subclass distribution for the search query stream.

Laboratory automation: trajectory, technology, and tactics.

PubMed

Markin, R S; Whalen, S A

2000-05-01

Laboratory automation is in its infancy, following a path parallel to the development of laboratory information systems in the late 1970s and early 1980s. Changes on the horizon in healthcare and clinical laboratory service that affect the delivery of laboratory results include the increasing age of the population in North America, the implementation of the Balanced Budget Act (1997), and the creation of disease management companies. Major technology drivers include outcomes optimization and phenotypically targeted drugs. Constant cost pressures in the clinical laboratory have forced diagnostic manufacturers into less than optimal profitability states. Laboratory automation can be a tool for the improvement of laboratory services and may decrease costs. The key to improvement of laboratory services is implementation of the correct automation technology. The design of this technology should be driven by required functionality. Automation design issues should be centered on the understanding of the laboratory and its relationship to healthcare delivery and the business and operational processes in the clinical laboratory. Automation design philosophy has evolved from a hardware-based approach to a software-based approach. Process control software to support repeat testing, reflex testing, and transportation management, and overall computer-integrated manufacturing approaches to laboratory automation implementation are rapidly expanding areas. It is clear that hardware and software are functionally interdependent and that the interface between the laboratory automation system and the laboratory information system is a key component. The cost-effectiveness of automation solutions suggested by vendors, however, has been difficult to evaluate because the number of automation installations are few and the precision with which operational data have been collected to determine payback is suboptimal. The trend in automation has moved from total laboratory automation to a

Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease

PubMed Central

Plant, Claudia; Teipel, Stefan J.; Oswald, Annahita; Böhm, Christian; Meindl, Thomas; Mourao-Miranda, Janaina; Bokde, Arun W.; Hampel, Harald; Ewers, Michael

2010-01-01

Subjects with mild cognitive impairment (MCI) have an increased risk to develop Alzheimer's disease (AD). Voxel-based MRI studies have demonstrated that widely distributed cortical and subcortical brain areas show atrophic changes in MCI, preceding the onset of AD-type dementia. Here we developed a novel data mining framework in combination with three different classifiers including support vector machine (SVM), Bayes statistics, and voting feature intervals (VFI) to derive a quantitative index of pattern matching for the prediction of the conversion from MCI to AD. MRI was collected in 32 AD patients, 24 MCI subjects and 18 healthy controls (HC). Nine out of 24 MCI subjects converted to AD after an average follow-up interval of 2.5 years. Using feature selection algorithms, brain regions showing the highest accuracy for the discrimination between AD and HC were identified, reaching a classification accuracy of up to 92%. The extracted AD clusters were used as a search region to extract those brain areas that are predictive of conversion to AD within MCI subjects. The most predictive brain areas included the anterior cingulate gyrus and orbitofrontal cortex. The best prediction accuracy, which was cross-validated via train-and-test, was 75% for the prediction of the conversion from MCI to AD. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD. PMID:19961938

Automation pilot

NASA Technical Reports Server (NTRS)

1983-01-01

An important concept of the Action Information Management System (AIMS) approach is to evaluate office automation technology in the context of hands on use by technical program managers in the conduct of human acceptance difficulties which may accompany the transition to a significantly changing work environment. The improved productivity and communications which result from application of office automation technology are already well established for general office environments, but benefits unique to NASA are anticipated and these will be explored in detail.

Selecting automation for the clinical chemistry laboratory.

PubMed

Melanson, Stacy E F; Lindeman, Neal I; Jarolim, Petr

2007-07-01

Laboratory automation proposes to improve the quality and efficiency of laboratory operations, and may provide a solution to the quality demands and staff shortages faced by today's clinical laboratories. Several vendors offer automation systems in the United States, with both subtle and obvious differences. Arriving at a decision to automate, and the ensuing evaluation of available products, can be time-consuming and challenging. Although considerable discussion concerning the decision to automate has been published, relatively little attention has been paid to the process of evaluating and selecting automation systems. To outline a process for evaluating and selecting automation systems as a reference for laboratories contemplating laboratory automation. Our Clinical Chemistry Laboratory staff recently evaluated all major laboratory automation systems in the United States, with their respective chemistry and immunochemistry analyzers. Our experience is described and organized according to the selection process, the important considerations in clinical chemistry automation, decisions and implementation, and we give conclusions pertaining to this experience. Including the formation of a committee, workflow analysis, submitting a request for proposal, site visits, and making a final decision, the process of selecting chemistry automation took approximately 14 months. We outline important considerations in automation design, preanalytical processing, analyzer selection, postanalytical storage, and data management. Selecting clinical chemistry laboratory automation is a complex, time-consuming process. Laboratories considering laboratory automation may benefit from the concise overview and narrative and tabular suggestions provided.

21 CFR 864.5200 - Automated cell counter.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Automated cell counter. 864.5200 Section 864.5200....5200 Automated cell counter. (a) Identification. An automated cell counter is a fully-automated or semi-automated device used to count red blood cells, white blood cells, or blood platelets using a sample of the...

Automated Neuropsychological Assessment Metrics (ANAM) Traumatic Brain Injury (TBI): Human Factors Assessment

DTIC Science & Technology

2011-07-01

Lindsay, Cory Overby, Angela Jeter, Petra E. Alfred, Gary L. Boykin, Carita DeVilbiss, and Raymond Bateman ARL-TN-0440 July 2011...Neuropsychological Assessment Metrics (ANAM) Traumatic Brain Injury (TBI): Human Factors Assessment Valerie J. Rice, Petra E. Alfred, Gary L. Boykin...Angela Jeter*, Petra E. Alfred, Gary L. Boykin, Carita DeVilbiss, and Raymond Bateman 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT

Flight-deck automation - Promises and problems

NASA Technical Reports Server (NTRS)

Wiener, E. L.; Curry, R. E.

1980-01-01

The paper analyzes the role of human factors in flight-deck automation, identifies problem areas, and suggests design guidelines. Flight-deck automation using microprocessor technology and display systems improves performance and safety while leading to a decrease in size, cost, and power consumption. On the other hand negative factors such as failure of automatic equipment, automation-induced error compounded by crew error, crew error in equipment set-up, failure to heed automatic alarms, and loss of proficiency must also be taken into account. Among the problem areas discussed are automation of control tasks, monitoring of complex systems, psychosocial aspects of automation, and alerting and warning systems. Guidelines are suggested for designing, utilising, and improving control and monitoring systems. Investigation into flight-deck automation systems is important as the knowledge gained can be applied to other systems such as air traffic control and nuclear power generation, but the many problems encountered with automated systems need to be analyzed and overcome in future research.

Modular workcells: modern methods for laboratory automation.

PubMed

Felder, R A

1998-12-01

Laboratory automation is beginning to become an indispensable survival tool for laboratories facing difficult market competition. However, estimates suggest that only 8% of laboratories will be able to afford total laboratory automation systems. Therefore, automation vendors have developed alternative hardware configurations called 'modular automation', to fit the smaller laboratory. Modular automation consists of consolidated analyzers, integrated analyzers, modular workcells, and pre- and post-analytical automation. These terms will be defined in this paper. Using a modular automation model, the automated core laboratory will become a site where laboratory data is evaluated by trained professionals to provide diagnostic information to practising physicians. Modem software information management and process control tools will complement modular hardware. Proper standardization that will allow vendor-independent modular configurations will assure success of this revolutionary new technology.

Physiological Self-Regulation and Adaptive Automation

NASA Technical Reports Server (NTRS)

Prinzell, Lawrence J.; Pope, Alan T.; Freeman, Frederick G.

2007-01-01

Adaptive automation has been proposed as a solution to current problems of human-automation interaction. Past research has shown the potential of this advanced form of automation to enhance pilot engagement and lower cognitive workload. However, there have been concerns voiced regarding issues, such as automation surprises, associated with the use of adaptive automation. This study examined the use of psychophysiological self-regulation training with adaptive automation that may help pilots deal with these problems through the enhancement of cognitive resource management skills. Eighteen participants were assigned to 3 groups (self-regulation training, false feedback, and control) and performed resource management, monitoring, and tracking tasks from the Multiple Attribute Task Battery. The tracking task was cycled between 3 levels of task difficulty (automatic, adaptive aiding, manual) on the basis of the electroencephalogram-derived engagement index. The other two tasks remained in automatic mode that had a single automation failure. Those participants who had received self-regulation training performed significantly better and reported lower National Aeronautics and Space Administration Task Load Index scores than participants in the false feedback and control groups. The theoretical and practical implications of these results for adaptive automation are discussed.

Protocols for Automated Protist Analysis

DTIC Science & Technology

2011-12-01

Report No: CG-D-14-13 Protocols for Automated Protist Analysis December 2011 Distribution Statement A: Approved for public...release; distribution is unlimited. Protocols for Automated Protist Analysis ii UNCLAS//Public | CG-926 RDC | B. Nelson, et al. | Public...Director United States Coast Guard Research & Development Center 1 Chelsea Street New London, CT 06320 Protocols for Automated Protist Analysis

Logistics Automation Master Plan (LAMP). Better Logistics Support through Automation.

DTIC Science & Technology

1983-06-01

office micro-computers, positioned throughout the command chain , by providing real time links between LCA and all users: 2. Goals: Assist HQDA staff in...field i.e., Airland Battle 2000. IV-27 Section V: CONCEPT OF EXECUTION Suply (Retail) A. SRstem Description. I. The Division Logistics Property Book...7. Divisional Direct Support Unit Automated Supply System (DDASS)/Direct pport Level Suply Automation (DLSA). DDASS and DLSA are system development

Automation: how much is too much?

PubMed

Hancock, P A

2014-01-01

The headlong rush to automate continues apace. The dominant question still remains whether we can automate, not whether we should automate. However, it is this latter question that is featured and considered explicitly here. The suggestion offered is that unlimited automation of all technical functions will eventually prove anathema to the fundamental quality of human life. Examples of tasks, pursuits and past-times that should potentially be excused from the automation imperative are discussed. This deliberation leads us back to the question of balance in the cooperation, coordination and potential conflict between humans and the machines they create.

A survey of MRI-based medical image analysis for brain tumor studies

NASA Astrophysics Data System (ADS)

Bauer, Stefan; Wiest, Roland; Nolte, Lutz-P.; Reyes, Mauricio

2013-07-01

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

Automated detection and quantification of residual brain tumor using an interactive computer-aided detection scheme

NASA Astrophysics Data System (ADS)

Gaffney, Kevin P.; Aghaei, Faranak; Battiste, James; Zheng, Bin

2017-03-01

Detection of residual brain tumor is important to evaluate efficacy of brain cancer surgery, determine optimal strategy of further radiation therapy if needed, and assess ultimate prognosis of the patients. Brain MR is a commonly used imaging modality for this task. In order to distinguish between residual tumor and surgery induced scar tissues, two sets of MRI scans are conducted pre- and post-gadolinium contrast injection. The residual tumors are only enhanced in the post-contrast injection images. However, subjective reading and quantifying this type of brain MR images faces difficulty in detecting real residual tumor regions and measuring total volume of the residual tumor. In order to help solve this clinical difficulty, we developed and tested a new interactive computer-aided detection scheme, which consists of three consecutive image processing steps namely, 1) segmentation of the intracranial region, 2) image registration and subtraction, 3) tumor segmentation and refinement. The scheme also includes a specially designed and implemented graphical user interface (GUI) platform. When using this scheme, two sets of pre- and post-contrast injection images are first automatically processed to detect and quantify residual tumor volume. Then, a user can visually examine segmentation results and conveniently guide the scheme to correct any detection or segmentation errors if needed. The scheme has been repeatedly tested using five cases. Due to the observed high performance and robustness of the testing results, the scheme is currently ready for conducting clinical studies and helping clinicians investigate the association between this quantitative image marker and outcome of patients.

Development and Implementation of a Corriedale Ovine Brain Atlas for Use in Atlas-Based Segmentation.

PubMed

Liyanage, Kishan Andre; Steward, Christopher; Moffat, Bradford Armstrong; Opie, Nicholas Lachlan; Rind, Gil Simon; John, Sam Emmanuel; Ronayne, Stephen; May, Clive Newton; O'Brien, Terence John; Milne, Marjorie Eileen; Oxley, Thomas James

2016-01-01

Segmentation is the process of partitioning an image into subdivisions and can be applied to medical images to isolate anatomical or pathological areas for further analysis. This process can be done manually or automated by the use of image processing computer packages. Atlas-based segmentation automates this process by the use of a pre-labelled template and a registration algorithm. We developed an ovine brain atlas that can be used as a model for neurological conditions such as Parkinson's disease and focal epilepsy. 17 female Corriedale ovine brains were imaged in-vivo in a 1.5T (low-resolution) MRI scanner. 13 of the low-resolution images were combined using a template construction algorithm to form a low-resolution template. The template was labelled to form an atlas and tested by comparing manual with atlas-based segmentations against the remaining four low-resolution images. The comparisons were in the form of similarity metrics used in previous segmentation research. Dice Similarity Coefficients were utilised to determine the degree of overlap between eight independent, manual and atlas-based segmentations, with values ranging from 0 (no overlap) to 1 (complete overlap). For 7 of these 8 segmented areas, we achieved a Dice Similarity Coefficient of 0.5-0.8. The amygdala was difficult to segment due to its variable location and similar intensity to surrounding tissues resulting in Dice Coefficients of 0.0-0.2. We developed a low resolution ovine brain atlas with eight clinically relevant areas labelled. This brain atlas performed comparably to prior human atlases described in the literature and to intra-observer error providing an atlas that can be used to guide further research using ovine brains as a model and is hosted online for public access.

Automation and robotics

NASA Technical Reports Server (NTRS)

Montemerlo, Melvin

1988-01-01

The Autonomous Systems focus on the automation of control systems for the Space Station and mission operations. Telerobotics focuses on automation for in-space servicing, assembly, and repair. The Autonomous Systems and Telerobotics each have a planned sequence of integrated demonstrations showing the evolutionary advance of the state-of-the-art. Progress is briefly described for each area of concern.

White matter lesion extension to automatic brain tissue segmentation on MRI.

PubMed

de Boer, Renske; Vrooman, Henri A; van der Lijn, Fedde; Vernooij, Meike W; Ikram, M Arfan; van der Lugt, Aad; Breteler, Monique M B; Niessen, Wiro J

2009-05-01

A fully automated brain tissue segmentation method is optimized and extended with white matter lesion segmentation. Cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM) are segmented by an atlas-based k-nearest neighbor classifier on multi-modal magnetic resonance imaging data. This classifier is trained by registering brain atlases to the subject. The resulting GM segmentation is used to automatically find a white matter lesion (WML) threshold in a fluid-attenuated inversion recovery scan. False positive lesions are removed by ensuring that the lesions are within the white matter. The method was visually validated on a set of 209 subjects. No segmentation errors were found in 98% of the brain tissue segmentations and 97% of the WML segmentations. A quantitative evaluation using manual segmentations was performed on a subset of 6 subjects for CSF, GM and WM segmentation and an additional 14 for the WML segmentations. The results indicated that the automatic segmentation accuracy is close to the interobserver variability of manual segmentations.

Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues

PubMed Central

Attique, Muhammad; Gilanie, Ghulam; Hafeez-Ullah; Mehmood, Malik S.; Naweed, Muhammad S.; Ikram, Masroor; Kamran, Javed A.; Vitkin, Alex

2012-01-01

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described. PMID:22479421

Cost effective raspberry pi-based radio frequency identification tagging of mice suitable for automated in vivo imaging.

PubMed

Bolaños, Federico; LeDue, Jeff M; Murphy, Timothy H

2017-01-30

Automation of animal experimentation improves consistency, reduces potential for error while decreasing animal stress and increasing well-being. Radio frequency identification (RFID) tagging can identify individual mice in group housing environments enabling animal-specific tracking of physiological parameters. We describe a simple protocol to radio frequency identification (RFID) tag and detect mice. RFID tags were injected sub-cutaneously after brief isoflurane anesthesia and do not require surgical steps such as suturing or incisions. We employ glass-encapsulated 125kHz tags that can be read within 30.2±2.4mm of the antenna. A raspberry pi single board computer and tag reader enable automated logging and cross platform support is possible through Python. We provide sample software written in Python to provide a flexible and cost effective system for logging the weights of multiple mice in relation to pre-defined targets. The sample software can serve as the basis of any behavioral or physiological task where users will need to identify and track specific animals. Recently, we have applied this system of tagging to automated mouse brain imaging within home-cages. We provide a cost effective solution employing open source software to facilitate adoption in applications such as automated imaging or tracking individual animal weights during tasks where food or water restriction is employed as motivation for a specific behavior. Copyright © 2016 Elsevier B.V. All rights reserved.

Fetal brain volumetry through MRI volumetric reconstruction and segmentation

PubMed Central

Estroff, Judy A.; Barnewolt, Carol E.; Connolly, Susan A.; Warfield, Simon K.

2013-01-01

Purpose Fetal MRI volumetry is a useful technique but it is limited by a dependency upon motion-free scans, tedious manual segmentation, and spatial inaccuracy due to thick-slice scans. An image processing pipeline that addresses these limitations was developed and tested. Materials and methods The principal sequences acquired in fetal MRI clinical practice are multiple orthogonal single-shot fast spin echo scans. State-of-the-art image processing techniques were used for inter-slice motion correction and super-resolution reconstruction of high-resolution volumetric images from these scans. The reconstructed volume images were processed with intensity non-uniformity correction and the fetal brain extracted by using supervised automated segmentation. Results Reconstruction, segmentation and volumetry of the fetal brains for a cohort of twenty-five clinically acquired fetal MRI scans was done. Performance metrics for volume reconstruction, segmentation and volumetry were determined by comparing to manual tracings in five randomly chosen cases. Finally, analysis of the fetal brain and parenchymal volumes was performed based on the gestational age of the fetuses. Conclusion The image processing pipeline developed in this study enables volume rendering and accurate fetal brain volumetry by addressing the limitations of current volumetry techniques, which include dependency on motion-free scans, manual segmentation, and inaccurate thick-slice interpolation. PMID:20625848

Brain segmentation and the generation of cortical surfaces

NASA Technical Reports Server (NTRS)

Joshi, M.; Cui, J.; Doolittle, K.; Joshi, S.; Van Essen, D.; Wang, L.; Miller, M. I.

1999-01-01

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.

Blastocyst microinjection automation.

PubMed

Mattos, Leonardo S; Grant, Edward; Thresher, Randy; Kluckman, Kimberly

2009-09-01

Blastocyst microinjections are routinely involved in the process of creating genetically modified mice for biomedical research, but their efficiency is highly dependent on the skills of the operators. As a consequence, much time and resources are required for training microinjection personnel. This situation has been aggravated by the rapid growth of genetic research, which has increased the demand for mutant animals. Therefore, increased productivity and efficiency in this area are highly desired. Here, we pursue these goals through the automation of a previously developed teleoperated blastocyst microinjection system. This included the design of a new system setup to facilitate automation, the definition of rules for automatic microinjections, the implementation of video processing algorithms to extract feedback information from microscope images, and the creation of control algorithms for process automation. Experimentation conducted with this new system and operator assistance during the cells delivery phase demonstrated a 75% microinjection success rate. In addition, implantation of the successfully injected blastocysts resulted in a 53% birth rate and a 20% yield of chimeras. These results proved that the developed system was capable of automatic blastocyst penetration and retraction, demonstrating the success of major steps toward full process automation.

Evolution paths for advanced automation

NASA Technical Reports Server (NTRS)

Healey, Kathleen J.

1990-01-01

As Space Station Freedom (SSF) evolves, increased automation and autonomy will be required to meet Space Station Freedom Program (SSFP) objectives. As a precursor to the use of advanced automation within the SSFP, especially if it is to be used on SSF (e.g., to automate the operation of the flight systems), the underlying technologies will need to be elevated to a high level of readiness to ensure safe and effective operations. Ground facilities supporting the development of these flight systems -- from research and development laboratories through formal hardware and software development environments -- will be responsible for achieving these levels of technology readiness. These facilities will need to evolve support the general evolution of the SSFP. This evolution will include support for increasing the use of advanced automation. The SSF Advanced Development Program has funded a study to define evolution paths for advanced automaton within the SSFP's ground-based facilities which will enable, promote, and accelerate the appropriate use of advanced automation on-board SSF. The current capability of the test beds and facilities, such as the Software Support Environment, with regard to advanced automation, has been assessed and their desired evolutionary capabilities have been defined. Plans and guidelines for achieving this necessary capability have been constructed. The approach taken has combined indepth interviews of test beds personnel at all SSF Work Package centers with awareness of relevant state-of-the-art technology and technology insertion methodologies. Key recommendations from the study include advocating a NASA-wide task force for advanced automation, and the creation of software prototype transition environments to facilitate the incorporation of advanced automation in the SSFP.

What Is an Automated External Defibrillator?

MedlinePlus

ANSWERS by heart Treatments + Tests What Is an Automated External Defibrillator? An automated external defibrillator (AED) is a lightweight, portable device ... ANSWERS by heart Treatments + Tests What Is an Automated External Defibrillator? detect a rhythm that should be ...

