Comprehensive Quantitative Evaluation Research Articles

Meta-analysis on the ecological impacts of widely spread non-indigenous species in the Baltic Sea

The introduction of non-indigenous species (NIS) is a major driver for global change in species biogeography, often associated with significant consequences for recipient ecosystems and services they provide for humans. Despite mandated by several high-level international legislative instruments, comprehensive quantitative evaluation on ecosystem impacts of marine NIS is scarce and lack a robust and data-driven assessment framework. The current study is aiming at fulfilling this gap, through quantitative assessment on the effects of the widespread NIS of the Baltic Sea on multiple ecosystem features and components including direct food-web effects. The outcomes of this study allowed identifying the most impacting widespread NIS, together with defining the processes underlying the most significant changes and outlined major sources of uncertainty. Lack and/or bias in the availability of evidence of impacts was recorded for several (both recent and early) introductions. Realizing a sophisticated, data and information-hungry framework for the evaluation of ecosystem impacts of NIS is not pragmatic for management purposes in the foreseeable future. Instead, simple approaches, such as application of common statistical parameters like absolute effect size, are more likely to result in tangible outcomes. As bearing no unit, effect sizes can be later easily aggregated across taxa, affected ecosystem features or spatial scales. The proposed approach enables performing systematic comparisons on the severity of impacts of different NIS along different study disciplines and ecosystems.

Automatic detection and segmentation of lung nodules in different locations from CT images based on adaptive α‐hull algorithm and DenseNet convolutional network

AbstractAutomatic lung nodules detection and segmentation can assist doctors in better diagnosis and treatment for lung cancer. However, precise detection and segmentation are still challenging, because lung nodules can have different contours or locations and may be attached to other tissues, such as neighboring blood vessel and pleural surface. In this study, an automatic detection and segmentation method for lung nodules in different locations has been developed. First, we apply Otsu thresholding to segment lung parenchyma. Next, a morphological opening operation is carried out to remove blood vessels. Then α‐hull operation is proposed to correct lung contours and optimal α values can be acquired adaptively. Finally, DenseNet convolutional network is applied to classify true lung nodules from all nodule candidates. We select the intersection area of at least three radiologists' annotations as ground truth and validate our method on 466 nodules including well‐circumscribed, juxta‐vascular, juxta‐pleural, and pleural tail. Our study not only concentrates on false positive reduction but also evaluates segmentation performance. To give a more comprehensive quantitative evaluation of nodule segmentation, we use evaluation metrics including Jaccard index (JI), dice similar coefficient (DSC), Hausdorff distance, under‐segmentation rate, over‐segmentation rate, sensitivity, specificity, accuracy, and false positive rate. Overall results are 0.6385 ± 0.1309, 0.7710 ± 0.1057, 3.5123 ± 3.1251, 0.1769 ± 0.1308, 0.1848 ± 0.1463, 0.7936 ± 0.1417, 0.9998 ± 0.0003, 0.9997 ± 0.0003, and 0.0002 ± 0.0003, respectively. These metrics can demonstrate segmentation performance in multiple dimensions. As a general and automatic detection and segmentation framework for lung nodules in different locations, this study achieves better performance than previous approaches in JI and DSC.

A GIS-Based System for Spatial-Temporal Availability Evaluation of the Open Spaces Used as Emergency Shelters: The Case of Victoria, British Columbia, Canada

Canadian emergency management planners have historically ignored the self-motivated evacuation procedures of people who cannot initially choose the safest evacuation areas. In densely developed urban areas, open spaces can be seen as ideal evacuation areas and should thus be included in shelter planning. In this study, the public open spaces in Great Victoria were selected as the study area and evaluated using GIS technologies. A multi-criteria TOPSIS evaluation model was used to conduct comprehensive quantitative evaluations of the open spaces’ safety, accessibility, and availability. Through hybrid process, service area, and POI aggregation coupling analyses, a model is created that provides an overall evaluation at the district level. In addition to providing a model for evaluating open spaces as emergency shelters, applicable to most Canadian cities, this study emphasizes the importance and disadvantages of open space emergency shelters in Canada, which have heretofore been ignored by decision makers. In Great Victoria, we found that the distribution of open spaces does not match the dynamics of the population distribution, meaning that through inadequate preparation some districts lack a safe evacuation place—this in an area where people are at high risk of earthquake disasters and their subsequent effects.

