Subregion Size Research Articles

A deep convolutional neural network for the automatic segmentation of glioblastoma brain tumor: Joint spatial pyramid module and attention mechanism network

This study proposes a deep convolutional neural network for the automatic segmentation of glioblastoma brain tumors, aiming sat replacing the manual segmentation method that is both time-consuming and labor-intensive. There are many challenges for automatic segmentation to finely segment sub-regions from multi-sequence magnetic resonance images because of the complexity and variability of glioblastomas, such as the loss of boundary information, misclassified regions, and subregion size. To overcome these challenges, this study introduces a spatial pyramid module and attention mechanism to the automatic segmentation algorithm, which focuses on multi-scale spatial details and context information. The proposed method has been tested in the public benchmarks BraTS 2018, BraTS 2019, BraTS 2020 and BraTS 2021 datasets. The Dice score on the enhanced tumor, whole tumor, and tumor core were respectively 79.90 %, 89.63 %, and 85.89 % on the BraTS 2018 dataset, respectively 77.14 %, 89.58 %, and 83.33 % on the BraTS 2019 dataset, and respectively 77.80 %, 90.04 %, and 83.18 % on the BraTS 2020 dataset, and respectively 83.48 %, 90.70 %, and 88.94 % on the BraTS 2021 dataset offering performance on par with that of state-of-the-art methods with only 1.90 M parameters. In addition, our approach significantly reduced the requirements for experimental equipment, and the average time taken to segment one case was only 1.48 s; these two benefits rendered the proposed network intensely competitive for clinical practice.

Holographic renormalized entanglement and entropic c-function

We compute holographic entanglement entropy (EE) and the renormalized EE in AdS solitons with gauge potential for various dimensions. The renormalized EE is a cutoff-independent universal component of EE. Via Kaluza-Klein compactification of S1 and considering the low-energy regime, we deduce the (d − 1)-dimensional renormalized EE from the odd-dimensional counterpart. This corresponds to the shrinking circle of AdS solitons, probed at large l. The minimal surface transitions from disk to cylinder dominance as l increases. The quantum phase transition occurs at a critical subregion size, with renormalized EE showing non-monotonic behavior around this size. Across dimensions, massive modes decouple at lower energy, while degrees of freedom with Wilson lines contribute at smaller energy scales.

Research on Road Network Partitioning Considering the Coupling of Network Connectivity and Traffic Attributes

The urban road network is a large and complex system characterized by significant heterogeneity arising from different spatial structures and traffic demands. To facilitate effective management and control, it is necessary to partition the road network into homogeneous sub-areas. In this regard, we aim to propose a hybrid method for partitioning sub-areas with intra-area homogeneity, inter-area heterogeneity, and similar sizes, called CSDRA. It is specifically designed for bidirectional road networks with segment weights that encompass traffic flow, speed, or roadside facility evaluation. Based on community detection and spectral clustering, this proposed method comprises four main modules: initial partition, partitioning of large sub-areas, reassignment of small sub-areas, and boundary adjustment. In the preliminary partitioning work, we also design a road network reconstruction method which further helps to enhance the intra-area homogeneity and inter-area heterogeneity of partitioning results. Furthermore, to align with the requirement for comparable work units in practical traffic management and control, we control the similarity in the size of sub-areas by enforcing upper and lower bound constraints on the size of the sub-areas. We verify the outperformance of the proposed method by an experiment on the partitioning of an urban road network in Guangzhou, China, where we employ sidewalk barrier-free score data as segment weights. The results demonstrate the effectiveness of both the road network reconstruction method and the CSDRA proposed in this paper, as they significantly improve the partitioning outcomes compared with other methods using different evaluation indicators corresponding to the partitioning objectives. Finally, we investigate the influence of constraint parameters on the evaluation indicator. Our findings indicate that appropriately configuring these constraint parameters can effectively minimize sub-region size variations while having minimal impact on other aspects.