Deformably registering and annotating whole CLARITY brains to an atlas via masked LDDMM

NASA Astrophysics Data System (ADS)

Kutten, Kwame S.; Vogelstein, Joshua T.; Charon, Nicolas; Ye, Li; Deisseroth, Karl; Miller, Michael I.

2016-04-01

The CLARITY method renders brains optically transparent to enable high-resolution imaging in the structurally intact brain. Anatomically annotating CLARITY brains is necessary for discovering which regions contain signals of interest. Manually annotating whole-brain, terabyte CLARITY images is difficult, time-consuming, subjective, and error-prone. Automatically registering CLARITY images to a pre-annotated brain atlas offers a solution, but is difficult for several reasons. Removal of the brain from the skull and subsequent storage and processing cause variable non-rigid deformations, thus compounding inter-subject anatomical variability. Additionally, the signal in CLARITY images arises from various biochemical contrast agents which only sparsely label brain structures. This sparse labeling challenges the most commonly used registration algorithms that need to match image histogram statistics to the more densely labeled histological brain atlases. The standard method is a multiscale Mutual Information B-spline algorithm that dynamically generates an average template as an intermediate registration target. We determined that this method performs poorly when registering CLARITY brains to the Allen Institute's Mouse Reference Atlas (ARA), because the image histogram statistics are poorly matched. Therefore, we developed a method (Mask-LDDMM) for registering CLARITY images, that automatically finds the brain boundary and learns the optimal deformation between the brain and atlas masks. Using Mask-LDDMM without an average template provided better results than the standard approach when registering CLARITY brains to the ARA. The LDDMM pipelines developed here provide a fast automated way to anatomically annotate CLARITY images; our code is available as open source software at http://NeuroData.io.

Anatomical brain images alone can accurately diagnose chronic neuropsychiatric illnesses.

PubMed

Bansal, Ravi; Staib, Lawrence H; Laine, Andrew F; Hao, Xuejun; Xu, Dongrong; Liu, Jun; Weissman, Myrna; Peterson, Bradley S

2012-01-01

Diagnoses using imaging-based measures alone offer the hope of improving the accuracy of clinical diagnosis, thereby reducing the costs associated with incorrect treatments. Previous attempts to use brain imaging for diagnosis, however, have had only limited success in diagnosing patients who are independent of the samples used to derive the diagnostic algorithms. We aimed to develop a classification algorithm that can accurately diagnose chronic, well-characterized neuropsychiatric illness in single individuals, given the availability of sufficiently precise delineations of brain regions across several neural systems in anatomical MR images of the brain. We have developed an automated method to diagnose individuals as having one of various neuropsychiatric illnesses using only anatomical MRI scans. The method employs a semi-supervised learning algorithm that discovers natural groupings of brains based on the spatial patterns of variation in the morphology of the cerebral cortex and other brain regions. We used split-half and leave-one-out cross-validation analyses in large MRI datasets to assess the reproducibility and diagnostic accuracy of those groupings. In MRI datasets from persons with Attention-Deficit/Hyperactivity Disorder, Schizophrenia, Tourette Syndrome, Bipolar Disorder, or persons at high or low familial risk for Major Depressive Disorder, our method discriminated with high specificity and nearly perfect sensitivity the brains of persons who had one specific neuropsychiatric disorder from the brains of healthy participants and the brains of persons who had a different neuropsychiatric disorder. Although the classification algorithm presupposes the availability of precisely delineated brain regions, our findings suggest that patterns of morphological variation across brain surfaces, extracted from MRI scans alone, can successfully diagnose the presence of chronic neuropsychiatric disorders. Extensions of these methods are likely to provide biomarkers

Intensive working memory training: a single case experimental design in a patient following hypoxic brain damage.

PubMed

Hynes, S M; Fish, J; Manly, T

2014-01-01

Recent reports suggest that intensive, progressive training on working memory tasks can lead to generalized cognitive gains. A patient, following hypoxic brain damage, showed significant difficulties in working memory and time-perception. This study examined the impact and specificity of any benefits resulting from automated working memory training (AWMT) in comparison with the effects of an equivalent programme that emphasized automated novel problem-solving (APST) which served as an active control. Following initial assessment, the patient trained for 4 weeks (20 days), 20-30 minutes a day on the APST tasks before repeating key outcome measures. He then trained for an identical period on AWMT. There were no cognitive gains apparent following APST. Furthermore, there were no disproportionate gains on digit span following AWMT. AWMT was, however, associated with improvement in time-perception that had previously been resistant to rehabilitation. In line with previous reports, AWMT was also followed by gains on a measure of planning. The results provide encouraging evidence that AWMT may have generalized benefits in the context of impaired WM capacity following brain injury.

Automated DBS microsampling, microscale automation and microflow LC-MS for therapeutic protein PK.

PubMed

Zhang, Qian; Tomazela, Daniela; Vasicek, Lisa A; Spellman, Daniel S; Beaumont, Maribel; Shyong, BaoJen; Kenny, Jacqueline; Fauty, Scott; Fillgrove, Kerry; Harrelson, Jane; Bateman, Kevin P

2016-04-01

Reduce animal usage for discovery-stage PK studies for biologics programs using microsampling-based approaches and microscale LC-MS. We report the development of an automated DBS-based serial microsampling approach for studying the PK of therapeutic proteins in mice. Automated sample preparation and microflow LC-MS were used to enable assay miniaturization and improve overall assay throughput. Serial sampling of mice was possible over the full 21-day study period with the first six time points over 24 h being collected using automated DBS sample collection. Overall, this approach demonstrated comparable data to a previous study using single mice per time point liquid samples while reducing animal and compound requirements by 14-fold. Reduction in animals and drug material is enabled by the use of automated serial DBS microsampling for mice studies in discovery-stage studies of protein therapeutics.

A Statistically Representative Atlas for Mapping Neuronal Circuits in the Drosophila Adult Brain

PubMed Central

Arganda-Carreras, Ignacio; Manoliu, Tudor; Mazuras, Nicolas; Schulze, Florian; Iglesias, Juan E.; Bühler, Katja; Jenett, Arnim; Rouyer, François; Andrey, Philippe

2018-01-01

Imaging the expression patterns of reporter constructs is a powerful tool to dissect the neuronal circuits of perception and behavior in the adult brain of Drosophila, one of the major models for studying brain functions. To date, several Drosophila brain templates and digital atlases have been built to automatically analyze and compare collections of expression pattern images. However, there has been no systematic comparison of performances between alternative atlasing strategies and registration algorithms. Here, we objectively evaluated the performance of different strategies for building adult Drosophila brain templates and atlases. In addition, we used state-of-the-art registration algorithms to generate a new group-wise inter-sex atlas. Our results highlight the benefit of statistical atlases over individual ones and show that the newly proposed inter-sex atlas outperformed existing solutions for automated registration and annotation of expression patterns. Over 3,000 images from the Janelia Farm FlyLight collection were registered using the proposed strategy. These registered expression patterns can be searched and compared with a new version of the BrainBaseWeb system and BrainGazer software. We illustrate the validity of our methodology and brain atlas with registration-based predictions of expression patterns in a subset of clock neurons. The described registration framework should benefit to brain studies in Drosophila and other insect species. PMID:29628885

A Statistically Representative Atlas for Mapping Neuronal Circuits in the Drosophila Adult Brain.

PubMed

Arganda-Carreras, Ignacio; Manoliu, Tudor; Mazuras, Nicolas; Schulze, Florian; Iglesias, Juan E; Bühler, Katja; Jenett, Arnim; Rouyer, François; Andrey, Philippe

2018-01-01

Imaging the expression patterns of reporter constructs is a powerful tool to dissect the neuronal circuits of perception and behavior in the adult brain of Drosophila , one of the major models for studying brain functions. To date, several Drosophila brain templates and digital atlases have been built to automatically analyze and compare collections of expression pattern images. However, there has been no systematic comparison of performances between alternative atlasing strategies and registration algorithms. Here, we objectively evaluated the performance of different strategies for building adult Drosophila brain templates and atlases. In addition, we used state-of-the-art registration algorithms to generate a new group-wise inter-sex atlas. Our results highlight the benefit of statistical atlases over individual ones and show that the newly proposed inter-sex atlas outperformed existing solutions for automated registration and annotation of expression patterns. Over 3,000 images from the Janelia Farm FlyLight collection were registered using the proposed strategy. These registered expression patterns can be searched and compared with a new version of the BrainBaseWeb system and BrainGazer software. We illustrate the validity of our methodology and brain atlas with registration-based predictions of expression patterns in a subset of clock neurons. The described registration framework should benefit to brain studies in Drosophila and other insect species.

Automating spectral measurements

NASA Astrophysics Data System (ADS)

Goldstein, Fred T.

2008-09-01

This paper discusses the architecture of software utilized in spectroscopic measurements. As optical coatings become more sophisticated, there is mounting need to automate data acquisition (DAQ) from spectrophotometers. Such need is exacerbated when 100% inspection is required, ancillary devices are utilized, cost reduction is crucial, or security is vital. While instrument manufacturers normally provide point-and-click DAQ software, an application programming interface (API) may be missing. In such cases automation is impossible or expensive. An API is typically provided in libraries (*.dll, *.ocx) which may be embedded in user-developed applications. Users can thereby implement DAQ automation in several Windows languages. Another possibility, developed by FTG as an alternative to instrument manufacturers' software, is the ActiveX application (*.exe). ActiveX, a component of many Windows applications, provides means for programming and interoperability. This architecture permits a point-and-click program to act as automation client and server. Excel, for example, can control and be controlled by DAQ applications. Most importantly, ActiveX permits ancillary devices such as barcode readers and XY-stages to be easily and economically integrated into scanning procedures. Since an ActiveX application has its own user-interface, it can be independently tested. The ActiveX application then runs (visibly or invisibly) under DAQ software control. Automation capabilities are accessed via a built-in spectro-BASIC language with industry-standard (VBA-compatible) syntax. Supplementing ActiveX, spectro-BASIC also includes auxiliary serial port commands for interfacing programmable logic controllers (PLC). A typical application is automatic filter handling.

Ask the experts: automation: part I.

PubMed

Allinson, John L; Blick, Kenneth E; Cohen, Lucinda; Higton, David; Li, Ming

2013-08-01

Bioanalysis invited a selection of leading researchers to express their views on automation in the bioanalytical laboratory. The topics discussed include the challenges that the modern bioanalyst faces when integrating automation into existing drug-development processes, the impact of automation and how they envision the modern bioanalytical laboratory changing in the near future. Their enlightening responses provide a valuable insight into the impact of automation and the future of the constantly evolving bioanalytical laboratory.

An Automation Survival Guide for Media Centers.

ERIC Educational Resources Information Center

Whaley, Roger E.

1989-01-01

Reviews factors that should affect the decision to automate a school media center and offers suggestions for the automation process. Topics discussed include getting the library collection ready for automation, deciding what automated functions are needed, evaluating software vendors, selecting software, and budgeting. (CLB)

Brain volume and fatigue in patients with postpoliomyelitis syndrome.

PubMed

Trojan, Daria A; Narayanan, Sridar; Francis, Simon J; Caramanos, Zografos; Robinson, Ann; Cardoso, Mauro; Arnold, Douglas L

2014-03-01

Acute paralytic poliomyelitis is associated with encephalitis. Early brain inflammation may produce permanent neuronal injury with brain atrophy, which may result in symptoms such as fatigue. Brain volume has not been assessed in postpoliomyelitis syndrome (PPS). To determine whether brain volume is decreased compared with that in normal controls, and whether brain volume is associated with fatigue in patients with PPS. A cross-sectional study. Tertiary university-affiliated hospital postpolio and multiple sclerosis (MS) clinics. Forty-nine ambulatory patients with PPS, 28 normal controls, and 53 ambulatory patients with MS. We studied the brains of all study subjects with magnetic resonance imaging by using a 1.5 T Siemens Sonata machine. The subjects completed the Fatigue Severity Scale. Multivariable linear regression models were computed to evaluate the contribution of PPS and MS compared with controls to explain brain volume. Normalized brain volume (NBV) was assessed with the automated program Structured Image Evaluation, using Normalization, of Atrophy method from the acquired magnetic resonance images. This method may miss brainstem atrophy. Technically adequate NBV measurements were available for 42 patients with PPS, 27 controls, and 49 patients with MS. The mean (standard deviation) age was 60.9 ± 7.6 years for patients with PPS, 47.0 ± 14.6 years for controls, and 46.2 ± 9.4 years for patients with MS. In a multivariable model adjusted for age and gender, NBV was not significantly different in patients with PPS compared with that in controls (P = .28). As expected, when using a similar model for patients with MS, NBV was significantly decreased compared with that in controls (P = .006). There was no significant association between NBV and fatigue in subjects with PPS (Spearman ρ = 0.23; P = .19). No significant whole-brain atrophy was found, and no association of brain volume with fatigue in PPS. Brain atrophy was confirmed in MS. It is

Automated diagnosis of autism: in search of a mathematical marker.

PubMed

Bhat, Shreya; Acharya, U Rajendra; Adeli, Hojjat; Bairy, G Muralidhar; Adeli, Amir

2014-01-01

Autism is a type of neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. An early detection of the abnormality, due to irregular processing in the brain, can be achieved using electroencephalograms (EEG). The variations in the EEG signals cannot be deciphered by mere visual inspection. Computer-aided diagnostic tools can be used to recognize the subtle and invisible information present in the irregular EEG pattern and diagnose autism. This paper presents a state-of-the-art review of automated EEG-based diagnosis of autism. Various time domain, frequency domain, time-frequency domain, and nonlinear dynamics for the analysis of autistic EEG signals are described briefly. A focus of the review is the use of nonlinear dynamics and chaos theory to discover the mathematical biomarkers for the diagnosis of the autism analogous to biological markers. A combination of the time-frequency and nonlinear dynamic analysis is the most effective approach to characterize the nonstationary and chaotic physiological signals for the automated EEG-based diagnosis of autism spectrum disorder (ASD). The features extracted using these nonlinear methods can be used as mathematical markers to detect the early stage of autism and aid the clinicians in their diagnosis. This will expedite the administration of appropriate therapies to treat the disorder.

[Non-medical applications for brain MRI: Ethical considerations].

PubMed

Sarrazin, S; Fagot-Largeault, A; Leboyer, M; Houenou, J

2015-04-01

The recent neuroimaging techniques offer the possibility to better understand complex cognitive processes that are involved in mental disorders and thus have become cornerstone tools for research in psychiatry. The performances of functional magnetic resonance imaging are not limited to medical research and are used in non-medical fields. These recent applications represent new challenges for bioethics. In this article we aim at discussing the new ethical issues raised by the applications of the latest neuroimaging technologies to non-medical fields. We included a selection of peer-reviewed English medical articles after a search on NCBI Pubmed database and Google scholar from 2000 to 2013. We screened bibliographical tables for supplementary references. Websites of governmental French institutions implicated in ethical questions were also screened for governmental reports. Findings of brain areas supporting emotional responses and regulation have been used for marketing research, also called neuromarketing. The discovery of different brain activation patterns in antisocial disorder has led to changes in forensic psychiatry with the use of imaging techniques with unproven validity. Automated classification algorithms and multivariate statistical analyses of brain images have been applied to brain-reading techniques, aiming at predicting unconscious neural processes in humans. We finally report the current position of the French legislation recently revised and discuss the technical limits of such techniques. In the near future, brain imaging could find clinical applications in psychiatry as diagnostic or predictive tools. However, the latest advances in brain imaging are also used in non-scientific fields raising key ethical questions. Involvement of neuroscientists, psychiatrists, physicians but also of citizens in neuroethics discussions is crucial to challenge the risk of unregulated uses of brain imaging. Copyright © 2014 L’Encéphale, Paris. Published by

Automations influence on nuclear power plants: a look at three accidents and how automation played a role.

PubMed

Schmitt, Kara

2012-01-01

Nuclear power is one of the ways that we can design an efficient sustainable future. Automation is the primary system used to assist operators in the task of monitoring and controlling nuclear power plants (NPP). Automation performs tasks such as assessing the status of the plant's operations as well as making real time life critical situational specific decisions. While the advantages and disadvantages of automation are well studied in variety of domains, accidents remind us that there is still vulnerability to unknown variables. This paper will look at the effects of automation within three NPP accidents and incidents and will consider why automation failed in preventing these accidents from occurring. It will also review the accidents at the Three Mile Island, Chernobyl, and Fukushima Daiichi NPP's in order to determine where better use of automation could have resulted in a more desirable outcome.

Automation in School Library Media Centers.

ERIC Educational Resources Information Center

Driver, Russell W.; Driver, Mary Anne

1982-01-01

Surveys the historical development of automated technical processing in schools and notes the impact of this automation in a number of cases. Speculations about the future involvement of school libraries in automated processing and networking are included. Thirty references are listed. (BBM)

Observer performance in semi-automated microbleed detection

NASA Astrophysics Data System (ADS)

Kuijf, Hugo J.; Brundel, Manon; de Bresser, Jeroen; Viergever, Max A.; Biessels, Geert Jan; Geerlings, Mirjam I.; Vincken, Koen L.

2013-03-01

Cerebral microbleeds are small bleedings in the human brain, detectable with MRI. Microbleeds are associated with vascular disease and dementia. The number of studies involving microbleed detection is increasing rapidly. Visual rating is the current standard for detection, but is a time-consuming process, especially at high-resolution 7.0 T MR images, has limited reproducibility and is highly observer dependent. Recently, multiple techniques have been published for the semi-automated detection of microbleeds, attempting to overcome these problems. In the present study, a 7.0 T dual-echo gradient echo MR image was acquired in 18 participants with microbleeds from the SMART study. Two experienced observers identified 54 microbleeds in these participants, using a validated visual rating scale. The radial symmetry transform (RST) can be used for semi-automated detection of microbleeds in 7.0 T MR images. In the present study, the results of the RST were assessed by two observers and 47 microbleeds were identified: 35 true positives and 12 extra positives (microbleeds that were missed during visual rating). Hence, after scoring a total number of 66 microbleeds could be identified in the 18 participants. The use of the RST increased the average sensitivity of observers from 59% to 69%. More importantly, inter-observer agreement (ICC and Dice's coefficient) increased from 0.85 and 0.64 to 0.98 and 0.96, respectively. Furthermore, the required rating time was reduced from 30 to 2 minutes per participant. By fine-tuning the RST, sensitivities up to 90% can be achieved, at the cost of extra false positives.

"First generation" automated DNA sequencing technology.

PubMed

Slatko, Barton E; Kieleczawa, Jan; Ju, Jingyue; Gardner, Andrew F; Hendrickson, Cynthia L; Ausubel, Frederick M

2011-10-01

Beginning in the 1980s, automation of DNA sequencing has greatly increased throughput, reduced costs, and enabled large projects to be completed more easily. The development of automation technology paralleled the development of other aspects of DNA sequencing: better enzymes and chemistry, separation and imaging technology, sequencing protocols, robotics, and computational advancements (including base-calling algorithms with quality scores, database developments, and sequence analysis programs). Despite the emergence of high-throughput sequencing platforms, automated Sanger sequencing technology remains useful for many applications. This unit provides background and a description of the "First-Generation" automated DNA sequencing technology. It also includes protocols for using the current Applied Biosystems (ABI) automated DNA sequencing machines. © 2011 by John Wiley & Sons, Inc.

Introduction matters: Manipulating trust in automation and reliance in automated driving.

PubMed

Körber, Moritz; Baseler, Eva; Bengler, Klaus

2018-01-01

Trust in automation is a key determinant for the adoption of automated systems and their appropriate use. Therefore, it constitutes an essential research area for the introduction of automated vehicles to road traffic. In this study, we investigated the influence of trust promoting (Trust promoted group) and trust lowering (Trust lowered group) introductory information on reported trust, reliance behavior and take-over performance. Forty participants encountered three situations in a 17-min highway drive in a conditionally automated vehicle (SAE Level 3). Situation 1 and Situation 3 were non-critical situations where a take-over was optional. Situation 2 represented a critical situation where a take-over was necessary to avoid a collision. A non-driving-related task (NDRT) was presented between the situations to record the allocation of visual attention. Participants reporting a higher trust level spent less time looking at the road or instrument cluster and more time looking at the NDRT. The manipulation of introductory information resulted in medium differences in reported trust and influenced participants' reliance behavior. Participants of the Trust promoted group looked less at the road or instrument cluster and more at the NDRT. The odds of participants of the Trust promoted group to overrule the automated driving system in the non-critical situations were 3.65 times (Situation 1) to 5 times (Situation 3) higher. In Situation 2, the Trust promoted group's mean take-over time was extended by 1154 ms and the mean minimum time-to-collision was 933 ms shorter. Six participants from the Trust promoted group compared to no participant of the Trust lowered group collided with the obstacle. The results demonstrate that the individual trust level influences how much drivers monitor the environment while performing an NDRT. Introductory information influences this trust level, reliance on an automated driving system, and if a critical take-over situation can be

Flight-deck automation: Promises and problems

NASA Technical Reports Server (NTRS)

Wiener, E. L.; Curry, R. E.

1980-01-01

The state of the art in human factors in flight-deck automation is presented. A number of critical problem areas are identified and broad design guidelines are offered. Automation-related aircraft accidents and incidents are discussed as examples of human factors problems in automated flight.