Hybrid Face Reflectance, Illumination, and Shape From a Single Image.

We propose HyFRIS-Net to jointly estimate the hybrid reflectance and illumination models, as well as the refined face shape from a single unconstrained face image in a pre-defined texture space. The proposed hybrid reflectance and illumination representation ensure photometric face appearance modeling in both parametric and non-parametric spaces for efficient learning. While forcing the reflectance consistency constraint for the same person and face identity constraint for different persons, our approach recovers an occlusion-free face albedo with disambiguated color from the illumination color. Our network is trained in a self-evolving manner to achieve general applicability on real-world data. We conduct comprehensive qualitative and quantitative evaluations with state-of-the-art methods to demonstrate the advantages of HyFRIS-Net in modeling photo-realistic face albedo, illumination, and shape.

Optimization of building model based on 5G virtual reality technology in computer vision software.

The 5G virtual reality system needs to interact with the user to draw the scene in real time. The contradiction between the complexity of the scene model and the real-time interaction is the main problem in the operation of the virtual reality system. The model optimization strategy of architectural scene in virtual reality design is studied, and the method of architectural scene model optimization is summarized. This article aims to study the optimization of computer vision software modeling through 5G virtual reality technology. In this paper, the optimization of the architectural model is studied by the method of image gray scale transformation, computer vision detection technology and virtual modeling technology. The four experiments are comprehensive evaluation and quantitative evaluation, comparison of channel estimation performance of different pilot structures, comparison of calculated and true values of external azimuth elements, and the effect of window-to-wall ratio on energy consumption per unit of residential building. The results show that hollow bricks of building materials have a great impact on the environment. The values of the three pixel coordinates X, Y, and Z calculated by the unit quaternion method are 1.27, 1.3, and -6.11, respectively, while the actual coordinate positions are 1.25, 1.37, and -6.22, respectively. It can be seen that the outer orientation element value calculated by the quaternion-based spatial rear intersection method is not much different from the actual value, and the correct result can be accurately calculated.

Ultrasonic Logging Image Denoising Based on CNN and Feature Attention

Various kinds of noise will be produced during the process of ultrasonic logging in high temperature and high-pressure environment under oil wells, which is blurring the logging image. This paper presents a novel end-to-end denoising model (ULNet) based on CNN and feature attention to address this problem and remove the noise from ultrasonic logging images. Our method mainly includes feature attention, feature enhancement based on residual model and reconstruction for ultrasonic logging image. Feature enhancement based on a residual model integrates global and local features to increase the expressive ability of the denoising model. Feature attention is used to distinguish the channel feature weights, and effective for blind denoising of actual images. Kernel dilation and skip-connection is used to reduce the computational cost during training. The Noise mapping results are used to reconstruct a clean image. Comprehensive quantitative and qualitative evaluations of results for selected study datasets collected at six oil wells in China show that this model is a feasible and effective means for denoising ultrasonic logging images. Overall, ULNet shows potential for practical ultrasonic logging images denoising.

A Review: Green Roof Based on the Waterfront in Hot Summer as Well as Cold Winter Zone Via Energyplus

There are some urgent problems in green construction, such as green technology stacking, focusing super on the form but not as effective, etc. Adopting literature / questionnaire survey / simulation / experiment. Considering the reality of our country, it is more realistic to study the compound effect of green building design on the waterfront in Wuhan. The comprehensive quantitative evaluation index is not perfect, as well as the applicable types of green system are limited. The reason may be the influence of data statistics on software. In order to understand the relationship between green building / waterfront as well as Wuhan as well as provide a theoretical basis on that account the sustainable development of buildings. Energy consumption is produced in the architecture. In addition, roof greening helps to restore vegetation in the environment, increase biodiversity as well as enhance the aesthetic value of the city. Examines the advantages as well as limitations of roof greening, focusing on the application of roof greening in the water-energy relationship. Such high-efficient review will open up ultra avenues on that account green roof.

