scholarly journals Statistical Analysis of Sacral Bone Density using a CNN-based Atlas Creation on a Large-scale CT database

10.29007/4h7v ◽  
2020 ◽  
Author(s):  
Akihiro Kawasaki ◽  
Yoshito Otake ◽  
Keisuke Uemura ◽  
Masaki Takao ◽  
Nobuhiko Sugano ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Bone Density ◽  
Large Scale ◽  
Ground Truth ◽  
Deformable Registration ◽  
Deformation Field ◽  
Male And Female ◽  
Deformable Template ◽  
Registration Algorithm ◽  
Sacral Bone

Fragility fracture of the sacrum has been an issue for elderly people. Research has been limited for the sacrum partly because its shape is complex with large inter-subject variation. Also, large-scale statistical analysis of its shape and density distribution has been limited mainly due to the computational load in establishment of the voxel correspondences, i.e., deformable registration.In this study, we employed a convolutional neural network (CNN)-based deformable registration algorithm in the analysis of the sacral bone. The algorithm we employed, VoxelMorph (Dalca et al. Med Image Anal 2019), is characterized as an unsupervised algorithm where no ground truth deformation field is required. The algorithm also allows to create a conditional deformable template, which is a volume image exhibiting smallest deformation field from all samples with specific attributes (e.g., age, sex, etc.), which in short represents the “average” (or “centroid”) image among the specific age and sex group. We applied it on a database consisting of 837 CTs (149 males, 688 females, 58.14 ± 14.73 y.o.) of the pelvis region, where the sacral bone was segmented and masked. We computed the templates corresponding to ages of 20 to 80 for male and female. The templates visually illustrated reduction of the bone density with aging in both male and female. The quantitative analysis showed that the average CT value over the sacrum region was reduced from 135.73 HU to 39.88 HU for 20 y.o. template to 80 y.o. template.

Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

2018 ◽  
pp. 45-51
Author(s):  
A. V. Ponomarev
Keyword(s):  
Large Scale ◽  
Wide Spectrum ◽  
Preference Relation ◽  
Pairwise Comparison ◽  
Ground Truth ◽  
Comparison Method ◽  
Characteristic Matrix ◽  
Image Tagging ◽  
Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems). 

scholarly journals Identification of Genes with Potential Roles in Apple Fruit Development and Biochemistry through Large-Scale Statistical Analysis of Expressed Sequence Tags

2006 ◽  
Vol 141 (3) ◽  
pp. 811-824 ◽  
Author(s):  
Sunchung Park ◽  
Nobuko Sugimoto ◽  
Matthew D. Larson ◽  
Randy Beaudry ◽  
Steven van Nocker
Keyword(s):  
Statistical Analysis ◽  
Fruit Development ◽  
Expressed Sequence Tags ◽  
Large Scale ◽  
Apple Fruit ◽  
Expressed Sequence

scholarly journals Barotropic Regeneration of Upper-Level Synoptic Disturbances in Different Configurations of the Zonal Weather Regime

2008 ◽  
Vol 65 (10) ◽  
pp. 3159-3178 ◽  
Author(s):  
Gwendal Rivière
Keyword(s):  
Large Scale ◽  
Deformation Field ◽  
Regeneration Process ◽  
Barotropic Model ◽  
Weather Regime ◽  
The North ◽  
The Difference ◽  
Upper Level ◽  
Composite Maps ◽  
Large Scale Flow