Automation in Photogrammetry,

DTIC Science & Technology

1980-07-25

matrix (DTM) and digital planimetric data, combined and integrated into so-called "data bases." I’ll say more about this later. AUTOMATION OF...projection with mechanical inversors to maintain the Scheimpflug condition. Some automation has been achieved, with computer control to determine rectifier... matrix (DTM) form that is not necessarily collected from the same photography as that from which the orthophoto is being produced. Because they are

21 CFR 864.5200 - Automated cell counter.

Code of Federal Regulations, 2014 CFR

2014-04-01

....5200 Automated cell counter. (a) Identification. An automated cell counter is a fully-automated or semi-automated device used to count red blood cells, white blood cells, or blood platelets using a sample of the patient's peripheral blood (blood circulating in one of the body's extremities, such as the arm). These...

21 CFR 864.5200 - Automated cell counter.

Code of Federal Regulations, 2011 CFR

2011-04-01

....5200 Automated cell counter. (a) Identification. An automated cell counter is a fully-automated or semi-automated device used to count red blood cells, white blood cells, or blood platelets using a sample of the patient's peripheral blood (blood circulating in one of the body's extremities, such as the arm). These...

21 CFR 864.5200 - Automated cell counter.

Code of Federal Regulations, 2012 CFR

2012-04-01

....5200 Automated cell counter. (a) Identification. An automated cell counter is a fully-automated or semi-automated device used to count red blood cells, white blood cells, or blood platelets using a sample of the patient's peripheral blood (blood circulating in one of the body's extremities, such as the arm). These...

21 CFR 864.5200 - Automated cell counter.

Code of Federal Regulations, 2013 CFR

2013-04-01

....5200 Automated cell counter. (a) Identification. An automated cell counter is a fully-automated or semi-automated device used to count red blood cells, white blood cells, or blood platelets using a sample of the patient's peripheral blood (blood circulating in one of the body's extremities, such as the arm). These...

Regional infant brain development: an MRI-based morphometric analysis in 3 to 13 month olds.

PubMed

Choe, Myong-Sun; Ortiz-Mantilla, Silvia; Makris, Nikos; Gregas, Matt; Bacic, Janine; Haehn, Daniel; Kennedy, David; Pienaar, Rudolph; Caviness, Verne S; Benasich, April A; Grant, P Ellen

2013-09-01

Elucidation of infant brain development is a critically important goal given the enduring impact of these early processes on various domains including later cognition and language. Although infants' whole-brain growth rates have long been available, regional growth rates have not been reported systematically. Accordingly, relatively less is known about the dynamics and organization of typically developing infant brains. Here we report global and regional volumetric growth of cerebrum, cerebellum, and brainstem with gender dimorphism, in 33 cross-sectional scans, over 3 to 13 months, using T1-weighted 3-dimensional spoiled gradient echo images and detailed semi-automated brain segmentation. Except for the midbrain and lateral ventricles, all absolute volumes of brain regions showed significant growth, with 6 different patterns of volumetric change. When normalized to the whole brain, the regional increase was characterized by 5 differential patterns. The putamen, cerebellar hemispheres, and total cerebellum were the only regions that showed positive growth in the normalized brain. Our results show region-specific patterns of volumetric change and contribute to the systematic understanding of infant brain development. This study greatly expands our knowledge of normal development and in future may provide a basis for identifying early deviation above and beyond normative variation that might signal higher risk for neurological disorders.

Regional Infant Brain Development: An MRI-Based Morphometric Analysis in 3 to 13 Month Olds

PubMed Central

Choe, Myong-sun; Ortiz-Mantilla, Silvia; Makris, Nikos; Gregas, Matt; Bacic, Janine; Haehn, Daniel; Kennedy, David; Pienaar, Rudolph; Caviness, Verne S.; Benasich, April A.; Grant, P. Ellen

2013-01-01

Elucidation of infant brain development is a critically important goal given the enduring impact of these early processes on various domains including later cognition and language. Although infants’ whole-brain growth rates have long been available, regional growth rates have not been reported systematically. Accordingly, relatively less is known about the dynamics and organization of typically developing infant brains. Here we report global and regional volumetric growth of cerebrum, cerebellum, and brainstem with gender dimorphism, in 33 cross-sectional scans, over 3 to 13 months, using T1-weighted 3-dimensional spoiled gradient echo images and detailed semi-automated brain segmentation. Except for the midbrain and lateral ventricles, all absolute volumes of brain regions showed significant growth, with 6 different patterns of volumetric change. When normalized to the whole brain, the regional increase was characterized by 5 differential patterns. The putamen, cerebellar hemispheres, and total cerebellum were the only regions that showed positive growth in the normalized brain. Our results show region-specific patterns of volumetric change and contribute to the systematic understanding of infant brain development. This study greatly expands our knowledge of normal development and in future may provide a basis for identifying early deviation above and beyond normative variation that might signal higher risk for neurological disorders. PMID:22772652

A combined MR and CT study for precise quantitative analysis of the avian brain

NASA Astrophysics Data System (ADS)

Jirak, Daniel; Janacek, Jiri; Kear, Benjamin P.

2015-10-01

Brain size is widely used as a measure of behavioural complexity and sensory-locomotive capacity in avians but has largely relied upon laborious dissections, endoneurocranial tissue displacement, and physical measurement to derive comparative volumes. As an alternative, we present a new precise calculation method based upon coupled magnetic resonance (MR) imaging and computed tomography (CT). Our approach utilizes a novel interactive Fakir probe cross-referenced with an automated CT protocol to efficiently generate total volumes and surface areas of the brain tissue and endoneurocranial space, as well as the discrete cephalic compartments. We also complemented our procedures by using sodium polytungstate (SPT) as a contrast agent. This greatly enhanced CT applications but did not degrade MR quality and is therefore practical for virtual brain tissue reconstructions employing multiple imaging modalities. To demonstrate our technique, we visualized sex-based brain size differentiation in a sample set of Ring-necked pheasants (Phasianus colchicus). This revealed no significant variance in relative volume or surface areas of the primary brain regions. Rather, a trend towards isometric enlargement of the total brain and endoneurocranial space was evidenced in males versus females, thus advocating a non-differential sexually dimorphic pattern of brain size increase amongst these facultatively flying birds.

Investigating structural brain changes of dehydration using voxel-based morphometry.

PubMed

Streitbürger, Daniel-Paolo; Möller, Harald E; Tittgemeyer, Marc; Hund-Georgiadis, Margret; Schroeter, Matthias L; Mueller, Karsten

2012-01-01

Dehydration can affect the volume of brain structures, which might imply a confound in volumetric and morphometric studies of normal or diseased brain. Six young, healthy volunteers were repeatedly investigated using three-dimensional T(1)-weighted magnetic resonance imaging during states of normal hydration, hyperhydration, and dehydration to assess volume changes in gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). The datasets were analyzed using voxel-based morphometry (VBM), a widely used voxel-wise statistical analysis tool, FreeSurfer, a fully automated volumetric segmentation measure, and SIENAr a longitudinal brain-change detection algorithm. A significant decrease of GM and WM volume associated with dehydration was found in various brain regions, most prominently, in temporal and sub-gyral parietal areas, in the left inferior orbito-frontal region, and in the extra-nuclear region. Moreover, we found consistent increases in CSF, that is, an expansion of the ventricular system affecting both lateral ventricles, the third, and the fourth ventricle. Similar degrees of shrinkage in WM volume and increase of the ventricular system have been reported in studies of mild cognitive impairment or Alzheimer's disease during disease progression. Based on these findings, a potential confound in GM and WM or ventricular volume studies due to the subjects' hydration state cannot be excluded and should be appropriately addressed in morphometric studies of the brain.

Investigating Structural Brain Changes of Dehydration Using Voxel-Based Morphometry

PubMed Central

Streitbürger, Daniel-Paolo; Möller, Harald E.; Tittgemeyer, Marc; Hund-Georgiadis, Margret; Schroeter, Matthias L.; Mueller, Karsten

2012-01-01

Dehydration can affect the volume of brain structures, which might imply a confound in volumetric and morphometric studies of normal or diseased brain. Six young, healthy volunteers were repeatedly investigated using three-dimensional T 1-weighted magnetic resonance imaging during states of normal hydration, hyperhydration, and dehydration to assess volume changes in gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). The datasets were analyzed using voxel-based morphometry (VBM), a widely used voxel-wise statistical analysis tool, FreeSurfer, a fully automated volumetric segmentation measure, and SIENAr a longitudinal brain-change detection algorithm. A significant decrease of GM and WM volume associated with dehydration was found in various brain regions, most prominently, in temporal and sub-gyral parietal areas, in the left inferior orbito-frontal region, and in the extra-nuclear region. Moreover, we found consistent increases in CSF, that is, an expansion of the ventricular system affecting both lateral ventricles, the third, and the fourth ventricle. Similar degrees of shrinkage in WM volume and increase of the ventricular system have been reported in studies of mild cognitive impairment or Alzheime s disease during disease progression. Based on these findings, a potential confound in GM and WM or ventricular volume studies due to the subjects’ hydration state cannot be excluded and should be appropriately addressed in morphometric studies of the brain. PMID:22952926

Brain atrophy and cerebral small vessel disease: a prospective follow-up study.

PubMed

Nitkunan, Arani; Lanfranconi, Silvia; Charlton, Rebecca A; Barrick, Thomas R; Markus, Hugh S

2011-01-01

cerebral small vessel disease (SVD) is the most common cause of vascular dementia. Interest in the use of surrogate markers is increasing. The aims of this study were to determine if brain volume was different between patients with SVD and control subjects, whether it correlated with cognition in SVD, and whether changes in brain volume could be detected during prospective follow-up. thirty-five patients (mean age, 68.8 years) who had a lacunar stroke and radiological evidence of confluent leukoaraiosis and 70 age- and gender-matched control subjects were recruited. Whole-brain T1-weighted imaging and neuropsychological testing were performed after 1 year on all patients and after 2 years for the control subjects. Fully automated software was used to determine brain volume and percentage brain volume change. An executive function score was derived. there was a significant difference in brain volume between the patients with SVD and control subjects (mean ± SD [mL] 1529 ± 84 versus 1573 ± 69, P=0.019). In the patients with SVD, there was a significant association between brain volume and executive function (r=0.501, P<0.05). The mean ± SD yearly brain atrophy rate for patients with SVD and control subjects was significantly different (-0.914% ± 0.8% versus -0.498% ± 0.4%, respectively, P=0.017). No change in executive function score was detected over this period. brain volume is reduced in SVD and a decline is detectable prospectively. The correlation with executive function at a cross-sectional level and the change in brain volume with time are both promising for the use of brain atrophy as a surrogate marker of SVD progression.

Automation Applications in an Advanced Air Traffic Management System : Volume 4A. Automation Requirements.

DOT National Transportation Integrated Search

1974-08-01

Volume 4 describes the automation requirements. A presentation of automation requirements is made for an advanced air traffic management system in terms of controller work force, computer resources, controller productivity, system manning, failure ef...

The Pros and Cons of Army Automation

DTIC Science & Technology

2007-11-13

The Pros and Cons of Army Automation 1 Running Head: THE PROS AND CONS OF ARMY AUTOMATION The Pros and Cons of Army Automation SGM...TITLE AND SUBTITLE The Pros and Cons of Army Automation 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT...Prescribed by ANSI Std Z39-18 The Pros and Cons of Army Automation 2 Outline I. Introduction (MSG (P) Dostie) II. Manual skills (MSG (P

I trust it, but I don't know why: effects of implicit attitudes toward automation on trust in an automated system.

PubMed

Merritt, Stephanie M; Heimbaugh, Heather; LaChapell, Jennifer; Lee, Deborah

2013-06-01

This study is the first to examine the influence of implicit attitudes toward automation on users' trust in automation. Past empirical work has examined explicit (conscious) influences on user level of trust in automation but has not yet measured implicit influences. We examine concurrent effects of explicit propensity to trust machines and implicit attitudes toward automation on trust in an automated system. We examine differential impacts of each under varying automation performance conditions (clearly good, ambiguous, clearly poor). Participants completed both a self-report measure of propensity to trust and an Implicit Association Test measuring implicit attitude toward automation, then performed an X-ray screening task. Automation performance was manipulated within-subjects by varying the number and obviousness of errors. Explicit propensity to trust and implicit attitude toward automation did not significantly correlate. When the automation's performance was ambiguous, implicit attitude significantly affected automation trust, and its relationship with propensity to trust was additive: Increments in either were related to increases in trust. When errors were obvious, a significant interaction between the implicit and explicit measures was found, with those high in both having higher trust. Implicit attitudes have important implications for automation trust. Users may not be able to accurately report why they experience a given level of trust. To understand why users trust or fail to trust automation, measurements of implicit and explicit predictors may be necessary. Furthermore, implicit attitude toward automation might be used as a lever to effectively calibrate trust.

Automation-induced monitoring inefficiency: role of display location.

PubMed

Singh, I L; Molloy, R; Parasuraman, R

1997-01-01

Operators can be poor monitors of automation if they are engaged concurrently in other tasks. However, in previous studies of this phenomenon the automated task was always presented in the periphery, away from the primary manual tasks that were centrally displayed. In this study we examined whether centrally locating an automated task would boost monitoring performance during a flight-simulation task consisting of system monitoring, tracking and fuel resource management sub-tasks. Twelve nonpilot subjects were required to perform the tracking and fuel management tasks manually while watching the automated system monitoring task for occasional failures. The automation reliability was constant at 87.5% for six subjects and variable (alternating between 87.5% and 56.25%) for the other six subjects. Each subject completed four 30 min sessions over a period of 2 days. In each automation reliability condition the automation routine was disabled for the last 20 min of the fourth session in order to simulate catastrophic automation failure (0 % reliability). Monitoring for automation failure was inefficient when automation reliability was constant but not when it varied over time, replicating previous results. Furthermore, there was no evidence of resource or speed accuracy trade-off between tasks. Thus, automation-induced failures of monitoring cannot be prevented by centrally locating the automated task.

Automation-induced monitoring inefficiency: role of display location

NASA Technical Reports Server (NTRS)

Singh, I. L.; Molloy, R.; Parasuraman, R.

1997-01-01

Operators can be poor monitors of automation if they are engaged concurrently in other tasks. However, in previous studies of this phenomenon the automated task was always presented in the periphery, away from the primary manual tasks that were centrally displayed. In this study we examined whether centrally locating an automated task would boost monitoring performance during a flight-simulation task consisting of system monitoring, tracking and fuel resource management sub-tasks. Twelve nonpilot subjects were required to perform the tracking and fuel management tasks manually while watching the automated system monitoring task for occasional failures. The automation reliability was constant at 87.5% for six subjects and variable (alternating between 87.5% and 56.25%) for the other six subjects. Each subject completed four 30 min sessions over a period of 2 days. In each automation reliability condition the automation routine was disabled for the last 20 min of the fourth session in order to simulate catastrophic automation failure (0 % reliability). Monitoring for automation failure was inefficient when automation reliability was constant but not when it varied over time, replicating previous results. Furthermore, there was no evidence of resource or speed accuracy trade-off between tasks. Thus, automation-induced failures of monitoring cannot be prevented by centrally locating the automated task.

Brain tumour classification and abnormality detection using neuro-fuzzy technique and Otsu thresholding.

PubMed

Renjith, Arokia; Manjula, P; Mohan Kumar, P

2015-01-01

Brain tumour is one of the main causes for an increase in transience among children and adults. This paper proposes an improved method based on Magnetic Resonance Imaging (MRI) brain image classification and image segmentation approach. Automated classification is encouraged by the need of high accuracy when dealing with a human life. The detection of the brain tumour is a challenging problem, due to high diversity in tumour appearance and ambiguous tumour boundaries. MRI images are chosen for detection of brain tumours, as they are used in soft tissue determinations. First of all, image pre-processing is used to enhance the image quality. Second, dual-tree complex wavelet transform multi-scale decomposition is used to analyse texture of an image. Feature extraction extracts features from an image using gray-level co-occurrence matrix (GLCM). Then, the Neuro-Fuzzy technique is used to classify the stages of brain tumour as benign, malignant or normal based on texture features. Finally, tumour location is detected using Otsu thresholding. The classifier performance is evaluated based on classification accuracies. The simulated results show that the proposed classifier provides better accuracy than previous method.

Quantum Mechanics, Pattern Recognition, and the Mammalian Brain

NASA Astrophysics Data System (ADS)

Chapline, George

2008-10-01

Although the usual way of representing Markov processes is time asymmetric, there is a way of describing Markov processes, due to Schrodinger, which is time symmetric. This observation provides a link between quantum mechanics and the layered Bayesian networks that are often used in automated pattern recognition systems. In particular, there is a striking formal similarity between quantum mechanics and a particular type of Bayesian network, the Helmholtz machine, which provides a plausible model for how the mammalian brain recognizes important environmental situations. One interesting aspect of this relationship is that the "wake-sleep" algorithm for training a Helmholtz machine is very similar to the problem of finding the potential for the multi-channel Schrodinger equation. As a practical application of this insight it may be possible to use inverse scattering techniques to study the relationship between human brain wave patterns, pattern recognition, and learning. We also comment on whether there is a relationship between quantum measurements and consciousness.

Humans: still vital after all these years of automation.

PubMed

Parasuraman, Raja; Wickens, Christopher D

2008-06-01

The authors discuss empirical studies of human-automation interaction and their implications for automation design. Automation is prevalent in safety-critical systems and increasingly in everyday life. Many studies of human performance in automated systems have been conducted over the past 30 years. Developments in three areas are examined: levels and stages of automation, reliance on and compliance with automation, and adaptive automation. Automation applied to information analysis or decision-making functions leads to differential system performance benefits and costs that must be considered in choosing appropriate levels and stages of automation. Human user dependence on automated alerts and advisories reflects two components of operator trust, reliance and compliance, which are in turn determined by the threshold designers use to balance automation misses and false alarms. Finally, adaptive automation can provide additional benefits in balancing workload and maintaining the user's situation awareness, although more research is required to identify when adaptation should be user controlled or system driven. The past three decades of empirical research on humans and automation has provided a strong science base that can be used to guide the design of automated systems. This research can be applied to most current and future automated systems.

Automation in haemostasis.

PubMed

Huber, A R; Méndez, A; Brunner-Agten, S

2013-01-01

Automatia, an ancient Greece goddess of luck who makes things happen by themselves and on her own will without human engagement, is present in our daily life in the medical laboratory. Automation has been introduced and perfected by clinical chemistry and since then expanded into other fields such as haematology, immunology, molecular biology and also coagulation testing. The initial small and relatively simple standalone instruments have been replaced by more complex systems that allow for multitasking. Integration of automated coagulation testing into total laboratory automation has become possible in the most recent years. Automation has many strengths and opportunities if weaknesses and threats are respected. On the positive side, standardization, reduction of errors, reduction of cost and increase of throughput are clearly beneficial. Dependence on manufacturers, high initiation cost and somewhat expensive maintenance are less favourable factors. The modern lab and especially the todays lab technicians and academic personnel in the laboratory do not add value for the doctor and his patients by spending lots of time behind the machines. In the future the lab needs to contribute at the bedside suggesting laboratory testing and providing support and interpretation of the obtained results. The human factor will continue to play an important role in testing in haemostasis yet under different circumstances.

Robotics/Automated Systems Technicians.

ERIC Educational Resources Information Center

Doty, Charles R.

Major resources exist that can be used to develop or upgrade programs in community colleges and technical institutes that educate robotics/automated systems technicians. The first category of resources is Economic, Social, and Education Issues. The Office of Technology Assessment (OTA) report, "Automation and the Workplace," presents analyses of…

Evaluation of an Automated Keywording System.

ERIC Educational Resources Information Center

Malone, Linda C.; And Others

1990-01-01

Discussion of automated indexing techniques focuses on ways to statistically document improvements in the development of an automated keywording system over time. The system developed by the Joint Chiefs of Staff to automate the storage, categorization, and retrieval of information from military exercises is explained, and performance measures are…

SEGMA: An Automatic SEGMentation Approach for Human Brain MRI Using Sliding Window and Random Forests

PubMed Central

Serag, Ahmed; Wilkinson, Alastair G.; Telford, Emma J.; Pataky, Rozalia; Sparrow, Sarah A.; Anblagan, Devasuda; Macnaught, Gillian; Semple, Scott I.; Boardman, James P.