Multi-Objective Trade-Off Quantification Based Design Optimization for Power Electronic Systems in More-Electric-Aircrafts

In this paper, a comprehensive formulation of reliability models for active and passive power components (i.e., capacitors and power semiconductor devices (MOSFETS)) of a regulated transformer rectifier unit (RTRU) for more electric aircraft application is presented. Additionally, the work focuses on the reliability analysis and evaluation at the system level for a non-modular discrete MOSFET based auxiliary power supply unit for more-electric-aircraft. A rigorous quantitative assessment of reliability, volume and power loss is carried out at the component level as well as system level, verifying how a particular design relates to the specific performance parameters. Keeping this as a motivation, this paper also focuses on studying and analyzing the inter-relation among reliability and other performance metrics such as power density and efficiency taking a 3kW RTRU as an example design. The major metrics under consideration for this study are the failure in time (FIT), mean time to failure (MTTF), power density, efficiency while keeping the eye on most failure prone elements of the power converter - capacitors and active power semiconductors. A comprehensive quantitative evaluation based on thermal management size, system reliability is done on choice of the switching semiconductors where the candidate pool includes Si and SiC MOSFET technologies. From the analysis on three different types of widely used capacitors - film, ceramic, aluminum electrolytic, it is concluded that the optimum choices of capacitors for high voltage DC link and low-voltage output belong to Aluminum and Ceramic types, respectively. Finally, the optimum selection of power stage components is derived from the extensive evaluation of the overall RTRU system in terms of system size and reliability.

THE SIGNIFICANCE OF LOCALIZATION AND ADAPTIVE HIERARCHICAL CYBER ATTACK DETECTION IN AN ACTIVE DISTRIBUTION SYSTEM

The increasing integration of distributed renewable energy sources into the grid has made it more challenging to design a cyber security plan for active distribution networks. This article outlines a method for locating and identifying cyberattacks in distributed active distribution systems using electrical waveform analysis. A sequential deep learning model is the foundation for cyber attack detection since it can identify even the smallest incursions. In a two-stage approach, the targeted cyberattack is first localized inside the estimated cyberattack sub-region. We introduce a network splitting strategy for "coarse" localization of hierarchical cyberattacks, based on a modified version of spectral clustering. It is recommended to create several waveform parameters and apply a normalised effect score based on statistical metrics of the waves themselves in order to further localise the origin of a cyberattack.Lastly, a comprehensive quantitative evaluation based on two case studies shows that, in comparison to both traditional and cutting-edge approaches, the suggested framework produces reliable estimations. Suggested Search Terms: Support Vector Machine, Random Forest, Logistic Regression, Gradient Boosting, and Cyber Attack Detection.

Comfort Evaluation of an Aircraft Cabin System Employing a Hybrid Model

Comfort is becoming one of the most important principles in the process of design and evaluation of civil aircraft cabins. However, the comprehensive quantitative evaluation of comfort in an aircraft cabin is a complicated issue because of the subjectivity of comfort perception and a large number of factors involved in the whole complex cabin system. A hybrid model combined with the Decision Making Trial and Evaluation Laboratory (DEMATEL) method and fuzzy comprehensive evaluation is proposed, which considers both the interrelation between the criteria and the fuzziness of subjective comfort perception concurrently. The result of the empirical study from the questionnaire survey in flight was consistent with that of the hybrid model. The proposed model is effective. It could provide a more reasonable priority to improve comfort in the aircraft cabin. According to the measured results of the cabin environment, the cabin facilities and layout, seats and service, the specific differences between the criteria can be displayed clearly, which is helpful to improve the cabin comfort level.

Serum neurofilament light chains in MS: Association with the Timed Up and Go.

ObjectiveThis cross-sectional study aims to assess the association between neuroaxonal damage assessed by serum neurofilament light chain (sNfL) and the Timed Up and Go (TUG)—a reliable and rapid measure of global neurologic disability—in patients with MS.MethodsA total of 41 consecutive patients with MS (38.0 ± 10.4 years; 57% women) with low level of disability (Expanded Disability Status Scale [EDSS] score 0–3) (EDSS score 1.0, interquartile range [IQR] 0.0–2.0) were included in this study. The TUG and sNfL were measured in a 6-month interval, together with a comprehensive neuropsychological and quantitative gait evaluation. The association of sNfL (dependant variable) with TUG, and other gait, cognitive, and behavioral measures (independent variables) were evaluated with multiple linear regressions adjusted for age, sex, and EDSS score.ResultsThe sNfL concentration was 23.51 pg/mL (IQR 16.51–32.21 pg/mL), and the mean TUG was 9.27 ± 1.70 seconds. Only the TUG was associated with sNfL (β = 0.021; 95% CI 0.003–0.037; p = 0.022) (after adjusting for age, sex, and EDSS score), whereas this was not the case for gait and neuropsychological measures.ConclusionsThe TUG—an easy and unexpansive measure of disability—is associated with the degree of neuroaxonal damage, as measured by sNfL, in patients with MS with low level of disability. These findings confirm the validity of the TUG as a reliable bedside measure of global neurologic disability as a result of neuroaxonal damage.