Hidden damage visualization using laser speckle photometry

This paper exploits laser speckle photometry (LSP), a full-field non-contact optical-based image analysis technique, for effectively and rapidly imaging hidden damage in structures, rather than with the complex setups in digital speckle pattern interferometry (DSPI) or shearography (SG)-based interferometry. This technique will demonstrate a promising potential for large-area inspection of composite structures in near real time to unearth barely visible impact damage (BVID) which would typically go unnoticed during routine inspections. Three image (processing) algorithms for localizing and then imaging the BVID area were explored: conventional mean squared error (MSE), normalized cross-correlation (NCC), and an index-centered algorithm known as structural similarity index measure (SSIM). When implementing these algorithms in LSP, a pre-processing step of selecting a window size (subregion size) for locally correlating the images to localize the damage and estimate its size was performed as an advancement on the previous pixel-by-pixel correlations made with LSP.A trade-off strategy between two perceptual-based metrics, image fidelity and image sharpness/blurriness, was implemented to evaluate an appropriate range for window sizes followed by the image algorithms for creating high contrast imaging of damage regions. From the correlation map, the strategy was carried out to mitigate image noise caused by the camera (image) sensors and speckle patterns dictated by the overall root-mean-square deviation (RMSD) while maintaining a high level of sharpness characterized by the magnitude of the third-level discrete wavelet transform. The proposed image algorithms in conjunction with the appropriately selected window size served as imaging conditions in the context of laser speckles for the first time and were tested on BVID in an impacted honeycomb composite panel under thermal excitation. A low coherence (high-power) laser for fast screening of a large area, if required, followed by a high coherence (low-power laser) for detailed imaging were used to demonstrate the efficacy of LSP. The damage image region agreed well with a baseline image from the well-established point-by-point CT-scan with all the three image algorithms. Overall, NCC and SSIM performed slightly better than MSE, with SSIM generally being the better of the two. Nevertheless, MSE has its merits with ease of interpretation and implementation. LSP with the proposed imaging conditions shows enormous potential as a real-time non-destructive inspection (NDI) technique not only in the aerospace industry but also in industries such as additive manufacturing where on-line in-situ monitoring is desired for prevalent defects. The real-time inspection using LSP will further allow immediate feedback for process controls.

Entanglement Negativity Spectrum of Random Mixed States: A Diagrammatic Approach

The entanglement properties of random pure states are relevant to a variety of problems ranging from chaotic quantum dynamics to black hole physics. The averaged bipartite entanglement entropy of such states admits a volume law and upon increasing the subregion size follows the Page curve. In this paper, we generalize this setup to random mixed states by coupling the system to a bath and use the partial transpose to study their entanglement properties. We develop a diagrammatic method to incorporate partial transpose within random matrix theory and formulate a perturbation theory in $1/L$, the inverse of the Hilbert space dimension. We compute several quantities including the spectral density of partial transpose (or entanglement negativity spectrum), two-point correlator of eigenvalues, and the logarithmic negativity. As long as the bath is smaller than the system, we find that upon sweeping the subregion size, the logarithmic negativity shows an initial increase and a final decrease similar to the Page curve, while it admits a plateau in the intermediate regime where the logarithmic negativity only depends on the size of the system and of the bath but not on how the system is partitioned. This intermediate phase has no analog in random pure states, and is separated from the two other regimes by a critical point. We further show that when the bath is larger than the system by at least two extra qubits the logarithmic negativity is identically zero which implies that there is no distillable entanglement. Using the diagrammatic approach, we provide a simple derivation of the semi-circle law of the entanglement negativity spectrum in the latter two regimes. We show that despite the appearance of a semicircle distribution, reminiscent of Gaussian unitary ensemble (GUE), the higher order corrections to the negativity spectrum and two-point correlator deviate from those of GUE.