Barotropic dynamics of upper-tropospheric midlatitude disturbances evolving in different configurations of the zonal weather regime (i.e., in different zonal-like large-scale flows) were studied using observational analyses and barotropic model experiments. The contraction stage of upper-level disturbances that follows their elongation stage leads to an increase of eddy kinetic energy that is called the barotropic regeneration process in this text. This barotropic mechanism is studied through notions of barotropic critical regions (BtCRs) and effective deformation that have been introduced in a previous paper. The effective deformation field is equal to the difference between the square of the large-scale deformation magnitude and the square of the large-scale vorticity. Regions where the effective deformation is positive correspond to regions where the large-scale flow tends to strongly stretch synoptic disturbances. A BtCR is an area separating two large-scale regions of positive effective deformation, one located upstream and on the south side of the jet and the other downstream and on the north side. Such a region presents a discontinuity in the orientation of the dilatation axes and is a potential area where the barotropic regeneration process may occur. Winter days presenting a zonal weather regime in the 40-yr ECMWF Re-Analysis dataset are decomposed, via a partitioning algorithm, into different configurations of the effective deformation field at 300 hPa. A six-cluster partition is obtained. Composite maps of the barotropic generation rate for each cluster exhibit a succession of negative and positive values on both sides of the BtCRs. It confirms statistically that the barotropic regeneration mechanism occurs preferentially about BtCRs. Numerical experiments using a forced barotropic model on the sphere are performed. Each experiment consists of adding a synoptic-scale perturbation to one of the zonal-like jet configurations found in observations, which is kept fixed with time. The combined effects of the effective deformation and nonlinearities are shown to be crucial to reproduce the barotropic regeneration process about BtCRs.

Lake-effect rains over Lake Victoria and their association with Mesoscale Convective Systems

2021 ◽  
Author(s):  
Sharon E. Nicholson ◽  
Douglas Klotter ◽  
Adam T. Hartman
Keyword(s):  
Large Scale ◽  
Lake Victoria ◽  
Ground Truth ◽  
Mesoscale Convective Systems ◽  
Convective Systems ◽  
Lake Effect ◽  
Mesoscale Convective ◽  
Strong Convection ◽  
Short Rains ◽  
Lake Catchment

AbstractThis article examined rainfall enhancement over Lake Victoria. Estimates of over-lake rainfall were compared with rainfall in the surrounding lake catchment. Four satellite products were initially tested against estimates based on gauges or water balance models. These included TRMM 3B43, IMERG V06 Final Run (IMERG-F), CHIRPS2, and PERSIANN-CDR. There was agreement among the satellite products for catchment rainfall but a large disparity among them for over-lake rainfall. IMERG-F was clearly an outlier, exceeding the estimate from TRMM 3B43 by 36%. The overestimation by IMERG-F was likely related to passive microwave assessments of strong convection, such as prevails over Lake Victoria. Overall, TRMM 3B43 showed the best agreement with the "ground truth" and was used in further analyses. Over-lake rainfall was found to be enhanced compared to catchment rainfall in all months. During the March-to-May long rains the enhancement varied between 40% and 50%. During the October-to-December short rains the enhancement varied between 33% and 44%. Even during the two dry seasons the enhancement was at least 20% and over 50% in some months. While the magnitude of enhancement varied from month to month, the seasonal cycle was essentially the same for over-lake and catchment rainfall, suggesting that the dominant influence on over-lake rainfall is the large-scale environment. The association with Mesoscale Convective Systems (MCSs) was also evaluated. The similarity of the spatial patterns of rainfall and MCS count each month suggested that these produced a major share of rainfall over the lake. Similarity in interannual variability further supported this conclusion.

Open Source Software Implementation of Anatomical Segmentation

2018 ◽  
Author(s):  
Maggie Hess
Keyword(s):  
Large Scale ◽  
Focused Ultrasound ◽  
Region Growing ◽  
Ground Truth ◽  
Clot Lysis ◽  
Focused Ultrasound Surgery ◽  
Manual Methods ◽  
Areas Of Interest ◽  
Seed Points ◽  
Viable Method

Purpose: Intraventricular hemorrhage (IVH) affects nearly 15% of preterm infants. It can lead to ventricular dilation and cognitive impairment. To ablate IVH clots, MR-guided focused ultrasound surgery (MRgFUS) is investigated. This procedure requires accurate, fast and consistent quantification of ventricle and clot volumes. Methods: We developed a semi-autonomous segmentation (SAS) algorithm for measuring changes in the ventricle and clot volumes. Images are normalized, and then ventricle and clot masks are registered to the images. Voxels of the registered masks and voxels obtained by thresholding the normalized images are used as seed points for competitive region growing, which provides the final segmentation. The user selects the areas of interest for correspondence after thresholding and these selections are the final seeds for region growing. SAS was evaluated on an IVH porcine model.  Results: SAS was compared to ground truth manual segmentation (MS) for accuracy, efficiency, and consistency. Accuracy was determined by comparing clot and ventricle volumes produced by SAS and MS. In Two-One-Sided Test, SAS and MS were found to be significantly equivalent (p < 0.01). SAS on average was found to be 15 times faster than MS (p < 0.01). Consistency was determined by repeated segmentation of the same image by both SAS and manual methods, SAS being significantly more consistent than MS (p < 0.05).  Conclusion: SAS is a viable method to quantify the IVH clot and the lateral brain ventricles and it is serving in a large- scale porcine study of MRgFUS treatment of IVH clot lysis.