Quantitative Characterization of Uncertainty in the Concentration-Response Relationship between Long-Term PM2.5 Exposure and Mortality at Low Concentrations.

Extensive epidemiologic evidence supports a linear, no-threshold concentration-response (C-R) relationship between long-term exposure to fine particles (PM2.5) and mortality in the United States. While examinations of the C-R relationship are designed to assess the shape of the C-R curve, they do not provide the information needed to quantitatively characterize uncertainty at specific PM2.5 concentrations, which is often needed in the context of risk assessments and benefits analyses. We developed a novel approach, using information that is typically available in published epidemiologic studies, to quantitatively characterize uncertainty at different concentrations along the PM2.5 concentration distribution. Our approach utilizes the annual mean PM2.5 concentration and corresponding standard deviation from a published epidemiologic study to estimate the standard deviation of hypothetical PM2.5 concentration distributions defined at 0.1 μg/m3 increments. The hypothetical distributions are then used to derive adjusted uncertainty estimates in the reported effect estimate at low concentrations (i.e., concentrations lower than the annual mean observed in the study). We demonstrate the application of this method in six individual epidemiologic studies that examined the relationship between long-term PM2.5 exposure and mortality and were conducted in different geographic locations worldwide and at different PM2.5 concentrations. This new method allows for a more comprehensive quantitative evaluation of uncertainty in the shape of the C-R relationship between long-term PM2.5 exposure and mortality at concentrations below the mean annual concentrations observed in current studies.

Quantitative evaluation for shape characteristics of aggregate particles based on 3D point cloud data

The aggregate with good shape characteristics are conducive to the formation of the internal structure of asphalt pavement and have an important impact on the pavement performance. In order to quantify the aggregate shape characteristics more comprehensively, the quantitative evaluation method for the shape of aggregate particles based on 3D point cloud data was proposed. Basalt, granite and limestone aggregates with size of 9.5 mm, 13.2 mm and 16 mm were obtained by screening. Each type of aggregates was divided into five different shapes of the tetrahedron, pentahedron, hexahedron, elongate and flake. The image acquisition system based on Gocator 3D intelligent sensor was used to collect 3D point cloud data of the aggregates. The noise in the 3D point cloud data collected was removed by the cascade filtering method and then the greedy triangulation algorithm was used to reconstruct the 3D aggregate digitized surface. Finally, the characteristic parameters such as length, width, height, volume and the maximum diameter of the 3D convex hull were extracted from the reconstructed aggregate particles, and the shape indexes of the aggregate, such as the isometric ratio, flakiness ratio, sphericity and roundness indexes, were calculated based on the extracted characteristics. The experimental results show that the 3D shape index of road aggregates proposed in this paper can be used for the comprehensive quantitative evaluation of the morphological characteristics of aggregates with different lithology, particle sizes and shapes. It is instructive to improve the performance of the asphalt mixture and study the aggregate gradation.

Ecological Environment Evaluation of Forest Ecosystem Nature Reserves Using an Unweighted Cloud Model

The ecological environment is the foundation of human survival and development, and forest ecosystem nature reserves play an important role in the protection of the ecological environment. The evaluation of forest ecosystem nature reserves facilitates the formulation of relevant management policies. At present, the evaluation of the ecological environment of forest ecosystem nature reserves is mainly based on detailed evaluation of some elements of the ecological environment, rather than on a comprehensive quantitative evaluation that reflects the ecological environment in many aspects. To address this shortcoming, the quantitative evaluation indicator system of comprehensive ecological environment for forest ecosystem nature reserves was established based on the water, air, soil, and biological environments, according to the consensus on ecological environment in the past research and characteristics of the research area. The weight is still a necessary and important link in the evaluation of forest ecosystem nature reserves, but the accuracy of the weight results is difficult to get a scientific judgment. To prevent the evaluation results being influenced by weighting uncertainty, an unweighted cloud model was constructed to provide an evaluation mechanism without weight. The ecological environment evaluation was then carried out using the unweighted cloud model, taking Songshan Nature Reserve as a research area. The results show that the grades of the ecological environment of Songshan Nature Reserve are 21% excellent, 67% good, and 12% qualified, and that the state of the ecological environment is stable and performing well. The evaluation results for the grades of the environmental dimension layers are water environment > soil environment > biological environment > air environment. The study’s research results can provide theoretical support for the evaluation of forest ecosystem nature reserves, and for evaluation work in general when weights are difficult to determine or uncertain.