2017-01-01

Quantitative volumes from brain magnetic resonance imaging (MRI) acquired across the life course may be useful for investigating long term effects of risk and resilience factors for brain development and healthy aging, and for understanding early life determinants of adult brain structure. Therefore, there is an increasing need for automated segmentation tools that can be applied to images acquired at different life stages. We developed an automatic segmentation method for human brain MRI, where a sliding window approach and a multi-class random forest classifier were applied to high-dimensional feature vectors for accurate segmentation. The method performed well on brain MRI data acquired from 179 individuals, analyzed in three age groups: newborns (38–42 weeks gestational age), children and adolescents (4–17 years) and adults (35–71 years). As the method can learn from partially labeled datasets, it can be used to segment large-scale datasets efficiently. It could also be applied to different populations and imaging modalities across the life course. PMID:28163680

TuMore: generation of synthetic brain tumor MRI data for deep learning based segmentation approaches

NASA Astrophysics Data System (ADS)

Lindner, Lydia; Pfarrkirchner, Birgit; Gsaxner, Christina; Schmalstieg, Dieter; Egger, Jan

2018-03-01

Accurate segmentation and measurement of brain tumors plays an important role in clinical practice and research, as it is critical for treatment planning and monitoring of tumor growth. However, brain tumor segmentation is one of the most challenging tasks in medical image analysis. Since manual segmentations are subjective, time consuming and neither accurate nor reliable, there exists a need for objective, robust and fast automated segmentation methods that provide competitive performance. Therefore, deep learning based approaches are gaining interest in the field of medical image segmentation. When the training data set is large enough, deep learning approaches can be extremely effective, but in domains like medicine, only limited data is available in the majority of cases. Due to this reason, we propose a method that allows to create a large dataset of brain MRI (Magnetic Resonance Imaging) images containing synthetic brain tumors - glioblastomas more specifically - and the corresponding ground truth, that can be subsequently used to train deep neural networks.

Towards automated segmentation of cells and cell nuclei in nonlinear optical microscopy.

PubMed

Medyukhina, Anna; Meyer, Tobias; Schmitt, Michael; Romeike, Bernd F M; Dietzek, Benjamin; Popp, Jürgen

2012-11-01

Nonlinear optical (NLO) imaging techniques based e.g. on coherent anti-Stokes Raman scattering (CARS) or two photon excited fluorescence (TPEF) show great potential for biomedical imaging. In order to facilitate the diagnostic process based on NLO imaging, there is need for an automated calculation of quantitative values such as cell density, nucleus-to-cytoplasm ratio, average nuclear size. Extraction of these parameters is helpful for the histological assessment in general and specifically e.g. for the determination of tumor grades. This requires an accurate image segmentation and detection of locations and boundaries of cells and nuclei. Here we present an image processing approach for the detection of nuclei and cells in co-registered TPEF and CARS images. The algorithm developed utilizes the gray-scale information for the detection of the nuclei locations and the gradient information for the delineation of the nuclear and cellular boundaries. The approach reported is capable for an automated segmentation of cells and nuclei in multimodal TPEF-CARS images of human brain tumor samples. The results are important for the development of NLO microscopy into a clinically relevant diagnostic tool. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Work and Programmable Automation.

ERIC Educational Resources Information Center

DeVore, Paul W.

A new industrial era based on electronics and the microprocessor has arrived, an era that is being called intelligent automation. Intelligent automation, in the form of robots, replaces workers, and the new products, using microelectronic devices, require significantly less labor to produce than the goods they replace. The microprocessor thus…

Whole-Brain Mapping of Neuronal Activity in the Learned Helplessness Model of Depression.

PubMed

Kim, Yongsoo; Perova, Zinaida; Mirrione, Martine M; Pradhan, Kith; Henn, Fritz A; Shea, Stephen; Osten, Pavel; Li, Bo

2016-01-01

Some individuals are resilient, whereas others succumb to despair in repeated stressful situations. The neurobiological mechanisms underlying such divergent behavioral responses remain unclear. Here, we employed an automated method for mapping neuronal activity in search of signatures of stress responses in the entire mouse brain. We used serial two-photon tomography to detect expression of c-FosGFP - a marker of neuronal activation - in c-fosGFP transgenic mice subjected to the learned helplessness (LH) procedure, a widely used model of stress-induced depression-like phenotype in laboratory animals. We found that mice showing "helpless" behavior had an overall brain-wide reduction in the level of neuronal activation compared with mice showing "resilient" behavior, with the exception of a few brain areas, including the locus coeruleus, that were more activated in the helpless mice. In addition, the helpless mice showed a strong trend of having higher similarity in whole-brain activity profile among individuals, suggesting that helplessness is represented by a more stereotypic brain-wide activation pattern. This latter effect was confirmed in rats subjected to the LH procedure, using 2-deoxy-2[18F]fluoro-D-glucose positron emission tomography to assess neural activity. Our findings reveal distinct brain activity markings that correlate with adaptive and maladaptive behavioral responses to stress, and provide a framework for further studies investigating the contribution of specific brain regions to maladaptive stress responses.

Brain morphology in school-aged children with prenatal opioid exposure: A structural MRI study.

PubMed

Sirnes, Eivind; Oltedal, Leif; Bartsch, Hauke; Eide, Geir Egil; Elgen, Irene B; Aukland, Stein Magnus

Both animal and human studies have suggested that prenatal opioid exposure may be detrimental to the developing fetal brain. However, results are somewhat conflicting. Structural brain changes in children with prenatal opioid exposure have been reported in a few studies, and such changes may contribute to neuropsychological impairments observed in exposed children. To investigate the association between prenatal opioid exposure and brain morphology in school-aged children. A cross-sectional magnetic resonance imaging (MRI) study of prenatally opioid-exposed children and matched controls. A hospital-based sample (n=16) of children aged 10-14years with prenatal exposure to opioids and 1:1 sex- and age-matched unexposed controls. Automated brain volume measures obtained from T1-weighted MRI scans using FreeSurfer. Volumes of the basal ganglia, thalamus, and cerebellar white matter were reduced in the opioid-exposed group, whereas there were no statistically significant differences in global brain measures (total brain, cerebral cortex, and cerebral white matter volumes). In line with the limited findings reported in the literature to date, our study showed an association between prenatal opioid exposure and reduced regional brain volumes. Adverse effects of opioids on the developing fetal brain may explain this association. However, further research is needed to explore the causal nature and functional consequences of these findings. Copyright © 2017 Elsevier B.V. All rights reserved.

The BAARA (Biological AutomAted RAdiotracking) System: A New Approach in Ecological Field Studies

PubMed Central

Řeřucha, Šimon; Bartonička, Tomáš; Jedlička, Petr; Čížek, Martin; Hlouša, Ondřej; Lučan, Radek; Horáček, Ivan

2015-01-01

Radiotracking is an important and often the only possible method to explore specific habits and the behaviour of animals, but it has proven to be very demanding and time-consuming, especially when frequent positioning of a large group is required. Our aim was to address this issue by making the process partially automated, to mitigate the demands and related costs. This paper presents a novel automated tracking system that consists of a network of automated tracking stations deployed within the target area. Each station reads the signals from telemetry transmitters, estimates the bearing and distance of the tagged animals and records their position. The station is capable of tracking a theoretically unlimited number of transmitters on different frequency channels with the period of 5–15 seconds per single channel. An ordinary transmitter that fits within the supported frequency band might be used with BAARA (Biological AutomAted RAdiotracking); an extra option is the use of a custom-programmable transmitter with configurable operational parameters, such as the precise frequency channel or the transmission parameters. This new approach to a tracking system was tested for its applicability in a series of field and laboratory tests. BAARA has been tested within fieldwork explorations of Rousettus aegyptiacus during field trips to Dakhla oasis in Egypt. The results illustrate the novel perspective which automated radiotracking opens for the study of spatial behaviour, particularly in addressing topics in the domain of population ecology. PMID:25714910

Predictive validity of driving-simulator assessments following traumatic brain injury: a preliminary study.

PubMed

Lew, Henry L; Poole, John H; Lee, Eun Ha; Jaffe, David L; Huang, Hsiu-Chen; Brodd, Edward

2005-03-01

To evaluate whether driving simulator and road test evaluations can predict long-term driving performance, we conducted a prospective study on 11 patients with moderate to severe traumatic brain injury. Sixteen healthy subjects were also tested to provide normative values on the simulator at baseline. At their initial evaluation (time-1), subjects' driving skills were measured during a 30-minute simulator trial using an automated 12-measure Simulator Performance Index (SPI), while a trained observer also rated their performance using a Driving Performance Inventory (DPI). In addition, patients were evaluated on the road by a certified driving evaluator. Ten months later (time-2), family members observed patients driving for at least 3 hours over 4 weeks and rated their driving performance using the DPI. At time-1, patients were significantly impaired on automated SPI measures of driving skill, including: speed and steering control, accidents, and vigilance to a divided-attention task. These simulator indices significantly predicted the following aspects of observed driving performance at time-2: handling of automobile controls, regulation of vehicle speed and direction, higher-order judgment and self-control, as well as a trend-level association with car accidents. Automated measures of simulator skill (SPI) were more sensitive and accurate than observational measures of simulator skill (DPI) in predicting actual driving performance. To our surprise, the road test results at time-1 showed no significant relation to driving performance at time-2. Simulator-based assessment of patients with brain injuries can provide ecologically valid measures that, in some cases, may be more sensitive than a traditional road test as predictors of long-term driving performance in the community.

Understanding and avoiding potential problems in implementing automation

NASA Astrophysics Data System (ADS)

Rouse, W. B.; Morris, N. M.

1985-11-01

Technology-driven efforts to implement automation often encounter problems due to lack of acceptance or begrudging acceptance by the personnel involved. It is argued in this paper that the level of automation perceived by an individual heavily influences whether or not the automation is accepted by that individual. The factors that appear to affect perceived level of automation are discussed. Issues considered include the impact of automation on the system and the individual, correlates of acceptance, problems and risks of automation, and factors influencing alienation. Based on an understanding of these issues, a set of eight guidelines is proposed as a possible means of avoiding problems in implementing automation.

Understanding and avoiding potential problems in implementing automation

NASA Technical Reports Server (NTRS)

Rouse, W. B.; Morris, N. M.

1985-01-01

Technology-driven efforts to implement automation often encounter problems due to lack of acceptance or begrudging acceptance by the personnel involved. It is argued in this paper that the level of automation perceived by an individual heavily influences whether or not the automation is accepted by that individual. The factors that appear to affect perceived level of automation are discussed. Issues considered include the impact of automation on the system and the individual, correlates of acceptance, problems and risks of automation, and factors influencing alienation. Based on an understanding of these issues, a set of eight guidelines is proposed as a possible means of avoiding problems in implementing automation.

You're a What? Automation Technician

ERIC Educational Resources Information Center

Mullins, John

2010-01-01

Many people think of automation as laborsaving technology, but it sure keeps Jim Duffell busy. Defined simply, automation is a technique for making a device run or a process occur with minimal direct human intervention. But the functions and technologies involved in automated manufacturing are complex. Nearly all functions, from orders coming in…

Does Automated Feedback Improve Writing Quality?

ERIC Educational Resources Information Center

Wilson, Joshua; Olinghouse, Natalie G.; Andrada, Gilbert N.

2014-01-01

The current study examines data from students in grades 4-8 who participated in a statewide computer-based benchmark writing assessment that featured automated essay scoring and automated feedback. We examined whether the use of automated feedback was associated with gains in writing quality across revisions to an essay, and with transfer effects…

Clinical Laboratory Automation: A Case Study

PubMed Central

Archetti, Claudia; Montanelli, Alessandro; Finazzi, Dario; Caimi, Luigi; Garrafa, Emirena

2017-01-01

Background This paper presents a case study of an automated clinical laboratory in a large urban academic teaching hospital in the North of Italy, the Spedali Civili in Brescia, where four laboratories were merged in a unique laboratory through the introduction of laboratory automation. Materials and Methods The analysis compares the preautomation situation and the new setting from a cost perspective, by considering direct and indirect costs. It also presents an analysis of the turnaround time (TAT). The study considers equipment, staff and indirect costs. Results The introduction of automation led to a slight increase in equipment costs which is highly compensated by a remarkable decrease in staff costs. Consequently, total costs decreased by 12.55%. The analysis of the TAT shows an improvement of nonemergency exams while emergency exams are still validated within the maximum time imposed by the hospital. Conclusions The strategy adopted by the management, which was based on re-using the available equipment and staff when merging the pre-existing laboratories, has reached its goal: introducing automation while minimizing the costs. Significance for public health Automation is an emerging trend in modern clinical laboratories with a positive impact on service level to patients and on staff safety as shown by different studies. In fact, it allows process standardization which, in turn, decreases the frequency of outliers and errors. In addition, it induces faster processing times, thus improving the service level. On the other side, automation decreases the staff exposition to accidents strongly improving staff safety. In this study, we analyse a further potential benefit of automation, that is economic convenience. We study the case of the automated laboratory of one of the biggest hospital in Italy and compare the cost related to the pre and post automation situation. Introducing automation lead to a cost decrease without affecting the service level to patients

System reliability, performance and trust in adaptable automation.

PubMed

Chavaillaz, Alain; Wastell, David; Sauer, Jürgen

2016-01-01

The present study examined the effects of reduced system reliability on operator performance and automation management in an adaptable automation environment. 39 operators were randomly assigned to one of three experimental groups: low (60%), medium (80%), and high (100%) reliability of automation support. The support system provided five incremental levels of automation which operators could freely select according to their needs. After 3 h of training on a simulated process control task (AutoCAMS) in which the automation worked infallibly, operator performance and automation management were measured during a 2.5-h testing session. Trust and workload were also assessed through questionnaires. Results showed that although reduced system reliability resulted in lower levels of trust towards automation, there were no corresponding differences in the operators' reliance on automation. While operators showed overall a noteworthy ability to cope with automation failure, there were, however, decrements in diagnostic speed and prospective memory with lower reliability. Copyright © 2015. Published by Elsevier Ltd.

Automation, Manpower, and Education.

ERIC Educational Resources Information Center

Rosenberg, Jerry M.

Each group in our population will be affected by automation and other forms of technological advancement. This book seeks to identify the needs of these various groups, and to present ways in which educators can best meet them. The author corrects certain prevalent misconceptions concerning manpower utilization and automation. Based on the…

Funding for Library Automation.

ERIC Educational Resources Information Center

Thompson, Ronelle K. H.

This paper provides a brief overview of planning and implementing a project to fund library automation. It is suggested that: (1) proposal budgets should include all costs of a project, such as furniture needed for computer terminals, costs for modifying library procedures, initial supplies, or ongoing maintenance; (2) automation does not save…

Automated Power-Distribution System

NASA Technical Reports Server (NTRS)

Ashworth, Barry; Riedesel, Joel; Myers, Chris; Miller, William; Jones, Ellen F.; Freeman, Kenneth; Walsh, Richard; Walls, Bryan K.; Weeks, David J.; Bechtel, Robert T.

1992-01-01

Autonomous power-distribution system includes power-control equipment and automation equipment. System automatically schedules connection of power to loads and reconfigures itself when it detects fault. Potential terrestrial applications include optimization of consumption of power in homes, power supplies for autonomous land vehicles and vessels, and power supplies for automated industrial processes.

Movement Path Tortuosity Predicts Compliance With Therapeutic Behavioral Prompts in Patients With Traumatic Brain Injury.

PubMed

Kearns, William D; Fozard, James L; Ray, Roger D; Scott, Steven; Jasiewicz, Jan M; Craighead, Jeffrey D; Pagano, Craig V

2016-01-01

Rehabilitation of patients with traumatic brain injury typically includes therapeutic prompts for keeping appointments and adhering to medication regimens. Level of cognitive impairment may significantly affect a traumatic brain injury victim's ability to benefit from text-based prompting. We tested the hypothesis that spatial disorientation as measured by movement path tortuosity during ambulation would be associated with poorer compliance with automated prompts by veterans actively being treated for traumatic brain injury. Clinical polytrauma center. Ten (1 female) veteran patients mean age = 35.4 (SD = 12.4) years. Small group correlational study without random assignment. Fractal Dimension, a measure of movement path tortuosity derived from a GPS logging device used to record casual outdoor ambulation at the start of the study. Compliance with smart home machine-generated therapeutic prompts received during rehabilitation at the James A. Haley Veterans Administration Hospital Polytrauma Transitional Rehabilitation Program. A patient was compliant with a prompt if they transited from where the prompt was presented to the prescribed destination (both within the Polytrauma Transitional Rehabilitation Program) within 30 minutes. Noncompliance was failure to appear at the destination within the allotted time. Fractal dimension was significantly inversely related to overall prompt compliance (r = -0.603, n = 10, P = .032; 1-tailed). The findings support the hypothesis that increased spatial disorientation adversely impacts compliance with automated prompts throughout therapy. The results are consistent with previous studies linking elevated path tortuosity to cognitive impairment and increased risk for falls in assisted living facility residents.

Decision-Making for Automation: Hebrew and Arabic Script Materials in the Automated Library. Occasional Papers, Number 205.

ERIC Educational Resources Information Center

Vernon, Elizabeth

It is generally accepted in the library world that an automated catalog means more accessible data for patrons, greater productivity for librarians, and an improvement in the sharing of bibliographic data among libraries. While the desirability of automation is not a controversial issue, some aspects of automating remain problematic. This article…

Multistability of the Brain Network for Self-other Processing

PubMed Central

Chen, Yi-An; Huang, Tsung-Ren

2017-01-01

Early fMRI studies suggested that brain areas processing self-related and other-related information were highly overlapping. Hypothesising functional localisation of the cortex, researchers have tried to locate “self-specific” and “other-specific” regions within these overlapping areas by subtracting suspected confounding signals in task-based fMRI experiments. Inspired by recent advances in whole-brain dynamic modelling, we instead explored an alternative hypothesis that similar spatial activation patterns could be associated with different processing modes in the form of different synchronisation patterns. Combining an automated synthesis of fMRI data with a presumption-free diffusion spectrum image (DSI) fibre-tracking algorithm, we isolated a network putatively composed of brain areas and white matter tracts involved in self-other processing. We sampled synchronisation patterns from the dynamical systems of this network using various combinations of physiological parameters. Our results showed that the self-other processing network, with simulated gamma-band activity, tended to stabilise at a number of distinct synchronisation patterns. This phenomenon, termed “multistability,” could serve as an alternative model in theorising the mechanism of processing self-other information. PMID:28256520

Tensor-based morphometry of cannabis use on brain structure in individuals at elevated genetic risk of schizophrenia.

PubMed

Welch, K A; Moorhead, T W; McIntosh, A M; Owens, D G C; Johnstone, E C; Lawrie, S M

2013-10-01

Schizophrenia is associated with various brain structural abnormalities, including reduced volume of the hippocampi, prefrontal lobes and thalami. Cannabis use increases the risk of schizophrenia but reports of brain structural abnormalities in the cannabis-using population have not been consistent. We used automated image analysis to compare brain structural changes over time in people at elevated risk of schizophrenia for familial reasons who did and did not use cannabis. Magnetic resonance imaging (MRI) scans were obtained from subjects at high familial risk of schizophrenia at entry to the Edinburgh High Risk Study (EHRS) and approximately 2 years later. Differential grey matter (GM) loss in those exposed (n=23) and not exposed to cannabis (n=32) in the intervening period was compared using tensor-based morphometry (TBM). Cannabis exposure was associated with significantly greater loss of right anterior hippocampal (pcorrected=0.029, t=3.88) and left superior frontal lobe GM (pcorrected=0.026, t=4.68). The former finding remained significant even after the exclusion of individuals who had used other drugs during the inter-scan interval. Using an automated analysis of longitudinal data, we demonstrate an association between cannabis use and GM loss in currently well people at familial risk of developing schizophrenia. This observation may be important in understanding the link between cannabis exposure and the subsequent development of schizophrenia.

78 FR 53466 - Modification of Two National Customs Automation Program (NCAP) Tests Concerning Automated...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-29

... DEPARTMENT OF HOMELAND SECURITY U.S. Customs and Border Protection Modification of Two National Customs Automation Program (NCAP) Tests Concerning Automated Commercial Environment (ACE) Document Image System (DIS) and Simplified Entry (SE); Correction AGENCY: U.S. Customs and Border Protection, Department...

Mesoscale brain explorer, a flexible python-based image analysis and visualization tool.

PubMed

Haupt, Dirk; Vanni, Matthieu P; Bolanos, Federico; Mitelut, Catalin; LeDue, Jeffrey M; Murphy, Tim H

2017-07-01

Imaging of mesoscale brain activity is used to map interactions between brain regions. This work has benefited from the pioneering studies of Grinvald et al., who employed optical methods to image brain function by exploiting the properties of intrinsic optical signals and small molecule voltage-sensitive dyes. Mesoscale interareal brain imaging techniques have been advanced by cell targeted and selective recombinant indicators of neuronal activity. Spontaneous resting state activity is often collected during mesoscale imaging to provide the basis for mapping of connectivity relationships using correlation. However, the information content of mesoscale datasets is vast and is only superficially presented in manuscripts given the need to constrain measurements to a fixed set of frequencies, regions of interest, and other parameters. We describe a new open source tool written in python, termed mesoscale brain explorer (MBE), which provides an interface to process and explore these large datasets. The platform supports automated image processing pipelines with the ability to assess multiple trials and combine data from different animals. The tool provides functions for temporal filtering, averaging, and visualization of functional connectivity relations using time-dependent correlation. Here, we describe the tool and show applications, where previously published datasets were reanalyzed using MBE.

Automated telescope scheduling

NASA Technical Reports Server (NTRS)

Johnston, Mark D.