Subregion complexity in holographic thermalization with dS boundary

We study the time evolution of holographic subregion complexity (HSC) in Vaidya spacetime with dS boundary. The subregion on the boundary is chosen to be a sphere within the cosmological horizon. It is found that the behaviour of HSC is similar to that in cases with flat boundary. The whole evolution can be divided into four stages: First, it grows almost linearly, then the growth slows down; After reaching a maximum it drops down quickly and gets to saturation finally. The linear growth rate in the first stage is found to depend almost only on the the mass parameter. As the subregion size approaches the cosmological horizon, this stage is expected to last forever with the subsequent three stages washed out. The saturation time {tilde{t}}_{sat} depends almost only on the subregion size {tilde{R}} as {tilde{t}}_{sat} = tanh ^{-1} ({tilde{R}}) which is linear in {tilde{R}} when {tilde{R}} is small but logarithmically divergent as {tilde{R}} approaches the cosmological horizon.

Holographic entanglement entropy for charged accelerating AdS black holes

We investigate the holographic entanglement entropy in the Rindler-AdS space-time to obtain an exact solution for the corresponding minimal surface. Moreover, the holographic entanglement entropy of the charged single accelerated AdS Black holes in four dimensions is investigated. We obtain the volume of the codimension one-time slice in the bulk geometry enclosed by the minimal surface for both the Rindler-AdS space-time and the charged accelerated AdS Black holes in the bulk. It is shown that the holographic entanglement entropy and the volume enclosed by the minimal hyper-surface in both the Rindler space-time and the charged single accelerated AdS Black holes (C-metric) in the bulk decrease with increasing acceleration parameter. Behavior of the entanglement entropy, subregion size and value of the acceleration parameter are investigated. It is shown that for |A|<0.2 a larger subregion on the boundary is equivalent to less information about the space-time.

Quantification of Cardiomyocyte Alignment from Three-Dimensional (3D) Confocal Microscopy of Engineered Tissue.

Biological tissues have complex, three-dimensional (3D) organizations of cells and matrix factors that provide the architecture necessary to meet morphogenic and functional demands. Disordered cell alignment is associated with congenital heart disease, cardiomyopathy, and neurodegenerative diseases and repairing or replacing these tissues using engineered constructs may improve regenerative capacity. However, optimizing cell alignment within engineered tissues requires quantitative 3D data on cell orientations and both efficient and validated processing algorithms. We developed an automated method to measure local 3D orientations based on structure tensor analysis and incorporated an adaptive subregion size to account for multiple scales. Our method calculates the statistical concentration parameter, κ, to quantify alignment, as well as the traditional orientational order parameter. We validated our method using synthetic images and accurately measured principal axis and concentration. We then applied our method to confocal stacks of cleared, whole-mount engineered cardiac tissues generated from human-induced pluripotent stem cells or embryonic chick cardiac cells and quantified cardiomyocyte alignment. We found significant differences in alignment based on cellular composition and tissue geometry. These results from our synthetic images and confocal data demonstrate the efficiency and accuracy of our method to measure alignment in 3D tissues.

Entanglement and Majorana edge states in the Kitaev model

We investigate the von Neumann entanglement entropy and Schmidt gap in the ground state of the Kitaev model on the honeycomb lattice for square and cylindrical subsystems. We find that, for both the subsystems, the free-fermionic contribution to the entanglement entropy $S_E$ exhibits signatures of the phase transitions between the gapless and gapped phases. However, we find that $S_E$ does not show an expected monotonic behaviour as a function of the coupling $J_z$ between the suitably defined one-dimensional chains within the gapless phase for either geometry; moreover the system generically reaches a point of minimum entanglement within the gapless phase before the entanglement saturates or increases again until the gapped phase is reached. This may be attributed to the competition between the nearest neighbour correlation functions of the $x-x$ and $z-z$ bonds, and to the onset of gapless $\vec{k}$-modes in the bulk spectrum. On the other hand $S_E$ always monotonically varies with $J_z$ in the gapped phase independent of the subregion size or shape. Finally, further confirming the Li-Haldane conjecture, we find that the Schmidt gap $\Delta$ defined from the entanglement spectrum also signals the topological transitions but only if there are corresponding zero energy Majorana edge states that simultaneously appear or disappear across the transitions. We analytically corroborate some of our results on entanglement entropy, Schmidt gap and the bulk-edge correspondence using perturbation theory.