Multi-Operator Minimum Variance Adaptive Beamforming Algorithms Accelerated With GPU.

The goal of this work is to design high-resolution, high-contrast and robust MV adaptive beamforming algorithms, which are also implemented in real-time frame rate. Multi-operator optimization is introduced into MV adaptive beamforming in this work to propose a multi-operator MV adaptive beamforming algorithmic optimization framework. Based on the proposed algorithmic optimization framework, the algorithm optimization can be either conducted by activating a single optimization operator, or conducted by activating multiple optimization operators. The multi-operator MV (MOMV) adaptive beamforming algorithms are then derived from this framework. Moreover, in order to promote the real-time imaging capability of MOMV beamforming, a GPU-based parallel acceleration framework is proposed along with the algorithmic optimization framework by exploring the image-level coarse-grained parallelization and pixel-level fine-grained parallelization. GPU computing resource allocation strategy and memory access strategy are both explored to better design the acceleration framework. Comprehensive quantitative simulation evaluations and qualitative in vivo experiments of imaging performance are studied, and the results demonstrate that the proposed MOMV adaptive beamforming algorithms significantly improve the imaging performance as compared with other MV beamforming algorithms, which have high resolution, high contrast, good robustness, and real-time imaging capability with thousands of acceleration speedup. Furthermore, the MOMV beamforming algorithm without eigen-decomposition and projection optimization operator achieves much higher beamforming frame rate with little downgrade of image quality as compared with the MOMV beamforming algorithm with all optimization operators.

Research on a multi-attribute comprehensive evaluation system for unmanned ground vehicles

To promote the development of unmanned ground vehicles, it is very necessary to establish a scientific and reasonable evaluation system for unmanned ground vehicles. This paper analyses the structure and operation of unmanned ground vehicles, and also proposes a multi-attribute comprehensive evaluation system for unmanned ground vehicles. Based on six aspects including environment perception, communication command, behavioural decision-making, human-computer interaction, weapon system and mobile platform, a new evaluation indicator system is established. In the quantitative process of qualitative indicators, Z-number theory is introduced to better describe the uncertainty degree of judgment and reasoning in human’s understanding of complex things. In order to make the indicator weights more scientific, the combined weighting model with analytic hierarchy process and information entropy is adopted for weight analysis, which not only takes into account the influence of evaluation experts on weights, but also reduces the subjective arbitrariness of weights. The least-squares grey relational analysis method is used to analyse the correlation between the comparison series and the reference series, and a comprehensive quantitative evaluation result of the unmanned ground vehicles is obtained. The evaluation examples of unmanned ground vehicles show that the proposed evaluation system can quantitatively evaluate the overall technical performance of unmanned ground vehicles. This evaluation system has high reference value for the evaluation of other complex weapons.

Decomposition of fuzzy exponential mathematical quantitative process in industrial manufacturing design

Small and medium-sized manufacturing enterprises have the characteristics of large numbers and small scales. Problems such as backward manufacturing technology, lack of talents, small amount of information resources, and insufficient product research and development capabilities have severely restricted the development of enterprises. The backward manufacturing design model cannot adapt to the development trend of modern manufacturing informatization. This paper proposes and designs a fuzzy inference model and fuzzy inference engine algorithm with threshold. In order to describe the numerical multiple input and multiple output variables in the industrial manufacturing design industry, the relevant experience is used to make numerical reasoning decisions. Applying fuzzy sets and fuzzy theory to the expert system, a fuzzy rule model containing the membership function information and thresholds of the corresponding fuzzy sets is proposed and established, and a fuzzy reasoning system suitable for numerical and uncertain reasoning decisions is constructed. The improved grey relational analysis method is used to decompose and evaluate the exponential mathematical quantitative process of manufacturing enterprises. Based on the fuzzy Decision Analytic Network Process (DANP) method to calculate the relative weight of the influencing factors in the evaluation system, the evaluation index of the enterprise is obtained. Starting from the industrial manufacturing design process, this article constructs a relatively comprehensive and reasonable enterprise exponential mathematical quantitative process decomposition evaluation system. Considering that there are complex interactions between the various influencing factors in the system, the fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) method is selected to process the direct impact matrix of the evaluation system, and the causal relationship between the indicators is obtained. The fuzzy exponential gray correlation method is used to evaluate the quantitative process of industrial manufacturing design, avoiding the shortcomings of traditional methods that only consider ideal values.