1988-01-01

With the ever increasing level of automation of astronomical telescopes the benefits and feasibility of automated planning and scheduling are becoming more apparent. Improved efficiency and increased overall telescope utilization are the most obvious goals. Automated scheduling at some level has been done for several satellite observatories, but the requirements on these systems were much less stringent than on modern ground or satellite observatories. The scheduling problem is particularly acute for Hubble Space Telescope: virtually all observations must be planned in excruciating detail weeks to months in advance. Space Telescope Science Institute has recently made significant progress on the scheduling problem by exploiting state-of-the-art artificial intelligence software technology. What is especially interesting is that this effort has already yielded software that is well suited to scheduling groundbased telescopes, including the problem of optimizing the coordinated scheduling of more than one telescope.

Greater Buyer Effectiveness through Automation

DTIC Science & Technology

1989-01-01

assignment to the buyer Coordination - automated routing of requirement package to technical, finance, transportation, packaging, small business ... security , data, safety, etc. Consolidation - automated identification of requirements for identical or similar items for potential consolidation

Order Division Automated System.

ERIC Educational Resources Information Center

Kniemeyer, Justin M.; And Others

This publication was prepared by the Order Division Automation Project staff to fulfill the Library of Congress' requirement to document all automation efforts. The report was originally intended for internal use only and not for distribution outside the Library. It is now felt that the library community at-large may have an interest in the…

Proof-of-concept automation of propellant processing

NASA Technical Reports Server (NTRS)

Ramohalli, Kumar; Schallhorn, P. A.

1989-01-01

For space-based propellant production, automation of the process is needed. Currently, all phases of terrestrial production have some form of human interaction. A mixer was acquired to help perform the tasks of automation. A heating system to be used with the mixer was designed, built, and installed. Tests performed on the heating system verify design criteria. An IBM PS/2 personal computer was acquired for the future automation work. It is hoped that some the mixing process itself will be automated. This is a concept demonstration task; proving that propellant production can be automated reliably.

Human-Centered Aviation Automation: Principles and Guidelines

NASA Technical Reports Server (NTRS)

Billings, Charles E.

1996-01-01

This document presents principles and guidelines for human-centered automation in aircraft and in the aviation system. Drawing upon operational experience with highly automated aircraft, it describes classes of problems that have occurred in these vehicles, the effects of advanced automation on the human operators of the aviation system, and ways in which these problems may be avoided in the design of future aircraft and air traffic management automation. Many incidents and a few serious accidents suggest that these problems are related to automation complexity, autonomy, coupling, and opacity, or inadequate feedback to operators. An automation philosophy that emphasizes improved communication, coordination and cooperation between the human and machine elements of this complex, distributed system is required to improve the safety and efficiency of aviation operations in the future.

Automated generation of massive image knowledge collections using Microsoft Live Labs Pivot to promote neuroimaging and translational research.

PubMed

Viangteeravat, Teeradache; Anyanwu, Matthew N; Ra Nagisetty, Venkateswara; Kuscu, Emin

2011-07-15

Massive datasets comprising high-resolution images, generated in neuro-imaging studies and in clinical imaging research, are increasingly challenging our ability to analyze, share, and filter such images in clinical and basic translational research. Pivot collection exploratory analysis provides each user the ability to fully interact with the massive amounts of visual data to fully facilitate sufficient sorting, flexibility and speed to fluidly access, explore or analyze the massive image data sets of high-resolution images and their associated meta information, such as neuro-imaging databases from the Allen Brain Atlas. It is used in clustering, filtering, data sharing and classifying of the visual data into various deep zoom levels and meta information categories to detect the underlying hidden pattern within the data set that has been used. We deployed prototype Pivot collections using the Linux CentOS running on the Apache web server. We also tested the prototype Pivot collections on other operating systems like Windows (the most common variants) and UNIX, etc. It is demonstrated that the approach yields very good results when compared with other approaches used by some researchers for generation, creation, and clustering of massive image collections such as the coronal and horizontal sections of the mouse brain from the Allen Brain Atlas. Pivot visual analytics was used to analyze a prototype of dataset Dab2 co-expressed genes from the Allen Brain Atlas. The metadata along with high-resolution images were automatically extracted using the Allen Brain Atlas API. It is then used to identify the hidden information based on the various categories and conditions applied by using options generated from automated collection. A metadata category like chromosome, as well as data for individual cases like sex, age, and plan attributes of a particular gene, is used to filter, sort and to determine if there exist other genes with a similar characteristics to Dab2. And

Automated generation of massive image knowledge collections using Microsoft Live Labs Pivot to promote neuroimaging and translational research

PubMed Central

2011-01-01

Background Massive datasets comprising high-resolution images, generated in neuro-imaging studies and in clinical imaging research, are increasingly challenging our ability to analyze, share, and filter such images in clinical and basic translational research. Pivot collection exploratory analysis provides each user the ability to fully interact with the massive amounts of visual data to fully facilitate sufficient sorting, flexibility and speed to fluidly access, explore or analyze the massive image data sets of high-resolution images and their associated meta information, such as neuro-imaging databases from the Allen Brain Atlas. It is used in clustering, filtering, data sharing and classifying of the visual data into various deep zoom levels and meta information categories to detect the underlying hidden pattern within the data set that has been used. Method We deployed prototype Pivot collections using the Linux CentOS running on the Apache web server. We also tested the prototype Pivot collections on other operating systems like Windows (the most common variants) and UNIX, etc. It is demonstrated that the approach yields very good results when compared with other approaches used by some researchers for generation, creation, and clustering of massive image collections such as the coronal and horizontal sections of the mouse brain from the Allen Brain Atlas. Results Pivot visual analytics was used to analyze a prototype of dataset Dab2 co-expressed genes from the Allen Brain Atlas. The metadata along with high-resolution images were automatically extracted using the Allen Brain Atlas API. It is then used to identify the hidden information based on the various categories and conditions applied by using options generated from automated collection. A metadata category like chromosome, as well as data for individual cases like sex, age, and plan attributes of a particular gene, is used to filter, sort and to determine if there exist other genes with a similar

PREDICTING APHASIA TYPE FROM BRAIN DAMAGE MEASURED WITH STRUCTURAL MRI

PubMed Central

Yourganov, Grigori; Smith, Kimberly G.; Fridriksson, Julius; Rorden, Chris

2015-01-01

Chronic aphasia is a common consequence of a left-hemisphere stroke. Since the early insights by Broca and Wernicke, studying the relationship between the loci of cortical damage and patterns of language impairment has been one of the concerns of aphasiology. We utilized multivariate classification in a cross-validation framework to predict the type of chronic aphasia from the spatial pattern of brain damage. Our sample consisted of 98 patients with five types of aphasia (Broca’s, Wernicke’s, global, conduction, and anomic), classified based on scores on the Western Aphasia Battery. Binary lesion maps were obtained from structural MRI scans (obtained at least 6 months poststroke, and within 2 days of behavioural assessment); after spatial normalization, the lesions were parcellated into a disjoint set of brain areas. The proportion of damage to the brain areas was used to classify patients’ aphasia type. To create this parcellation, we relied on five brain atlases; our classifier (support vector machine) could differentiate between different kinds of aphasia using any of the five parcellations. In our sample, the best classification accuracy was obtained when using a novel parcellation that combined two previously published brain atlases, with the first atlas providing the segmentation of grey matter, and the second atlas used to segment the white matter. For each aphasia type, we computed the relative importance of different brain areas for distinguishing it from other aphasia types; our findings were consistent with previously published reports of lesion locations implicated in different types of aphasia. Overall, our results revealed that automated multivariate classification could distinguish between aphasia types based on damage to atlas-defined brain areas. PMID:26465238

Predicting aphasia type from brain damage measured with structural MRI.

PubMed

Yourganov, Grigori; Smith, Kimberly G; Fridriksson, Julius; Rorden, Chris

2015-12-01

Chronic aphasia is a common consequence of a left-hemisphere stroke. Since the early insights by Broca and Wernicke, studying the relationship between the loci of cortical damage and patterns of language impairment has been one of the concerns of aphasiology. We utilized multivariate classification in a cross-validation framework to predict the type of chronic aphasia from the spatial pattern of brain damage. Our sample consisted of 98 patients with five types of aphasia (Broca's, Wernicke's, global, conduction, and anomic), classified based on scores on the Western Aphasia Battery (WAB). Binary lesion maps were obtained from structural MRI scans (obtained at least 6 months poststroke, and within 2 days of behavioural assessment); after spatial normalization, the lesions were parcellated into a disjoint set of brain areas. The proportion of damage to the brain areas was used to classify patients' aphasia type. To create this parcellation, we relied on five brain atlases; our classifier (support vector machine - SVM) could differentiate between different kinds of aphasia using any of the five parcellations. In our sample, the best classification accuracy was obtained when using a novel parcellation that combined two previously published brain atlases, with the first atlas providing the segmentation of grey matter, and the second atlas used to segment the white matter. For each aphasia type, we computed the relative importance of different brain areas for distinguishing it from other aphasia types; our findings were consistent with previously published reports of lesion locations implicated in different types of aphasia. Overall, our results revealed that automated multivariate classification could distinguish between aphasia types based on damage to atlas-defined brain areas. Copyright © 2015 Elsevier Ltd. All rights reserved.

Automated Test-Form Generation

ERIC Educational Resources Information Center

van der Linden, Wim J.; Diao, Qi

2011-01-01

In automated test assembly (ATA), the methodology of mixed-integer programming is used to select test items from an item bank to meet the specifications for a desired test form and optimize its measurement accuracy. The same methodology can be used to automate the formatting of the set of selected items into the actual test form. Three different…

76 FR 34246 - Automated Commercial Environment (ACE); Announcement of National Customs Automation Program Test...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-13

... CBP with authority to conduct limited test programs or procedures designed to evaluate planned... aspects of this test, including the design, conduct and implementation of the test, in order to determine... Environment (ACE); Announcement of National Customs Automation Program Test of Automated Procedures for In...

Automation Applications in an Advanced Air Traffic Management System : Volume 4B. Automation Requirements (Concluded)

DOT National Transportation Integrated Search

1974-08-01

Volume 4 describes the automation requirements. A presentation of automation requirements is made for an advanced air traffic management system in terms of controller work for-e, computer resources, controller productivity, system manning, failure ef...

Individual differences in the calibration of trust in automation.

PubMed

Pop, Vlad L; Shrewsbury, Alex; Durso, Francis T

2015-06-01

The objective was to determine whether operators with an expectancy that automation is trustworthy are better at calibrating their trust to changes in the capabilities of automation, and if so, why. Studies suggest that individual differences in automation expectancy may be able to account for why changes in the capabilities of automation lead to a substantial change in trust for some, yet only a small change for others. In a baggage screening task, 225 participants searched for weapons in 200 X-ray images of luggage. Participants were assisted by an automated decision aid exhibiting different levels of reliability. Measures of expectancy that automation is trustworthy were used in conjunction with subjective measures of trust and perceived reliability to identify individual differences in trust calibration. Operators with high expectancy that automation is trustworthy were more sensitive to changes (both increases and decreases) in automation reliability. This difference was eliminated by manipulating the causal attribution of automation errors. Attributing the cause of automation errors to factors external to the automation fosters an understanding of tasks and situations in which automation differs in reliability and may lead to more appropriate trust. The development of interventions can lead to calibrated trust in automation. © 2014, Human Factors and Ergonomics Society.

Space station automation study-satellite servicing, volume 2

NASA Technical Reports Server (NTRS)

Meissinger, H. F.

1984-01-01

Technology requirements for automated satellite servicing operations aboard the NASA space station were studied. The three major tasks addressed: (1) servicing requirements (satellite and space station elements) and the role of automation; (2) assessment of automation technology; and (3) conceptual design of servicing facilities on the space station. It is found that many servicing functions cloud benefit from automation support; and the certain research and development activities on automation technologies for servicing should start as soon as possible. Also, some advanced automation developments for orbital servicing could be effectively applied to U.S. industrial ground based operations.

Automated 4D analysis of dendritic spine morphology: applications to stimulus-induced spine remodeling and pharmacological rescue in a disease model

PubMed Central

2011-01-01

Uncovering the mechanisms that regulate dendritic spine morphology has been limited, in part, by the lack of efficient and unbiased methods for analyzing spines. Here, we describe an automated 3D spine morphometry method and its application to spine remodeling in live neurons and spine abnormalities in a disease model. We anticipate that this approach will advance studies of synapse structure and function in brain development, plasticity, and disease. PMID:21982080

Efficacy Evaluation of Different Wavelet Feature Extraction Methods on Brain MRI Tumor Detection

NASA Astrophysics Data System (ADS)

Nabizadeh, Nooshin; John, Nigel; Kubat, Miroslav

2014-03-01

Automated Magnetic Resonance Imaging brain tumor detection and segmentation is a challenging task. Among different available methods, feature-based methods are very dominant. While many feature extraction techniques have been employed, it is still not quite clear which of feature extraction methods should be preferred. To help improve the situation, we present the results of a study in which we evaluate the efficiency of using different wavelet transform features extraction methods in brain MRI abnormality detection. Applying T1-weighted brain image, Discrete Wavelet Transform (DWT), Discrete Wavelet Packet Transform (DWPT), Dual Tree Complex Wavelet Transform (DTCWT), and Complex Morlet Wavelet Transform (CMWT) methods are applied to construct the feature pool. Three various classifiers as Support Vector Machine, K Nearest Neighborhood, and Sparse Representation-Based Classifier are applied and compared for classifying the selected features. The results show that DTCWT and CMWT features classified with SVM, result in the highest classification accuracy, proving of capability of wavelet transform features to be informative in this application.

An Intelligent Systems Approach to Automated Object Recognition: A Preliminary Study

USGS Publications Warehouse

Maddox, Brian G.; Swadley, Casey L.

2002-01-01

Attempts at fully automated object recognition systems have met with varying levels of success over the years. However, none of the systems have achieved high enough accuracy rates to be run unattended. One of the reasons for this may be that they are designed from the computer's point of view and rely mainly on image-processing methods. A better solution to this problem may be to make use of modern advances in computational intelligence and distributed processing to try to mimic how the human brain is thought to recognize objects. As humans combine cognitive processes with detection techniques, such a system would combine traditional image-processing techniques with computer-based intelligence to determine the identity of various objects in a scene.

Cooperative Mapping for Automated Vehicles

DOT National Transportation Integrated Search

2017-10-01

Localization is essential for automated vehicles, even for simple tasks such as lanekeeping. Some automated vehicle systems use their sensors to perceive their surroundings on-the-fly, such as the early variants of the Tesla Autopilot, while others s...

FRIEND: a brain-monitoring agent for adaptive and assistive systems.

PubMed

Morris, Alexis; Ulieru, Mihaela

2012-01-01

This paper presents an architectural design for adaptive-systems agents (FRIEND) that use brain state information to make more effective decisions on behalf of a user; measuring brain context versus situational demands. These systems could be useful for alerting users to cognitive workload levels or fatigue, and could attempt to compensate for higher cognitive activity by filtering noise information. In some cases such systems could also share control of devices, such as pulling over in an automated vehicle. These aim to assist people in everyday systems to perform tasks better and be more aware of internal states. Achieving a functioning system of this sort is a challenge, involving a unification of brain- computer-interfaces, human-computer-interaction, soft-computin deliberative multi-agent systems disciplines. Until recently, these were not able to be combined into a usable platform due largely to technological limitations (e.g., size, cost, and processing speed), insufficient research on extracting behavioral states from EEG signals, and lack of low-cost wireless sensing headsets. We aim to surpass these limitations and develop control architectures for making sense of brain state in applications by realizing an agent architecture for adaptive (human-aware) technology. In this paper we present an early, high-level design towards implementing a multi-purpose brain-monitoring agent system to improve user quality of life through the assistive applications of psycho-physiological monitoring, noise-filtering, and shared system control.

Effects of non-neuronal components for functional connectivity analysis from resting-state functional MRI toward automated diagnosis of schizophrenia

NASA Astrophysics Data System (ADS)

Kim, Junghoe; Lee, Jong-Hwan

2014-03-01

A functional connectivity (FC) analysis from resting-state functional MRI (rsfMRI) is gaining its popularity toward the clinical application such as diagnosis of neuropsychiatric disease. To delineate the brain networks from rsfMRI data, non-neuronal components including head motions and physiological artifacts mainly observed in cerebrospinal fluid (CSF), white matter (WM) along with a global brain signal have been regarded as nuisance variables in calculating the FC level. However, it is still unclear how the non-neuronal components can affect the performance toward diagnosis of neuropsychiatric disease. In this study, a systematic comparison of classification performance of schizophrenia patients was provided employing the partial correlation coefficients (CCs) as feature elements. Pair-wise partial CCs were calculated between brain regions, in which six combinatorial sets of nuisance variables were considered. The partial CCs were used as candidate feature elements followed by feature selection based on the statistical significance test between two groups in the training set. Once a linear support vector machine was trained using the selected features from the training set, the classification performance was evaluated using the features from the test set (i.e. leaveone- out cross validation scheme). From the results, the error rate using all non-neuronal components as nuisance variables (12.4%) was significantly lower than those using remaining combination of non-neuronal components as nuisance variables (13.8 ~ 20.0%). In conclusion, the non-neuronal components substantially degraded the automated diagnosis performance, which supports our hypothesis that the non-neuronal components are crucial in controlling the automated diagnosis performance of the neuropsychiatric disease using an fMRI modality.

Human-centered automation: Development of a philosophy

NASA Technical Reports Server (NTRS)

Graeber, Curtis; Billings, Charles E.

1990-01-01

Information on human-centered automation philosophy is given in outline/viewgraph form. It is asserted that automation of aircraft control will continue in the future, but that automation should supplement, not supplant the human management and control function in civil air transport.

Automation--down to the nuts and bolts.

PubMed

Fix, R J; Rowe, J M; McConnell, B C

2000-01-01

Laboratories that once viewed automation as an expensive luxury are now looking to automation as a solution to increase sample throughput, to help ensure data integrity and to improve laboratory safety. The question is no longer, 'Should we automate?', but 'How should we approach automation?' A laboratory may choose from three approaches when deciding to automate: (1) contract with a third party vendor to produce a turnkey system, (2) develop and fabricate the system in-house or (3) some combination of approaches (1) and (2). The best approach for a given laboratory depends upon its available resources. The first lesson to be learned in automation is that no matter how straightforward an idea appears in the beginning, the solution will not be realized until many complex problems have been resolved. Issues dealing with sample vessel manipulation, liquid handling and system control must be addressed before a final design can be developed. This requires expertise in engineering, electronics, programming and chemistry. Therefore, the team concept of automation should be employed to help ensure success. This presentation discusses the advantages and disadvantages of the three approaches to automation. The development of an automated sample handling and control system for the STAR System focused microwave will be used to illustrate the complexities encountered in a seemingly simple project, and to highlight the importance of the team concept to automation no matter which approach is taken. The STAR System focused microwave from CEM Corporation is an open vessel digestion system with six microwave cells. This system is used to prepare samples for trace metal determination. The automated sample handling was developed around a XYZ motorized gantry system. Grippers were specially designed to perform several different functions and to provide feedback to the control software. Software was written in Visual Basic 5.0 to control the movement of the samples and the operation and

Progress in Fully Automated Abdominal CT Interpretation

PubMed Central

Summers, Ronald M.

2016-01-01

OBJECTIVE Automated analysis of abdominal CT has advanced markedly over just the last few years. Fully automated assessment of organs, lymph nodes, adipose tissue, muscle, bowel, spine, and tumors are some examples where tremendous progress has been made. Computer-aided detection of lesions has also improved dramatically. CONCLUSION This article reviews the progress and provides insights into what is in store in the near future for automated analysis for abdominal CT, ultimately leading to fully automated interpretation. PMID:27101207

The need for improved brain lesion segmentation techniques for children with cerebral palsy: A review.

PubMed

Pagnozzi, Alex M; Gal, Yaniv; Boyd, Roslyn N; Fiori, Simona; Fripp, Jurgen; Rose, Stephen; Dowson, Nicholas

2015-12-01

Cerebral palsy (CP) describes a group of permanent disorders of posture and movement caused by disturbances in the developing brain. Accurate diagnosis and prognosis, in terms of motor type and severity, is difficult to obtain due to the heterogeneous appearance of brain injury and large anatomical distortions commonly observed in children with CP. There is a need to optimise treatment strategies for individual patients in order to lead to lifelong improvements in function and capabilities. Magnetic resonance imaging (MRI) is critical to non-invasively visualizing brain lesions, and is currently used to assist the diagnosis and qualitative classification in CP patients. Although such qualitative approaches under-utilise available data, the quantification of MRIs is not automated and therefore not widely performed in clinical assessment. Automated brain lesion segmentation techniques are necessary to provide valid and reproducible quantifications of injury. Such techniques have been used to study other neurological disorders, however the technical challenges unique to CP mean that existing algorithms require modification to be sufficiently reliable, and therefore have not been widely applied to MRIs of children with CP. In this paper, we present a review of a subset of available brain injury segmentation approaches that could be applied to CP, including the detection of cortical malformations, white and grey matter lesions and ventricular enlargement. Following a discussion of strengths and weaknesses, we suggest areas of future research in applying segmentation techniques to the MRI of children with CP. Specifically, we identify atlas-based priors to be ineffective in regions of substantial malformations, instead propose relying on adaptive, spatially consistent algorithms, with fast initialisation mechanisms to provide additional robustness to injury. We also identify several cortical shape parameters that could be used to identify cortical injury, and shape

A system-level approach to automation research

NASA Technical Reports Server (NTRS)

Harrison, F. W.; Orlando, N. E.