Anomalies in the entanglement properties of the square-lattice Heisenberg model

We compute the bipartite entanglement properties of the spin-half square-lattice Heisenberg model by a variety of numerical techniques that include valence bond quantum Monte Carlo (QMC), stochastic series expansion QMC, high temperature series expansions and zero temperature coupling constant expansions around the Ising limit. We find that the area law is always satisfied, but in addition to the entanglement entropy per unit boundary length, there are other terms that depend logarithmically on the subregion size, arising from broken symmetry in the bulk and from the existence of corners at the boundary. We find that the numerical results are anomalous in several ways. First, the bulk term arising from broken symmetry deviates from an exact calculation that can be done for a mean-field Neel state. Second, the corner logs do not agree with the known results for non-interacting Boson modes. And, third, even the finite temperature mutual information shows an anomalous behavior as T goes to zero, suggesting that T->0 and L->infinity limits do not commute. These calculations show that entanglement entropy demonstrates a very rich behavior in d>1, which deserves further attention.

2DRMP: A suite of two-dimensional R-matrix propagation codes

The R-matrix method has proved to be a remarkably stable, robust and efficient technique for solving the close-coupling equations that arise in electron and photon collisions with atoms, ions and molecules. During the last thirty-four years a series of related R-matrix program packages have been published periodically in CPC. These packages are primarily concerned with low-energy scattering where the incident energy is insufficient to ionise the target. In this paper we describe 2DRMP, a suite of two-dimensional R-matrix propagation programs aimed at creating virtual experiments on high performance and grid architectures to enable the study of electron scattering from H-like atoms and ions at intermediate energies. Program summary Program title: 2DRMP Catalogue identifier: AEEA_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEEA_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 196 717 No. of bytes in distributed program, including test data, etc.: 3 819 727 Distribution format: tar.gz Programming language: Fortran 95, MPI Computer: Tested on CRAY XT4 [1]; IBM eServer 575 [2]; Itanium II cluster [3] Operating system: Tested on UNICOS/lc [1]; IBM AIX [2]; Red Hat Linux Enterprise AS [3] Has the code been vectorised or parallelised?: Yes. 16 cores were used for small test run Classification: 2.4 External routines: BLAS, LAPACK, PBLAS, ScaLAPACK Subprograms used: ADAZ_v1_1 Nature of problem: 2DRMP is a suite of programs aimed at creating virtual experiments on high performance architectures to enable the study of electron scattering from H-like atoms and ions at intermediate energies. Solution method: Two-dimensional R-matrix propagation theory. The ( r 1 , r 2 ) space of the internal region is subdivided into a number of subregions. Local R-matrices are constructed within each subregion and used to propagate a global R-matrix, ℜ, across the internal region. On the boundary of the internal region ℜ is transformed onto the IERM target state basis. Thus, the two-dimensional R-matrix propagation technique transforms an intractable problem into a series of tractable problems enabling the internal region to be extended far beyond that which is possible with the standard one-sector codes. A distinctive feature of the method is that both electrons are treated identically and the R-matrix basis states are constructed to allow for both electrons to be in the continuum. The subregion size is flexible and can be adjusted to accommodate the number of cores available. Restrictions: The implementation is currently restricted to electron scattering from H-like atoms and ions. Additional comments: The programs have been designed to operate on serial computers and to exploit the distributed memory parallelism found on tightly coupled high performance clusters and supercomputers. 2DRMP has been systematically and comprehensively documented using ROBODoc [4] which is an API documentation tool that works by extracting specially formatted headers from the program source code and writing them to documentation files. Running time: The wall clock running time for the small test run using 16 cores and performed on [3] is as follows: bp (7 s); rint2 (34 s); newrd (32 s); diag (21 s); amps (11 s); prop (24 s).