Efficient Vector Partitioning Algorithms for Graph Clustering

2020 ◽  
Vol 1 (2) ◽  
pp. 101-123
Author(s):  
Hiroaki Shiokawa ◽  
Yasunori Futamura
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Ground Truth ◽  
Graph Clustering ◽  
Mining Communities ◽  
Fine Grained ◽  
Efficient Vector ◽  
Public Datasets ◽  
Many Core

This paper addressed the problem of finding clusters included in graph-structured data such as Web graphs, social networks, and others. Graph clustering is one of the fundamental techniques for understanding structures present in the complex graphs such as Web pages, social networks, and others. In the Web and data mining communities, the modularity-based graph clustering algorithm is successfully used in many applications. However, it is difficult for the modularity-based methods to find fine-grained clusters hidden in large-scale graphs; the methods fail to reproduce the ground truth. In this paper, we present a novel modularity-based algorithm, \textit{CAV}, that shows better clustering results than the traditional algorithm. The proposed algorithm employs a cohesiveness-aware vector partitioning into the graph spectral analysis to improve the clustering accuracy. Additionally, this paper also presents a novel efficient algorithm \textit{P-CAV} for further improving the clustering speed of CAV; P-CAV is an extension of CAV that utilizes the thread-based parallelization on a many-core CPU. Our extensive experiments on synthetic and public datasets demonstrate the performance superiority of our approaches over the state-of-the-art approaches.

scholarly journals DeepMAsED: evaluating the quality of metagenomic assemblies

2020 ◽  
Vol 36 (10) ◽  
pp. 3011-3017 ◽  
Author(s):  
Olga Mineeva ◽  
Mateo Rojas-Carulla ◽  
Ruth E Ley ◽  
Bernhard Schölkopf ◽  
Nicholas D Youngblut
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Ground Truth ◽  
Supplementary Information ◽  
Learning Approach ◽  
Wide Range ◽  
Metagenome Assembly ◽  
Model Training ◽  
Reference Genomes

Abstract Motivation Methodological advances in metagenome assembly are rapidly increasing in the number of published metagenome assemblies. However, identifying misassemblies is challenging due to a lack of closely related reference genomes that can act as pseudo ground truth. Existing reference-free methods are no longer maintained, can make strong assumptions that may not hold across a diversity of research projects, and have not been validated on large-scale metagenome assemblies. Results We present DeepMAsED, a deep learning approach for identifying misassembled contigs without the need for reference genomes. Moreover, we provide an in silico pipeline for generating large-scale, realistic metagenome assemblies for comprehensive model training and testing. DeepMAsED accuracy substantially exceeds the state-of-the-art when applied to large and complex metagenome assemblies. Our model estimates a 1% contig misassembly rate in two recent large-scale metagenome assembly publications. Conclusions DeepMAsED accurately identifies misassemblies in metagenome-assembled contigs from a broad diversity of bacteria and archaea without the need for reference genomes or strong modeling assumptions. Running DeepMAsED is straight-forward, as well as is model re-training with our dataset generation pipeline. Therefore, DeepMAsED is a flexible misassembly classifier that can be applied to a wide range of metagenome assembly projects. Availability and implementation DeepMAsED is available from GitHub at https://github.com/leylabmpi/DeepMAsED. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Large scale statistical analysis of genome data with Ruby and R: skipping interface libraries

2014 ◽  
Vol 20 (0) ◽  
Author(s):  
Sergio RP Line ◽  
Ana P De Souza ◽  
Luciana S Mofatto
Keyword(s):  
Statistical Analysis ◽  
Large Scale ◽  
Genome Data
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