1984-01-01

Automation is the application of self-regulating mechanical and electronic devices to processes that can be accomplished with the human organs of perception, decision, and actuation. The successful application of automation to a system process should reduce man/system interaction and the perceived complexity of the system, or should increase affordability, productivity, quality control, and safety. The expense, time constraints, and risk factors associated with extravehicular activities have led the Automation Technology Branch (ATB), as part of the NASA Automation Research and Technology Program, to investigate the use of robots and teleoperators as automation aids in the context of space operations. The ATB program addresses three major areas: (1) basic research in autonomous operations, (2) human factors research on man-machine interfaces with remote systems, and (3) the integration and analysis of automated systems. This paper reviews the current ATB research in the area of robotics and teleoperators.

OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI.

PubMed

Sweeney, Elizabeth M; Shinohara, Russell T; Shiee, Navid; Mateen, Farrah J; Chudgar, Avni A; Cuzzocreo, Jennifer L; Calabresi, Peter A; Pham, Dzung L; Reich, Daniel S; Crainiceanu, Ciprian M

2013-01-01

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1% and below of 0.59% (95% CI; [0.50%, 0.67%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74% (95% CI: [65%, 82%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77% (95% CI: [71%, 83%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76% (95% CI: [64%, 88%]) of cases, the neurologist 66% (95% CI: [52%, 78

Aberrant brain stem morphometry associated with sleep disturbance in drug-naïve subjects with Alzheimer's disease.

PubMed

Lee, Ji Han; Jung, Won Sang; Choi, Woo Hee; Lim, Hyun Kook

2016-01-01

Among patients with Alzheimer's disease (AD), sleep disturbances are common and serious noncognitive symptoms. Previous studies of AD patients have identified deformations in the brain stem, which may play an important role in the regulation of sleep. The aim of this study was to further investigate the relationship between sleep disturbances and alterations in brain stem morphology in AD. In 44 patients with AD and 40 healthy elderly controls, sleep disturbances were measured using the Neuropsychiatry Inventory sleep subscale. We employed magnetic resonance imaging-based automated segmentation tools to examine the relationship between sleep disturbances and changes in brain stem morphology. Analyses of the data from AD subjects revealed significant correlations between the Neuropsychiatry Inventory sleep-subscale scores and structural alterations in the left posterior lateral region of the brain stem, as well as normalized brain stem volumes. In addition, significant group differences in posterior brain stem morphology were observed between the AD group and the control group. This study is the first to analyze an association between sleep disturbances and brain stem morphology in AD. In line with previous findings, this study lends support to the possibility that brain stem structural abnormalities might be important neurobiological mechanisms underlying sleep disturbances associated with AD. Further longitudinal research is needed to confirm these findings.

Automation's Effect on Library Personnel.

ERIC Educational Resources Information Center

Dakshinamurti, Ganga

1985-01-01

Reports on survey studying the human-machine interface in Canadian university, public, and special libraries. Highlights include position category and educational background of 118 participants, participants' feelings toward automation, physical effects of automation, diffusion in decision making, interpersonal communication, future trends,…

Specimen coordinate automated measuring machine/fiducial automated measuring machine

DOEpatents

Hedglen, Robert E.; Jacket, Howard S.; Schwartz, Allan I.

1991-01-01

The Specimen coordinate Automated Measuring Machine (SCAMM) and the Fiducial Automated Measuring Machine (FAMM) is a computer controlled metrology system capable of measuring length, width, and thickness, and of locating fiducial marks. SCAMM and FAMM have many similarities in their designs, and they can be converted from one to the other without taking them out of the hot cell. Both have means for: supporting a plurality of samples and a standard; controlling the movement of the samples in the +/- X and Y directions; determining the coordinates of the sample; compensating for temperature effects; and verifying the accuracy of the measurements and repeating as necessary. SCAMM and FAMM are designed to be used in hot cells.

3D-Printed Microfluidic Automation

PubMed Central

Au, Anthony K.; Bhattacharjee, Nirveek; Horowitz, Lisa F.; Chang, Tim C.; Folch, Albert

2015-01-01

Microfluidic automation – the automated routing, dispensing, mixing, and/or separation of fluids through microchannels – generally remains a slowly-spreading technology because device fabrication requires sophisticated facilities and the technology’s use demands expert operators. Integrating microfluidic automation in devices has involved specialized multi-layering and bonding approaches. Stereolithography is an assembly-free, 3D-printing technique that is emerging as an efficient alternative for rapid prototyping of biomedical devices. Here we describe fluidic valves and pumps that can be stereolithographically printed in optically-clear, biocompatible plastic and integrated within microfluidic devices at low cost. User-friendly fluid automation devices can be printed and used by non-engineers as replacement for costly robotic pipettors or tedious manual pipetting. Engineers can manipulate the designs as digital modules into new devices of expanded functionality. Printing these devices only requires the digital file and electronic access to a printer. PMID:25738695

3D-printed microfluidic automation.

PubMed

Au, Anthony K; Bhattacharjee, Nirveek; Horowitz, Lisa F; Chang, Tim C; Folch, Albert

2015-04-21

Microfluidic automation - the automated routing, dispensing, mixing, and/or separation of fluids through microchannels - generally remains a slowly-spreading technology because device fabrication requires sophisticated facilities and the technology's use demands expert operators. Integrating microfluidic automation in devices has involved specialized multi-layering and bonding approaches. Stereolithography is an assembly-free, 3D-printing technique that is emerging as an efficient alternative for rapid prototyping of biomedical devices. Here we describe fluidic valves and pumps that can be stereolithographically printed in optically-clear, biocompatible plastic and integrated within microfluidic devices at low cost. User-friendly fluid automation devices can be printed and used by non-engineers as replacement for costly robotic pipettors or tedious manual pipetting. Engineers can manipulate the designs as digital modules into new devices of expanded functionality. Printing these devices only requires the digital file and electronic access to a printer.

Library Automation in the Netherlands and Pica.

ERIC Educational Resources Information Center

Bossers, Anton; Van Muyen, Martin

1984-01-01

Describes the Pica Library Automation Network (originally the Project for Integrated Catalogue Automation), which is based on a centralized bibliographic database. Highlights include the Pica conception of library automation, online shared cataloging system, circulation control system, acquisition system, and online Dutch union catalog with…

Brain size regulations by cbp haploinsufficiency evaluated by in-vivo MRI based volumetry

NASA Astrophysics Data System (ADS)

Ateca-Cabarga, Juan C.; Cosa, Alejandro; Pallarés, Vicente; López-Atalaya, José P.; Barco, Ángel; Canals, Santiago; Moratal, David

2015-11-01

The Rubinstein-Taybi Syndrome (RSTS) is a congenital disease that affects brain development causing severe cognitive deficits. In most cases the disease is associated with dominant mutations in the gene encoding the CREB binding protein (CBP). In this work, we present the first quantitative analysis of brain abnormalities in a mouse model of RSTS using magnetic resonance imaging (MRI) and two novel self-developed automated algorithms for image volumetric analysis. Our results quantitatively confirm key syndromic features observed in RSTS patients, such as reductions in brain size (-16.31%, p < 0.05), white matter volume (-16.00%, p < 0.05), and corpus callosum (-12.40%, p < 0.05). Furthermore, they provide new insight into the developmental origin of the disease. By comparing brain tissues in a region by region basis between cbp+/- and cbp+/+ littermates, we found that cbp haploinsufficiency is specifically associated with significant reductions in prosencephalic tissue, such us in the olfactory bulb and neocortex, whereas regions evolved from the embryonic rhombencephalon were spared. Despite the large volume reductions, the proportion between gray-, white-matter and cerebrospinal fluid were conserved, suggesting a role of CBP in brain size regulation. The commonalities with holoprosencephaly and arhinencephaly conditions suggest the inclusion of RSTS in the family of neuronal migration disorders.

Automation of the longwall mining system

NASA Technical Reports Server (NTRS)

Zimmerman, W.; Aster, R. W.; Harris, J.; High, J.

1982-01-01

Cost effective, safe, and technologically sound applications of automation technology to underground coal mining were identified. The longwall analysis commenced with a general search for government and industry experience of mining automation technology. A brief industry survey was conducted to identify longwall operational, safety, and design problems. The prime automation candidates resulting from the industry experience and survey were: (1) the shearer operation, (2) shield and conveyor pan line advance, (3) a management information system to allow improved mine logistics support, and (4) component fault isolation and diagnostics to reduce untimely maintenance delays. A system network analysis indicated that a 40% improvement in productivity was feasible if system delays associated with all of the above four areas were removed. A technology assessment and conceptual system design of each of the four automation candidate areas showed that state of the art digital computer, servomechanism, and actuator technologies could be applied to automate the longwall system.

Distribution automation applications of fiber optics

NASA Technical Reports Server (NTRS)

Kirkham, Harold; Johnston, A.; Friend, H.

1989-01-01

Motivations for interest and research in distribution automation are discussed. The communication requirements of distribution automation are examined and shown to exceed the capabilities of power line carrier, radio, and telephone systems. A fiber optic based communication system is described that is co-located with the distribution system and that could satisfy the data rate and reliability requirements. A cost comparison shows that it could be constructed at a cost that is similar to that of a power line carrier system. The requirements for fiber optic sensors for distribution automation are discussed. The design of a data link suitable for optically-powered electronic sensing is presented. Empirical results are given. A modeling technique that was used to understand the reflections of guided light from a variety of surfaces is described. An optical position-indicator design is discussed. Systems aspects of distribution automation are discussed, in particular, the lack of interface, communications, and data standards. The economics of distribution automation are examined.

Whole-Brain Mapping of Neuronal Activity in the Learned Helplessness Model of Depression

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Yongsoo; Perova, Zinaida; Mirrione, Martine M.

Some individuals are resilient, whereas others succumb to despair in repeated stressful situations. The neurobiological mechanisms underlying such divergent behavioral responses remain unclear. Here, we employed an automated method for mapping neuronal activity in search of signatures of stress responses in the entire mouse brain. We used serial two-photon tomography to detect expression of c-FosGFP – a marker of neuronal activation – in c-fosGFP transgenic mice subjected to the learned helplessness (LH) procedure, a widely used model of stress-induced depression-like phenotype in laboratory animals. We found that mice showing “helpless” behavior had an overall brain-wide reduction in the level ofmore » neuronal activation compared with mice showing “resilient” behavior, with the exception of a few brain areas, including the locus coeruleus, that were more activated in the helpless mice. In addition, the helpless mice showed a strong trend of having higher similarity in whole-brain activity profile among individuals, suggesting that helplessness is represented by a more stereotypic brain-wide activation pattern. This latter effect was confirmed in rats subjected to the LH procedure, using 2-deoxy-2[18F]fluoro-D-glucose positron emission tomography to assess neural activity. In conclusion, our findings reveal distinct brain activity markings that correlate with adaptive and maladaptive behavioral responses tostress, and provide a framework for further studies investigating the contribution of specific brain regions to maladaptive stress responses.« less

Whole-Brain Mapping of Neuronal Activity in the Learned Helplessness Model of Depression

PubMed Central

Kim, Yongsoo; Perova, Zinaida; Mirrione, Martine M.; Pradhan, Kith; Henn, Fritz A.; Shea, Stephen; Osten, Pavel; Li, Bo

2016-01-01

Some individuals are resilient, whereas others succumb to despair in repeated stressful situations. The neurobiological mechanisms underlying such divergent behavioral responses remain unclear. Here, we employed an automated method for mapping neuronal activity in search of signatures of stress responses in the entire mouse brain. We used serial two-photon tomography to detect expression of c-FosGFP – a marker of neuronal activation – in c-fosGFP transgenic mice subjected to the learned helplessness (LH) procedure, a widely used model of stress-induced depression-like phenotype in laboratory animals. We found that mice showing “helpless” behavior had an overall brain-wide reduction in the level of neuronal activation compared with mice showing “resilient” behavior, with the exception of a few brain areas, including the locus coeruleus, that were more activated in the helpless mice. In addition, the helpless mice showed a strong trend of having higher similarity in whole-brain activity profile among individuals, suggesting that helplessness is represented by a more stereotypic brain-wide activation pattern. This latter effect was confirmed in rats subjected to the LH procedure, using 2-deoxy-2[18F]fluoro-D-glucose positron emission tomography to assess neural activity. Our findings reveal distinct brain activity markings that correlate with adaptive and maladaptive behavioral responses to stress, and provide a framework for further studies investigating the contribution of specific brain regions to maladaptive stress responses. PMID:26869888

Whole-Brain Mapping of Neuronal Activity in the Learned Helplessness Model of Depression

DOE PAGES

Kim, Yongsoo; Perova, Zinaida; Mirrione, Martine M.; ...

2016-02-03

Some individuals are resilient, whereas others succumb to despair in repeated stressful situations. The neurobiological mechanisms underlying such divergent behavioral responses remain unclear. Here, we employed an automated method for mapping neuronal activity in search of signatures of stress responses in the entire mouse brain. We used serial two-photon tomography to detect expression of c-FosGFP – a marker of neuronal activation – in c-fosGFP transgenic mice subjected to the learned helplessness (LH) procedure, a widely used model of stress-induced depression-like phenotype in laboratory animals. We found that mice showing “helpless” behavior had an overall brain-wide reduction in the level ofmore » neuronal activation compared with mice showing “resilient” behavior, with the exception of a few brain areas, including the locus coeruleus, that were more activated in the helpless mice. In addition, the helpless mice showed a strong trend of having higher similarity in whole-brain activity profile among individuals, suggesting that helplessness is represented by a more stereotypic brain-wide activation pattern. This latter effect was confirmed in rats subjected to the LH procedure, using 2-deoxy-2[18F]fluoro-D-glucose positron emission tomography to assess neural activity. In conclusion, our findings reveal distinct brain activity markings that correlate with adaptive and maladaptive behavioral responses tostress, and provide a framework for further studies investigating the contribution of specific brain regions to maladaptive stress responses.« less

Automated knowledge generation

NASA Technical Reports Server (NTRS)

Myler, Harley R.; Gonzalez, Avelino J.

1988-01-01

The general objectives of the NASA/UCF Automated Knowledge Generation Project were the development of an intelligent software system that could access CAD design data bases, interpret them, and generate a diagnostic knowledge base in the form of a system model. The initial area of concentration is in the diagnosis of the process control system using the Knowledge-based Autonomous Test Engineer (KATE) diagnostic system. A secondary objective was the study of general problems of automated knowledge generation. A prototype was developed, based on object-oriented language (Flavors).

Automated processing of endoscopic surgical instruments.

PubMed

Roth, K; Sieber, J P; Schrimm, H; Heeg, P; Buess, G

1994-10-01

This paper deals with the requirements for automated processing of endoscopic surgical instruments. After a brief analysis of the current problems, solutions are discussed. Test-procedures have been developed to validate the automated processing, so that the cleaning results are guaranteed and reproducable. Also a device for testing and cleaning was designed together with Netzsch Newamatic and PCI, called TC-MIC, to automate processing and reduce manual work.

Automated Pilot Advisory System

NASA Technical Reports Server (NTRS)

Parks, J. L., Jr.; Haidt, J. G.

1981-01-01

An Automated Pilot Advisory System (APAS) was developed and operationally tested to demonstrate the concept that low cost automated systems can provide air traffic and aviation weather advisory information at high density uncontrolled airports. The system was designed to enhance the see and be seen rule of flight, and pilots who used the system preferred it over the self announcement system presently used at uncontrolled airports.

Automated Status Notification System

NASA Technical Reports Server (NTRS)

2005-01-01

NASA Lewis Research Center's Automated Status Notification System (ASNS) was born out of need. To prevent "hacker attacks," Lewis' telephone system needed to monitor communications activities 24 hr a day, 7 days a week. With decreasing staff resources, this continuous monitoring had to be automated. By utilizing existing communications hardware, a UNIX workstation, and NAWK (a pattern scanning and processing language), we implemented a continuous monitoring system.

21 CFR 864.5240 - Automated blood cell diluting apparatus.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Automated blood cell diluting apparatus. 864.5240... § 864.5240 Automated blood cell diluting apparatus. (a) Identification. An automated blood cell diluting apparatus is a fully automated or semi-automated device used to make appropriate dilutions of a blood sample...

21 CFR 864.5240 - Automated blood cell diluting apparatus.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Automated blood cell diluting apparatus. 864.5240... § 864.5240 Automated blood cell diluting apparatus. (a) Identification. An automated blood cell diluting apparatus is a fully automated or semi-automated device used to make appropriate dilutions of a blood sample...

A machine learning approach for automated wide-range frequency tagging analysis in embedded neuromonitoring systems.

PubMed

Montagna, Fabio; Buiatti, Marco; Benatti, Simone; Rossi, Davide; Farella, Elisabetta; Benini, Luca

2017-10-01

EEG is a standard non-invasive technique used in neural disease diagnostics and neurosciences. Frequency-tagging is an increasingly popular experimental paradigm that efficiently tests brain function by measuring EEG responses to periodic stimulation. Recently, frequency-tagging paradigms have proven successful with low stimulation frequencies (0.5-6Hz), but the EEG signal is intrinsically noisy in this frequency range, requiring heavy signal processing and significant human intervention for response estimation. This limits the possibility to process the EEG on resource-constrained systems and to design smart EEG based devices for automated diagnostic. We propose an algorithm for artifact removal and automated detection of frequency tagging responses in a wide range of stimulation frequencies, which we test on a visual stimulation protocol. The algorithm is rooted on machine learning based pattern recognition techniques and it is tailored for a new generation parallel ultra low power processing platform (PULP), reaching performance of more that 90% accuracy in the frequency detection even for very low stimulation frequencies (<1Hz) with a power budget of 56mW. Copyright © 2017 Elsevier Inc. All rights reserved.

21 CFR 864.5620 - Automated hemoglobin system.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Automated hemoglobin system. 864.5620 Section 864....5620 Automated hemoglobin system. (a) Identification. An automated hemoglobin system is a fully... hemoglobin content of human blood. (b) Classification. Class II (performance standards). [45 FR 60601, Sept...

21 CFR 864.5620 - Automated hemoglobin system.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Automated hemoglobin system. 864.5620 Section 864....5620 Automated hemoglobin system. (a) Identification. An automated hemoglobin system is a fully... hemoglobin content of human blood. (b) Classification. Class II (performance standards). [45 FR 60601, Sept...

Automatic brain MR image denoising based on texture feature-based artificial neural networks.

PubMed

Chang, Yu-Ning; Chang, Herng-Hua

2015-01-01

Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

World History on the World Wide Web: A Student Satisfaction Survey and a Blinding Flash of the Obvious

ERIC Educational Resources Information Center

Longhurst, James

2003-01-01

A colleague of the author once observed that papers from first-year history students often feature blinding flashes of the obvious: (1) sweeping declarations that war is bad; (2) social inequality is unfair; or (3) that China is a big place. These sorts of papers sometimes begin with mind-boggling generalizations like "Throughout history, all…

Small cities face greater impact from automation.

PubMed

Frank, Morgan R; Sun, Lijun; Cebrian, Manuel; Youn, Hyejin; Rahwan, Iyad

2018-02-01

The city has proved to be the most successful form of human agglomeration and provides wide employment opportunities for its dwellers. As advances in robotics and artificial intelligence revive concerns about the impact of automation on jobs, a question looms: how will automation affect employment in cities? Here, we provide a comparative picture of the impact of automation across US urban areas. Small cities will undertake greater adjustments, such as worker displacement and job content substitutions. We demonstrate that large cities exhibit increased occupational and skill specialization due to increased abundance of managerial and technical professions. These occupations are not easily automatable, and, thus, reduce the potential impact of automation in large cities. Our results pass several robustness checks including potential errors in the estimation of occupational automation and subsampling of occupations. Our study provides the first empirical law connecting two societal forces: urban agglomeration and automation's impact on employment. © 2018 The Authors.

Small cities face greater impact from automation

PubMed Central

Sun, Lijun; Cebrian, Manuel; Rahwan, Iyad

2018-01-01

The city has proved to be the most successful form of human agglomeration and provides wide employment opportunities for its dwellers. As advances in robotics and artificial intelligence revive concerns about the impact of automation on jobs, a question looms: how will automation affect employment in cities? Here, we provide a comparative picture of the impact of automation across US urban areas. Small cities will undertake greater adjustments, such as worker displacement and job content substitutions. We demonstrate that large cities exhibit increased occupational and skill specialization due to increased abundance of managerial and technical professions. These occupations are not easily automatable, and, thus, reduce the potential impact of automation in large cities. Our results pass several robustness checks including potential errors in the estimation of occupational automation and subsampling of occupations. Our study provides the first empirical law connecting two societal forces: urban agglomeration and automation's impact on employment. PMID:29436514

Intelligent Automation Approach for Improving Pilot Situational Awareness

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly

2004-01-01

Automation in the aviation domain has been increasing for the past two decades. Pilot reaction to automation varies from highly favorable to highly critical depending on both the pilot's background and how effectively the automation is implemented. We describe a user-centered approach for automation that considers the pilot's tasks and his needs related to accomplishing those tasks. Further, we augment rather than replace how the pilot currently fulfills his goals, relying on redundant displays that offer the pilot an opportunity to build trust in the automation. Our prototype system automates the interpretation of hydraulic system faults of the UH-60 helicopter. We describe the problem with the current system and our methodology for resolving it.