A Technique for Regional Analysis of Femorotibial Cartilage Thickness Based on Quantitative Magnetic Resonance Imaging

The objective of this work was to develop a methodology for measuring cartilage thickness in anatomically based subregions in the tibial and in the central weight-bearing femoral cartilage from magnetic resonance (MR) images. The tibial plateau was divided into a central area of the total subchondral bone area (tAB), and anterior, posterior, internal, and external subregions surrounding it. In the weight-bearing femoral condyles, central, internal, and external subregions were determined. The Euclidean distance between the tAB and cartilage surface was used for determining cartilage thickness. The reproducibility of the method was evaluated on test-retest data sets of 12 participants (six healthy, six with osteoarthritis). The subregion size was varied systematically to study the influence on the reproducibility. The size of the subregions was highly consistent under conditions of repositioning (standard deviation 0.0%-0.3%). The precision errors for regional mean cartilage thickness measurements ranged from 19 microm (1.5%) to 84 microm (4.7%). The computation of regional cartilage thickness values from segmented MR images is shown to be highly reproducible and robust under conditions of joint repositioning. In longitudinal studies, this technique may substantially enhance the ability of quantitative MRI to monitor structural changes in osteoarthritis at narrow time intervals.

Three-Dimensional Tracking Method of Tissue Motion with Biplane Images

For real-time three-dimensional tissue motion detection using biplane ultrasound images, we investigated the efficiency of a correlation method in which each region of interest (ROI) is divided into sub-regions so small that the deformation in each subregion can be ignored when estimating its motion in and perpendicular to the image planes. The allowed range of displacement perpendicular to the image planes in sensitively estimating tissue motion by cross-correlation was analyzed. In vitro results with a phantom revealed that the velocity component in the image plane could be detected when the perpendicular displacement between two consecutive frames was as small as 0.4 mm. Moreover it was also revealed that the subregion size must be larger than the speckle size. We also performed in vivo experiments on human liver and found that the tissue motion induced by systole was detectable by our cross-correlation method. The obtained results demonstrated the potential usefulness of the proposed method in medical applications, such as an image guide for minimally invasive therapy.

Minimizing the bias and variance of the gradient estimate in RSM simulation studies

To minimize the variance of the response estimator in response surface methodology (RSM)-based simulations, most users extend the design points to the limits of the subregion under investigation. Our analysis, however, shows that when common pseudorandom numbers are used, this practice likely increases the variance of the estimated gradient. Under the criterion of the variance of the gradient and the Mahalanobis distance function, we show that smaller subregions reduce both the bias and the variance of the estimated gradient. Furthermore, we discuss how users may take advantage of the magnitude of the induced correlations to draw inferences about the search process and to manipulate the subregion size. Our analysis offers a new direction for future research.

Response surface approximations for structural optimization

Response surface methodology can be used to construct global and midrange approximations to functions in structural optimization. Since structural optimization requires expensive function evaluations, it is important to construct accurate function approximations so that rapid convergence may be achieved. In this paper techniques to find the region of interest containing the optimal design, and techniques for finding more accurate approximations are reviewed and investigated. Aspects considered are experimental design techniques, the selection of the ‘best’ regression equation, intermediate response functions and the location and size of the region of interest. Standard examples in structural optimization are used to show that the accuracy is largely dependent on the choice of the approximating function with its associated subregion size, while the selection of a larger number of points is not necessarily cost-effective. In a further attempt to improve efficiency, different regression models were investigated. The results indicate that the use of the two methods investigated does not significantly improve the results. Finding an accurate global approximation is challenging, and sufficient accuracy could only be achieved in the example problems by considering a smaller region of the design space. © 1998 John Wiley & Sons, Ltd.

相関法によるディジタル画像処理システムにおける過誤ベクトルと標本化誤差に及ぼす要因の検討

In order to reduce the erroneous vectors which appear in image processing system for automatic measurement of flow fields with high vorticity and shearing strain rate and to ensure the measurement precision which is affected mainly by the digital sampling errors, a numerical simulation was executed using an elementary solution of two dimensional Oseen's equation as a flow model, which corresponds to the unsteady viscous diffusing free doublet flow. Four effects of vorticty and shearing strain rate, subregion size, tracer size, and interval between two pictures on erroneous vectors were investigated, and a quantitative estimation of sampling errors was proposed based on the simulation analysis.