Space power subsystem automation technology

NASA Technical Reports Server (NTRS)

Graves, J. R. (Compiler)

1982-01-01

The technology issues involved in power subsystem automation and the reasonable objectives to be sought in such a program were discussed. The complexities, uncertainties, and alternatives of power subsystem automation, along with the advantages from both an economic and a technological perspective were considered. Whereas most spacecraft power subsystems now use certain automated functions, the idea of complete autonomy for long periods of time is almost inconceivable. Thus, it seems prudent that the technology program for power subsystem automation be based upon a growth scenario which should provide a structured framework of deliberate steps to enable the evolution of space power subsystems from the current practice of limited autonomy to a greater use of automation with each step being justified on a cost/benefit basis. Each accomplishment should move toward the objectives of decreased requirement for ground control, increased system reliability through onboard management, and ultimately lower energy cost through longer life systems that require fewer resources to operate and maintain. This approach seems well-suited to the evolution of more sophisticated algorithms and eventually perhaps even the use of some sort of artificial intelligence. Multi-hundred kilowatt systems of the future will probably require an advanced level of autonomy if they are to be affordable and manageable.

Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder

PubMed Central

Zhao, Guangjun; Wang, Xuchu; Niu, Yanmin; Tan, Liwen; Zhang, Shao-Xiang

2016-01-01

Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain. PMID:27057543

Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder.

PubMed

Zhao, Guangjun; Wang, Xuchu; Niu, Yanmin; Tan, Liwen; Zhang, Shao-Xiang

2016-01-01

Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain.

ARTIST: A fully automated artifact rejection algorithm for single-pulse TMS-EEG data.

PubMed

Wu, Wei; Keller, Corey J; Rogasch, Nigel C; Longwell, Parker; Shpigel, Emmanuel; Rolle, Camarin E; Etkin, Amit

2018-04-01

Concurrent single-pulse TMS-EEG (spTMS-EEG) is an emerging noninvasive tool for probing causal brain dynamics in humans. However, in addition to the common artifacts in standard EEG data, spTMS-EEG data suffer from enormous stimulation-induced artifacts, posing significant challenges to the extraction of neural information. Typically, neural signals are analyzed after a manual time-intensive and often subjective process of artifact rejection. Here we describe a fully automated algorithm for spTMS-EEG artifact rejection. A key step of this algorithm is to decompose the spTMS-EEG data into statistically independent components (ICs), and then train a pattern classifier to automatically identify artifact components based on knowledge of the spatio-temporal profile of both neural and artefactual activities. The autocleaned and hand-cleaned data yield qualitatively similar group evoked potential waveforms. The algorithm achieves a 95% IC classification accuracy referenced to expert artifact rejection performance, and does so across a large number of spTMS-EEG data sets (n = 90 stimulation sites), retains high accuracy across stimulation sites/subjects/populations/montages, and outperforms current automated algorithms. Moreover, the algorithm was superior to the artifact rejection performance of relatively novice individuals, who would be the likely users of spTMS-EEG as the technique becomes more broadly disseminated. In summary, our algorithm provides an automated, fast, objective, and accurate method for cleaning spTMS-EEG data, which can increase the utility of TMS-EEG in both clinical and basic neuroscience settings. © 2018 Wiley Periodicals, Inc.

21 CFR 864.5850 - Automated slide spinner.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Automated slide spinner. 864.5850 Section 864.5850 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES HEMATOLOGY AND PATHOLOGY DEVICES Automated and Semi-Automated Hematology Devices § 864...

21 CFR 864.5620 - Automated hemoglobin system.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Automated hemoglobin system. 864.5620 Section 864.5620 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES HEMATOLOGY AND PATHOLOGY DEVICES Automated and Semi-Automated Hematology Devices § 864...

21 CFR 864.5620 - Automated hemoglobin system.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Automated hemoglobin system. 864.5620 Section 864.5620 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES HEMATOLOGY AND PATHOLOGY DEVICES Automated and Semi-Automated Hematology Devices § 864...

21 CFR 864.5680 - Automated heparin analyzer.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Automated heparin analyzer. 864.5680 Section 864.5680 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES HEMATOLOGY AND PATHOLOGY DEVICES Automated and Semi-Automated Hematology Devices § 864...

21 CFR 864.5850 - Automated slide spinner.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Automated slide spinner. 864.5850 Section 864.5850 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES HEMATOLOGY AND PATHOLOGY DEVICES Automated and Semi-Automated Hematology Devices § 864...

21 CFR 864.5680 - Automated heparin analyzer.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Automated heparin analyzer. 864.5680 Section 864.5680 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES HEMATOLOGY AND PATHOLOGY DEVICES Automated and Semi-Automated Hematology Devices § 864...

21 CFR 864.5850 - Automated slide spinner.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Automated slide spinner. 864.5850 Section 864.5850 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES HEMATOLOGY AND PATHOLOGY DEVICES Automated and Semi-Automated Hematology Devices § 864...

21 CFR 864.5680 - Automated heparin analyzer.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Automated heparin analyzer. 864.5680 Section 864.5680 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES HEMATOLOGY AND PATHOLOGY DEVICES Automated and Semi-Automated Hematology Devices § 864...

21 CFR 864.5850 - Automated slide spinner.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Automated slide spinner. 864.5850 Section 864.5850 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES HEMATOLOGY AND PATHOLOGY DEVICES Automated and Semi-Automated Hematology Devices § 864...

21 CFR 864.5850 - Automated slide spinner.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Automated slide spinner. 864.5850 Section 864.5850 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES HEMATOLOGY AND PATHOLOGY DEVICES Automated and Semi-Automated Hematology Devices § 864...

21 CFR 864.5680 - Automated heparin analyzer.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Automated heparin analyzer. 864.5680 Section 864.5680 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES HEMATOLOGY AND PATHOLOGY DEVICES Automated and Semi-Automated Hematology Devices § 864...

21 CFR 864.5680 - Automated heparin analyzer.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Automated heparin analyzer. 864.5680 Section 864.5680 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES HEMATOLOGY AND PATHOLOGY DEVICES Automated and Semi-Automated Hematology Devices § 864...

21 CFR 864.5620 - Automated hemoglobin system.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Automated hemoglobin system. 864.5620 Section 864.5620 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES HEMATOLOGY AND PATHOLOGY DEVICES Automated and Semi-Automated Hematology Devices § 864...

Automation literature: A brief review and analysis

NASA Technical Reports Server (NTRS)

Smith, D.; Dieterly, D. L.

1980-01-01

Current thought and research positions which may allow for an improved capability to understand the impact of introducing automation to an existing system are established. The orientation was toward the type of studies which may provide some general insight into automation; specifically, the impact of automation in human performance and the resulting system performance. While an extensive number of articles were reviewed, only those that addressed the issue of automation and human performance were selected to be discussed. The literature is organized along two dimensions: time, Pre-1970, Post-1970; and type of approach, Engineering or Behavioral Science. The conclusions reached are not definitive, but do provide the initial stepping stones in an attempt to begin to bridge the concept of automation in a systematic progression.

Automated brain tissue and myelin volumetry based on quantitative MR imaging with various in-plane resolutions.

PubMed

Andica, C; Hagiwara, A; Hori, M; Nakazawa, M; Goto, M; Koshino, S; Kamagata, K; Kumamaru, K K; Aoki, S

2018-05-01

Segmented brain tissue and myelin volumes can now be automatically calculated using dedicated software (SyMRI), which is based on quantification of R 1 and R 2 relaxation rates and proton density. The aim of this study was to determine the validity of SyMRI brain tissue and myelin volumetry using various in-plane resolutions. We scanned 10 healthy subjects on a 1.5T MR scanner with in-plane resolutions of 0.8, 2.0 and 3.0mm. Two scans were performed for each resolution. The acquisition time was 7-min and 24-sec for 0.8mm, 3-min and 9-sec for 2.0mm and 1-min and 56-sec for 3.0mm resolutions. The volumes of white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), non-WM/GM/CSF (NoN), brain parenchymal volume (BPV), intracranial volume (ICV) and myelin were compared between in-plane resolutions. Repeatability for each resolution was then analyzed. No significant differences in volumes measured were found between the different in-plane resolutions, except for NoN between 0.8mm and 2.0mm and between 2.0mm and 3.0mm. The repeatability error value for the WM, GM, CSF, NoN, BPV and myelin volumes relative to ICV was 0.97%, 1.01%, 0.65%, 0.86%, 1.06% and 0.25% in 0.8mm; 1.22%, 1.36%, 0.73%, 0.37%, 1.18% and 0.35% in 2.0mm and 1.18%, 1.02%, 0.96%, 0.45%, 1.36%, and 0.28% in 3.0mm resolutions. SyMRI brain tissue and myelin volumetry with low in-plane resolution and short acquisition times is robust and has a good repeatability so could be useful for follow-up studies. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

The application of integrated knowledge-based systems for the Biomedical Risk Assessment Intelligent Network (BRAIN)

NASA Technical Reports Server (NTRS)

Loftin, Karin C.; Ly, Bebe; Webster, Laurie; Verlander, James; Taylor, Gerald R.; Riley, Gary; Culbert, Chris

1992-01-01

One of NASA's goals for long duration space flight is to maintain acceptable levels of crew health, safety, and performance. One way of meeting this goal is through BRAIN, an integrated network of both human and computer elements. BRAIN will function as an advisor to mission managers by assessing the risk of inflight biomedical problems and recommending appropriate countermeasures. Described here is a joint effort among various NASA elements to develop BRAIN and the Infectious Disease Risk Assessment (IDRA) prototype. The implementation of this effort addresses the technological aspects of knowledge acquisition, integration of IDRA components, the use of expert systems to automate the biomedical prediction process, development of a user friendly interface, and integration of IDRA and ExerCISys systems. Because C language, CLIPS and the X-Window System are portable and easily integrated, they were chosen ss the tools for the initial IDRA prototype.

Laboratory systems integration: robotics and automation.

PubMed

Felder, R A

1991-01-01

Robotic technology is going to have a profound impact on the clinical laboratory of the future. Faced with increased pressure to reduce health care spending yet increase services to patients, many laboratories are looking for alternatives to the inflexible or "fixed" automation found in many clinical analyzers. Robots are being examined by many clinical pathologists as an attractive technology which can adapt to the constant changes in laboratory testing. Already, laboratory designs are being altered to accommodate robotics and automated specimen processors. However, the use of robotics and computer intelligence in the clinical laboratory is still in its infancy. Successful examples of robotic automation exist in several laboratories. Investigators have used robots to automate endocrine testing, high performance liquid chromatography, and specimen transportation. Large commercial laboratories are investigating the use of specimen processors which combine the use of fixed automation and robotics. Robotics have also reduced the exposure of medical technologists to specimens infected with viral pathogens. The successful examples of clinical robotics applications were a result of the cooperation of clinical chemists, engineers, and medical technologists. At the University of Virginia we have designed and implemented a robotic critical care laboratory. Initial clinical experience suggests that robotic performance is reliable, however, staff acceptance and utilization requires continuing education. We are also developing a robotic cyclosporine which promises to greatly reduce the labor costs of this analysis. The future will bring lab wide automation that will fully integrate computer artificial intelligence and robotics. Specimens will be transported by mobile robots. Specimen processing, aliquotting, and scheduling will be automated.(ABSTRACT TRUNCATED AT 250 WORDS)

Automated cGMP-compliant radiosynthesis of [18 F]-(E)-PSS232 for brain PET imaging of metabotropic glutamate receptor subtype 5.

PubMed

Park, Jun Young; Son, Jeongmin; Yun, Mijin; Ametamey, Simon M; Chun, Joong-Hyun

2018-01-01

(E)-3-(Pyridin-2-yl ethynyl)cyclohex-2-enone O-(3-(2-[ 18 F]-fluoroethoxy)propyl) oxime ([ 18 F]-(E)-PSS232, [ 18 F]2a) is a recently developed radiotracer that can be used to visualize metabotropic glutamate receptor subtype 5 (mGlu 5 ) in vivo. The mGlu 5 has become an attractive therapeutic and diagnostic target owing to its role in many neuropsychiatric disorders. Several carbon-11-labeled and fluorine-18-labeled radiotracers have been developed to measure mGlu 5 receptor occupancy in the human brain. The radiotracer [ 18 F]2a, which is used as an analogue for [ 11 C]ABP688 ([ 11 C]1) and has a longer physical half-life, is a selective radiotracer that exhibits high binding affinity for mGlu 5 . Herein, we report the fully automated radiosynthesis of [ 18 F]2a using a commercial GE TRACERlab™ FX- FN synthesizer for routine production and distribution to nearby satellite clinics. Nucleophilic substitution of the corresponding mesylate precursor with cyclotron-produced [ 18 F]fluoride ion at 100°C in dimethyl sulfoxide (DMSO), followed by high-performance liquid chromatography (HPLC) purification and formulation, readily provided [ 18 F]2a with a radiochemical yield of 40 ± 2% (decay corrected, n = 5) at the end of synthesis. Radiochemical purity for the [ 18 F]-(E)-conformer was greater than 95%. Molar activity was determined to be 63.6 ± 9.6 GBq/μmol (n = 5), and the overall synthesis time was 70 minutes. Copyright © 2017 John Wiley & Sons, Ltd.

Stages and levels of automation in support of space teleoperations.

PubMed

Li, Huiyang; Wickens, Christopher D; Sarter, Nadine; Sebok, Angelia

2014-09-01

This study examined the impact of stage of automation on the performance and perceived workload during simulated robotic arm control tasks in routine and off-nominal scenarios. Automation varies with respect to the stage of information processing it supports and its assigned level of automation. Making appropriate choices in terms of stages and levels of automation is critical to ensure robust joint system performance. To date, this issue has been empirically studied in domains such as aviation and medicine but not extensively in the context of space operations. A total of 36 participants played the role of a payload specialist and controlled a simulated robotic arm. Participants performed fly-to tasks with two types of automation (camera recommendation and trajectory control automation) of varying stage. Tasks were performed during routine scenarios and in scenarios in which either the trajectory control automation or a hazard avoidance automation failed. Increasing the stage of automation progressively improved performance and lowered workload when the automation was reliable, but incurred severe performance costs when the system failed. The results from this study support concerns about automation-induced complacency and automation bias when later stages of automation are introduced. The benefits of such automation are offset by the risk of catastrophic outcomes when system failures go unnoticed or become difficult to recover from. A medium stage of automation seems preferable as it provides sufficient support during routine operations and helps avoid potentially catastrophic outcomes in circumstances when the automation fails.

Implementation of and experiences with new automation

PubMed Central

Mahmud, Ifte; Kim, David

2000-01-01

In an environment where cost, timeliness, and quality drives the business, it is essential to look for answers in technology where these challenges can be met. In the Novartis Pharmaceutical Quality Assurance Department, automation and robotics have become just the tools to meet these challenges. Although automation is a relatively new concept in our department, we have fully embraced it within just a few years. As our company went through a merger, there was a significant reduction in the workforce within the Quality Assurance Department through voluntary and involuntary separations. However the workload remained constant or in some cases actually increased. So even with reduction in laboratory personnel, we were challenged internally and from the headquarters in Basle to improve productivity while maintaining integrity in quality testing. Benchmark studies indicated the Suffern site to be the choice manufacturing site above other facilities. This is attributed to the Suffern facility employees' commitment to reduce cycle time, improve efficiency, and maintain high level of regulatory compliance. One of the stronger contributing factors was automation technology in the laboratoriess, and this technology will continue to help the site's status in the future. The Automation Group was originally formed about 2 years ago to meet the demands of high quality assurance testing throughput needs and to bring our testing group up to standard with the industry. Automation began with only two people in the group and now we have three people who are the next generation automation scientists. Even with such a small staff,we have made great strides in laboratory automation as we have worked extensively with each piece of equipment brought in. The implementation process of each project was often difficult because the second generation automation group came from the laboratory and without much automation experience. However, with the involvement from the users at ‘get-go’, we

Implementation of and experiences with new automation.

PubMed

Mahmud, I; Kim, D

2000-01-01

In an environment where cost, timeliness, and quality drives the business, it is essential to look for answers in technology where these challenges can be met. In the Novartis Pharmaceutical Quality Assurance Department, automation and robotics have become just the tools to meet these challenges. Although automation is a relatively new concept in our department, we have fully embraced it within just a few years. As our company went through a merger, there was a significant reduction in the workforce within the Quality Assurance Department through voluntary and involuntary separations. However the workload remained constant or in some cases actually increased. So even with reduction in laboratory personnel, we were challenged internally and from the headquarters in Basle to improve productivity while maintaining integrity in quality testing. Benchmark studies indicated the Suffern site to be the choice manufacturing site above other facilities. This is attributed to the Suffern facility employees' commitment to reduce cycle time, improve efficiency, and maintain high level of regulatory compliance. One of the stronger contributing factors was automation technology in the laboratoriess, and this technology will continue to help the site's status in the future. The Automation Group was originally formed about 2 years ago to meet the demands of high quality assurance testing throughput needs and to bring our testing group up to standard with the industry. Automation began with only two people in the group and now we have three people who are the next generation automation scientists. Even with such a small staff,we have made great strides in laboratory automation as we have worked extensively with each piece of equipment brought in. The implementation process of each project was often difficult because the second generation automation group came from the laboratory and without much automation experience. However, with the involvement from the users at 'get-go', we were

Fatigue and voluntary utilization of automation in simulated driving.

PubMed

Neubauer, Catherine; Matthews, Gerald; Langheim, Lisa; Saxby, Dyani

2012-10-01

A driving simulator was used to assess the impact on fatigue, stress, and workload of full vehicle automation that was initiated by the driver. Previous studies have shown that mandatory use of full automation induces a state of "passive fatigue" associated with loss of alertness. By contrast, voluntary use of automation may enhance the driver's perceptions of control and ability to manage fatigue. Participants were assigned to one of two experimental conditions, automation optional (AO) and nonautomation (NA), and then performed a 35 min, monotonous simulated drive. In the last 5 min, automation was unavailable and drivers were required to respond to an emergency event. Subjective state and workload were evaluated before and after the drive. Making automation available to the driver failed to alleviate fatigue and stress states induced by driving in monotonous conditions. Drivers who were fatigued prior to the drive were more likely to choose to use automation, but automation use increased distress, especially in fatigue-prone drivers. Drivers in the AO condition were slower to initiate steering responses to the emergency event, suggesting optional automation may be distracting. Optional, driver-controlled automation appears to pose the same dangers to task engagement and alertness as externally initiated automation. Drivers of automated vehicles may be vulnerable to fatigue that persists when normal vehicle control is restored. It is important to evaluate automated systems' impact on driver fatigue, to seek design solutions to the issue of maintaining driver engagement, and to address the vulnerabilities of fatigue-prone drivers.

Automated System Marketplace 1987: Maturity and Competition.

ERIC Educational Resources Information Center

Walton, Robert A.; Bridge, Frank R.

1988-01-01

This annual review of the library automation marketplace presents profiles of 15 major library automation firms and looks at emerging trends. Seventeen charts and tables provide data on market shares, number and size of installations, hardware availability, operating systems, and interfaces. A directory of 49 automation sources is included. (MES)

Automated lattice data generation

NASA Astrophysics Data System (ADS)

Ayyar, Venkitesh; Hackett, Daniel C.; Jay, William I.; Neil, Ethan T.

2018-03-01

The process of generating ensembles of gauge configurations (and measuring various observables over them) can be tedious and error-prone when done "by hand". In practice, most of this procedure can be automated with the use of a workflow manager. We discuss how this automation can be accomplished using Taxi, a minimal Python-based workflow manager built for generating lattice data. We present a case study demonstrating this technology.

Automated data collection in single particle electron microscopy

PubMed Central

Tan, Yong Zi; Cheng, Anchi; Potter, Clinton S.; Carragher, Bridget

2016-01-01

Automated data collection is an integral part of modern workflows in single particle electron microscopy (EM) research. This review surveys the software packages available for automated single particle EM data collection. The degree of automation at each stage of data collection is evaluated, and the capabilities of the software packages are described. Finally, future trends in automation are discussed. PMID:26671944

Large scale serial two-photon microscopy to investigate local vascular changes in whole rodent brain models of Alzheimer's disease

NASA Astrophysics Data System (ADS)

Delafontaine-Martel, P.; Lefebvre, J.; Damseh, R.; Castonguay, A.; Tardif, P.; Lesage, F.

2018-02-01

In this study, an automated serial two-photon microscope was used to image a fluorescent gelatin filled rodent's brain in 3D. A method to compute vascular density using automatic segmentation was combined with coregistration techniques to build group-level vasculature metrics. By studying the medial prefrontal cortex and the hippocampal formation of 3 age groups (2, 4.5 and 8 months old), we compared vascular density for both WT and an Alzheimer model transgenic brain (APP/PS1). We observe a loss of vascular density caused by the ageing process and we propose further analysis to confirm our results.

Pilot interaction with automated airborne decision making systems

NASA Technical Reports Server (NTRS)

Rouse, W. B.; Chu, Y. Y.; Greenstein, J. S.; Walden, R. S.

1976-01-01

An investigation was made of interaction between a human pilot and automated on-board decision making systems. Research was initiated on the topic of pilot problem solving in automated and semi-automated flight management systems and attempts were made to develop a model of human decision making in a multi-task situation. A study was made of allocation of responsibility between human and computer, and discussed were various pilot performance parameters with varying degrees of automation. Optimal allocation of responsibility between human and computer was considered and some theoretical results found in the literature were presented. The pilot as a problem solver was discussed. Finally the design of displays, controls, procedures, and computer aids for problem solving tasks in automated and semi-automated systems was considered.

Functional divisions for visual processing in the central brain of flying Drosophila.

PubMed

Weir, Peter T; Dickinson, Michael H

2015-10-06

Although anatomy is often the first step in assigning functions to neural structures, it is not always clear whether architecturally distinct regions of the brain correspond to operational units. Whereas neuroarchitecture remains relatively static, functional connectivity may change almost instantaneously according to behavioral context. We imaged panneuronal responses to visual stimuli in a highly conserved central brain region in the fruit fly, Drosophila, during flight. In one substructure, the fan-shaped body, automated analysis revealed three layers that were unresponsive in quiescent flies but became responsive to visual stimuli when the animal was flying. The responses of these regions to a broad suite of visual stimuli suggest that they are involved in the regulation of flight heading. To identify the cell types that underlie these responses, we imaged activity in sets of genetically defined neurons with arborizations in the targeted layers. The responses of this collection during flight also segregated into three sets, confirming the existence of three layers, and they collectively accounted for the panneuronal activity. Our results provide an atlas of flight-gated visual responses in a central brain circuit.

Identifying Requirements for Effective Human-Automation Teamwork

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jeffrey C. Joe; John O'Hara; Heather D. Medema

Previous studies have shown that poorly designed human-automation collaboration, such as poorly designed communication protocols, often leads to problems for the human operators, such as: lack of vigilance, complacency, and loss of skills. These problems often lead to suboptimal system performance. To address this situation, a considerable amount of research has been conducted to improve human-automation collaboration and to make automation function better as a “team player.” Much of this research is based on an understanding of what it means to be a good team player from the perspective of a human team. However, the research is often based onmore » a simplified view of human teams and teamwork. In this study, we sought to better understand the capabilities and limitations of automation from the standpoint of human teams. We first examined human teams to identify the principles for effective teamwork. We next reviewed the research on integrating automation agents and human agents into mixed agent teams to identify the limitations of automation agents to conform to teamwork principles. This research resulted in insights that can lead to more effective human-automation collaboration by enabling a more realistic set of requirements to be developed based on the strengths and limitations of all agents.« less

Managing the Implementation of Mission Operations Automation

NASA Technical Reports Server (NTRS)

Sodano, R.; Crouse, P.; Odendahl, S.; Fatig, M.; McMahon, K.; Lakin, J.

2006-01-01

Reducing the cost of mission operations has necessitated a high level of automation both on spacecraft and ground systems. While automation on spacecraft is implemented during the design phase, ground system automation tends to be implemented during the prime mission operations phase. Experience has shown that this tendency for late automation development can be hindered by several factors: additional hardware and software resources may need to be procured; software must be developed and tested on a non-interference basis with primary operations with limited manpower; and established procedures may not be suited for automation requiring substantial rework. In this paper we will review the experience of successfully automating mission operations for seven on-orbit missions: the Compton Gamma Ray Observatory (CGRO), the Rossi X-Ray Timing Explorer (RXTE), the Advanced Composition Explorer (ACE), the Far Ultraviolet Spectroscopic Explorer (FUSE), Interplanetary Physics Laboratory (WIND), Polar Plasma Laboratory (POLAR), and the Imager for Magnetopause-to-Aurora Global Exploration (IMAGE). We will provide lessons learned in areas such as: spacecraft recorder management, procedure development, lights out commanding from the ground system vs. stored command loads, spacecraft contingency response time, and ground station interfaces. Implementing automation strategies during the mission concept and spacecraft integration and test phase as the most efficient method will be discussed.

Advanced automation for in-space vehicle processing

NASA Technical Reports Server (NTRS)

Sklar, Michael; Wegerif, D.

1990-01-01

The primary objective of this 3-year planned study is to assure that the fully evolved Space Station Freedom (SSF) can support automated processing of exploratory mission vehicles. Current study assessments show that required extravehicular activity (EVA) and to some extent intravehicular activity (IVA) manpower requirements for required processing tasks far exceeds the available manpower. Furthermore, many processing tasks are either hazardous operations or they exceed EVA capability. Thus, automation is essential for SSF transportation node functionality. Here, advanced automation represents the replacement of human performed tasks beyond the planned baseline automated tasks. Both physical tasks such as manipulation, assembly and actuation, and cognitive tasks such as visual inspection, monitoring and diagnosis, and task planning are considered. During this first year of activity both the Phobos/Gateway Mars Expedition and Lunar Evolution missions proposed by the Office of Exploration have been evaluated. A methodology for choosing optimal tasks to be automated has been developed. Processing tasks for both missions have been ranked on the basis of automation potential. The underlying concept in evaluating and describing processing tasks has been the use of a common set of 'Primitive' task descriptions. Primitive or standard tasks have been developed both for manual or crew processing and automated machine processing.

Automated Assessment in Massive Open Online Courses

ERIC Educational Resources Information Center

Ivaniushin, Dmitrii A.; Shtennikov, Dmitrii G.; Efimchick, Eugene A.; Lyamin, Andrey V.

2016-01-01

This paper describes an approach to use automated assessments in online courses. Open edX platform is used as the online courses platform. The new assessment type uses Scilab as learning and solution validation tool. This approach allows to use automated individual variant generation and automated solution checks without involving the course…

Human brain lesion-deficit inference remapped.

PubMed

Mah, Yee-Haur; Husain, Masud; Rees, Geraint; Nachev, Parashkev

2014-09-01

Our knowledge of the anatomical organization of the human brain in health and disease draws heavily on the study of patients with focal brain lesions. Historically the first method of mapping brain function, it is still potentially the most powerful, establishing the necessity of any putative neural substrate for a given function or deficit. Great inferential power, however, carries a crucial vulnerability: without stronger alternatives any consistent error cannot be easily detected. A hitherto unexamined source of such error is the structure of the high-dimensional distribution of patterns of focal damage, especially in ischaemic injury-the commonest aetiology in lesion-deficit studies-where the anatomy is naturally shaped by the architecture of the vascular tree. This distribution is so complex that analysis of lesion data sets of conventional size cannot illuminate its structure, leaving us in the dark about the presence or absence of such error. To examine this crucial question we assembled the largest known set of focal brain lesions (n = 581), derived from unselected patients with acute ischaemic injury (mean age = 62.3 years, standard deviation = 17.8, male:female ratio = 0.547), visualized with diffusion-weighted magnetic resonance imaging, and processed with validated automated lesion segmentation routines. High-dimensional analysis of this data revealed a hidden bias within the multivariate patterns of damage that will consistently distort lesion-deficit maps, displacing inferred critical regions from their true locations, in a manner opaque to replication. Quantifying the size of this mislocalization demonstrates that past lesion-deficit relationships estimated with conventional inferential methodology are likely to be significantly displaced, by a magnitude dependent on the unknown underlying lesion-deficit relationship itself. Past studies therefore cannot be retrospectively corrected, except by new knowledge that would render them redundant

Improved spatial coverage for brain 3D PRESS MRSI by automatic placement of outer-volume suppression saturation bands.

PubMed

Ozhinsky, Eugene; Vigneron, Daniel B; Nelson, Sarah J

2011-04-01

To develop a technique for optimizing coverage of brain 3D (1) H magnetic resonance spectroscopic imaging (MRSI) by automatic placement of outer-volume suppression (OVS) saturation bands (sat bands) and to compare the performance for point-resolved spectroscopic sequence (PRESS) MRSI protocols with manual and automatic placement of sat bands. The automated OVS procedure includes the acquisition of anatomic images from the head, obtaining brain and lipid tissue maps, calculating optimal sat band placement, and then using those optimized parameters during the MRSI acquisition. The data were analyzed to quantify brain coverage volume and data quality. 3D PRESS MRSI data were acquired from three healthy volunteers and 29 patients using protocols that included either manual or automatic sat band placement. On average, the automatic sat band placement allowed the acquisition of PRESS MRSI data from 2.7 times larger brain volumes than the conventional method while maintaining data quality. The technique developed helps solve two of the most significant problems with brain PRESS MRSI acquisitions: limited brain coverage and difficulty in prescription. This new method will facilitate routine clinical brain 3D MRSI exams and will be important for performing serial evaluation of response to therapy in patients with brain tumors and other neurological diseases. Copyright © 2011 Wiley-Liss, Inc.

Automation trust and attention allocation in multitasking workspace.

PubMed

Karpinsky, Nicole D; Chancey, Eric T; Palmer, Dakota B; Yamani, Yusuke

2018-07-01

Previous research suggests that operators with high workload can distrust and then poorly monitor automation, which has been generally inferred from automation dependence behaviors. To test automation monitoring more directly, the current study measured operators' visual attention allocation, workload, and trust toward imperfect automation in a dynamic multitasking environment. Participants concurrently performed a manual tracking task with two levels of difficulty and a system monitoring task assisted by an unreliable signaling system. Eye movement data indicate that operators allocate less visual attention to monitor automation when the tracking task is more difficult. Participants reported reduced levels of trust toward the signaling system when the tracking task demanded more focused visual attention. Analyses revealed that trust mediated the relationship between the load of the tracking task and attention allocation in Experiment 1, an effect that was not replicated in Experiment 2. Results imply a complex process underlying task load, visual attention allocation, and automation trust during multitasking. Automation designers should consider operators' task load in multitasking workspaces to avoid reduced automation monitoring and distrust toward imperfect signaling systems. Copyright © 2018. Published by Elsevier Ltd.

How age of acquisition influences brain architecture in bilinguals

PubMed Central

Wei, Miao; Joshi, Anand A.; Zhang, Mingxia; Mei, Leilei; Manis, Franklin R.; He, Qinghua; Beattie, Rachel L.; Xue, Gui; Shattuck, David W.; Leahy, Richard M.; Xue, Feng; Houston, Suzanne M.; Chen, Chuansheng; Dong, Qi; Lu, Zhong-Lin

2016-01-01

In the present study, we explored how Age of Acquisition (AoA) of L2 affected brain structures in bilingual individuals. Thirty-six native English speakers who were bilingual were scanned with high resolution MRI. After MRI signal intensity inhomogeneity correction, we applied both voxel-based morphometry (VBM) and surface-based morphometry (SBM) approaches to the data. VBM analysis was performed using FSL’s standard VBM processing pipeline. For the SBM analysis, we utilized a semi-automated sulci delineation procedure, registered the brains to an atlas, and extracted measures of twenty four pre-selected regions of interest. We addressed three questions: (1) Which areas are more susceptible to differences in AoA? (2) How do AoA, proficiency and current level of exposure work together in predicting structural differences in the brain? And (3) What is the direction of the effect of AoA on regional volumetric and surface measures? Both VBM and SBM results suggested that earlier second language exposure was associated with larger volumes in the right parietal cortex. Consistently, SBM showed that the cortical area of the right superior parietal lobule increased as AoA decreased. In contrast, in the right pars orbitalis of the inferior frontal gyrus, AoA, proficiency, and current level of exposure are equally important in accounting for the structural differences. We interpret our results in terms of current theory and research on the effects of L2 learning on brain structures and functions. PMID:27695193

Espina: A Tool for the Automated Segmentation and Counting of Synapses in Large Stacks of Electron Microscopy Images

PubMed Central

Morales, Juan; Alonso-Nanclares, Lidia; Rodríguez, José-Rodrigo; DeFelipe, Javier; Rodríguez, Ángel; Merchán-Pérez, Ángel

2011-01-01

The synapses in the cerebral cortex can be classified into two main types, Gray's type I and type II, which correspond to asymmetric (mostly glutamatergic excitatory) and symmetric (inhibitory GABAergic) synapses, respectively. Hence, the quantification and identification of their different types and the proportions in which they are found, is extraordinarily important in terms of brain function. The ideal approach to calculate the number of synapses per unit volume is to analyze 3D samples reconstructed from serial sections. However, obtaining serial sections by transmission electron microscopy is an extremely time consuming and technically demanding task. Using focused ion beam/scanning electron microscope microscopy, we recently showed that virtually all synapses can be accurately identified as asymmetric or symmetric synapses when they are visualized, reconstructed, and quantified from large 3D tissue samples obtained in an automated manner. Nevertheless, the analysis, segmentation, and quantification of synapses is still a labor intensive procedure. Thus, novel solutions are currently necessary to deal with the large volume of data that is being generated by automated 3D electron microscopy. Accordingly, we have developed ESPINA, a software tool that performs the automated segmentation and counting of synapses in a reconstructed 3D volume of the cerebral cortex, and that greatly facilitates and accelerates these processes. PMID:21633491

Aviation Safety/Automation Program Conference

NASA Technical Reports Server (NTRS)

Morello, Samuel A. (Compiler)

1990-01-01

The Aviation Safety/Automation Program Conference - 1989 was sponsored by the NASA Langley Research Center on 11 to 12 October 1989. The conference, held at the Sheraton Beach Inn and Conference Center, Virginia Beach, Virginia, was chaired by Samuel A. Morello. The primary objective of the conference was to ensure effective communication and technology transfer by providing a forum for technical interchange of current operational problems and program results to date. The Aviation Safety/Automation Program has as its primary goal to improve the safety of the national airspace system through the development and integration of human-centered automation technologies for aircraft crews and air traffic controllers.

[Automated analyzer of enzyme immunoassay].

PubMed

Osawa, S

1995-09-01

Automated analyzers for enzyme immunoassay can be classified by several points of view: the kind of labeled antibodies or enzymes, detection methods, the number of tests per unit time, analytical time and speed per run. In practice, it is important for us consider the several points such as detection limits, the number of tests per unit time, analytical range, and precision. Most of the automated analyzers on the market can randomly access and measure samples. I will describe the recent advance of automated analyzers reviewing their labeling antibodies and enzymes, the detection methods, the number of test per unit time and analytical time and speed per test.

Programmable Automated Welding System (PAWS)

NASA Technical Reports Server (NTRS)

Kline, Martin D.

1994-01-01

An ambitious project to develop an advanced, automated welding system is being funded as part of the Navy Joining Center with Babcock & Wilcox as the prime integrator. This program, the Programmable Automated Welding System (PAWS), involves the integration of both planning and real-time control activities. Planning functions include the development of a graphical decision support system within a standard, portable environment. Real-time control functions include the development of a modular, intelligent, real-time control system and the integration of a number of welding process sensors. This paper presents each of these components of the PAWS and discusses how they can be utilized to automate the welding operation.

Nonanalytic Laboratory Automation: A Quarter Century of Progress.

PubMed

Hawker, Charles D

2017-06-01

Clinical laboratory automation has blossomed since the 1989 AACC meeting, at which Dr. Masahide Sasaki first showed a western audience what his laboratory had implemented. Many diagnostics and other vendors are now offering a variety of automated options for laboratories of all sizes. Replacing manual processing and handling procedures with automation was embraced by the laboratory community because of the obvious benefits of labor savings and improvement in turnaround time and quality. Automation was also embraced by the diagnostics vendors who saw automation as a means of incorporating the analyzers purchased by their customers into larger systems in which the benefits of automation were integrated to the analyzers.This report reviews the options that are available to laboratory customers. These options include so called task-targeted automation-modules that range from single function devices that automate single tasks (e.g., decapping or aliquoting) to multifunction workstations that incorporate several of the functions of a laboratory sample processing department. The options also include total laboratory automation systems that use conveyors to link sample processing functions to analyzers and often include postanalytical features such as refrigerated storage and sample retrieval.Most importantly, this report reviews a recommended process for evaluating the need for new automation and for identifying the specific requirements of a laboratory and developing solutions that can meet those requirements. The report also discusses some of the practical considerations facing a laboratory in a new implementation and reviews the concept of machine vision to replace human inspections. © 2017 American Association for Clinical Chemistry.

Power subsystem automation study

NASA Technical Reports Server (NTRS)

Imamura, M. S.; Moser, R. L.; Veatch, M.

1983-01-01

Generic power-system elements and their potential faults are identified. Automation functions and their resulting benefits are defined and automation functions between power subsystem, central spacecraft computer, and ground flight-support personnel are partitioned. All automation activities were categorized as data handling, monitoring, routine control, fault handling, planning and operations, or anomaly handling. Incorporation of all these classes of tasks, except for anomaly handling, in power subsystem hardware and software was concluded to be mandatory to meet the design and operational requirements of the space station. The key drivers are long mission lifetime, modular growth, high-performance flexibility, a need to accommodate different electrical user-load equipment, onorbit assembly/maintenance/servicing, and potentially large number of power subsystem components. A significant effort in algorithm development and validation is essential in meeting the 1987 technology readiness date for the space station.

Flight deck automation: Promises and realities

NASA Technical Reports Server (NTRS)

Norman, Susan D. (Editor); Orlady, Harry W. (Editor)

1989-01-01

Issues of flight deck automation are multifaceted and complex. The rapid introduction of advanced computer-based technology onto the flight deck of transport category aircraft has had considerable impact both on aircraft operations and on the flight crew. As part of NASA's responsibility to facilitate an active exchange of ideas and information among members of the aviation community, a NASA/FAA/Industry workshop devoted to flight deck automation, organized by the Aerospace Human Factors Research Division of NASA Ames Research Center. Participants were invited from industry and from government organizations responsible for design, certification, operation, and accident investigation of transport category, automated aircraft. The goal of the workshop was to clarify the implications of automation, both positive and negative. Workshop panels and working groups identified issues regarding the design, training, and procedural aspects of flight deck automation, as well as the crew's ability to interact and perform effectively with the new technology. The proceedings include the invited papers and the panel and working group reports, as well as the summary and conclusions of the conference.

Brain tissue segmentation based on DTI data

PubMed Central

Liu, Tianming; Li, Hai; Wong, Kelvin; Tarokh, Ashley; Guo, Lei; Wong, Stephen T.C.

2008-01-01

We present a method for automated brain tissue segmentation based on the multi-channel fusion of diffusion tensor imaging (DTI) data. The method is motivated by the evidence that independent tissue segmentation based on DTI parametric images provides complementary information of tissue contrast to the tissue segmentation based on structural MRI data. This has important applications in defining accurate tissue maps when fusing structural data with diffusion data. In the absence of structural data, tissue segmentation based on DTI data provides an alternative means to obtain brain tissue segmentation. Our approach to the tissue segmentation based on DTI data is to classify the brain into two compartments by utilizing the tissue contrast existing in a single channel. Specifically, because the apparent diffusion coefficient (ADC) values in the cerebrospinal fluid (CSF) are more than twice that of gray matter (GM) and white matter (WM), we use ADC images to distinguish CSF and non-CSF tissues. Additionally, fractional anisotropy (FA) images are used to separate WM from non-WM tissues, as highly directional white matter structures have much larger fractional anisotropy values. Moreover, other channels to separate tissue are explored, such as eigenvalues of the tensor, relative anisotropy (RA), and volume ratio (VR). We developed an approach based on the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm that combines these two-class maps to obtain a complete tissue segmentation map of CSF, GM, and WM. Evaluations are provided to demonstrate the performance of our approach. Experimental results of applying this approach to brain tissue segmentation and deformable registration of DTI data and spoiled gradient-echo (SPGR) data are also provided. PMID:17804258

Opening up Library Automation Software

ERIC Educational Resources Information Center

Breeding, Marshall

2009-01-01

Throughout the history of library automation, the author has seen a steady advancement toward more open systems. In the early days of library automation, when proprietary systems dominated, the need for standards was paramount since other means of inter-operability and data exchange weren't possible. Today's focus on Application Programming…

Translation: Aids, Robots, and Automation.

ERIC Educational Resources Information Center

Andreyewsky, Alexander

1981-01-01

Examines electronic aids to translation both as ways to automate it and as an approach to solve problems resulting from shortage of qualified translators. Describes the limitations of robotic MT (Machine Translation) systems, viewing MAT (Machine-Aided Translation) as the only practical solution and the best vehicle for further automation. (MES)

Automated packing systems: review of industrial implementations

NASA Astrophysics Data System (ADS)

Whelan, Paul F.; Batchelor, Bruce G.

1993-08-01

A rich theoretical background to the problems that occur in the automation of material handling can be found in operations research, production engineering, systems engineering and automation, more specifically machine vision, literature. This work has contributed towards the design of intelligent handling systems. This paper will review the application of these automated material handling and packing techniques to industrial problems. The discussion will also highlight the systems integration issues involved in these applications. An outline of one such industrial application, the automated placement of shape templates on to leather hides, is also discussed. The purpose of this system is to arrange shape templates on a leather hide in an efficient manner, so as to minimize the leather waste, before they are automatically cut from the hide. These pieces are used in the furniture and car manufacturing industries for the upholstery of high quality leather chairs and car seats. Currently this type of operation is semi-automated. The paper will outline the problems involved in the full automation of such a procedure.