Green Infrastructure Practice and a Sustainability Key Performance Indicators Framework for Neighbourhood-Level Construction of Sponge City Programme

In recent years, the Sponge City program (SCP) of China, as a sustainable stormwater management approach, has been strengthened as a national strategic level program. The Green Infrastructure (GI), due to its multi-objective and multi-benefits, has been adopted as an important measure of this new nationwide initiative. However, there is a lack of a comprehensive quantitative evaluation system for neighbourhood scale SCP. Hence, in the process of GI plan optimization, selection of implementation methods to balance its multi-benefits has become one of the key obstacles in the practice of SCP. To support robust decision making on multi-objective GI planning and comprehensive assessment, the analytic hierarchy process (AHP) has been used as a structural and systematic technique. In addition, a set of sustainability key performance indicators (KPIs) including requisite dimensions is the foundation for neighbourhood scale sustainability. Hence, AHP-based evaluation system including selection, weighting and ranking of the KPIs, is defined as a key performance indicator framework (KPIF), which is still in need for further development. Taking the GI planning for the Liangnong, Siming Lake sponge node restoration as an example, this paper develops KPIF with a comprehensive evaluation system for high-quality “Sponge Node” transitional construction. This KPIF consists of three basic criteria: “Environmental Performance”, “Economic and Adaptability Performance”, and “Social-cultural Performance and Wellbeing Performance”. In addition, 15 weighted KPIs are concluded and amongst them, the followings were relatively high: weight of the ATRCR, the promotion of biodiversity, the construction cost saving, the maintenance cost saving, and the level of recreational and wellbeing improvements for all people. In addition, the developed KPIF provides a reference for similar program’s decision-making, not only for the Jiangnan area of China, but also for quantitatively comprehensive evaluations of SCP in other regions.

Expression of MMP-14 and prognosis in digestive system carcinoma: a meta-analysis and databases validation.

Background: The Matrix metalloproteinase-14 (MMP-14) expression has been shown to be overexpressed in different cancers. However, there is no comprehensive quantitative evaluation of the MMP-14 prognostic value in digestive system carcinoma (DSC). The aim of this study is to explore the correlation between the MMP-14 expression and DSC prognosis.Methods: We conducted a meta-analysis to estimate the association strength between MMP-14 expression and prognosis. GEPIA and Kaplan Meier plotters were used to assess overall survival (OS), disease-free survival (DFS)/progression-free survival (PFS) in DSC patients and the differential expression of MMP-14 in DSC tissues and adjacent tissues.Results: A total of 20 studies including 2,519 patients with OS and 438 patients with DFS/PFS data were analyzed in evidence synthesis. Overall, the combined hazard ratio (HR) with 95% confidence interval (95% CI) was 1.98 (95%Cl: 1.77-2.22, P<0.001) for OS and 3.61 (95%Cl: 2.39-5.43, P<0.001) for DFS/PFS. For subgroup analyses, significant correlations were revealed between increased MMP-14 expression and poor OS in patients with gastric cancer (HR=2.21, 95%CI: 1.76-2.77, P<0.001), esophageal carcinoma (HR=2.01, 95%CI: 1.58-2.57, P<0.001), oral cancer (HR = 1.69, 95% CI: 1.30-2.20, P < 0.001) (HR=2.14, 95%CI 1.35-2.19, P<0.001) and hepatocarcinoma. In database verification analyses, the MMP-14 expression levels in normal tissues were significantly higher than that in DSC tissues, and significant associations were observed between high MMP-14 expression levels and poor prognosis.Conclusions: The high expression levels of MMP-14 might predict poor prognosis in DSC. Larger prospective clinical cohort studies are required to validate the prognostic role.

Registration with probabilistic correspondences — Accurate and robust registration for pathological and inhomogeneous medical data

The registration of two medical images is usually based on the assumption that corresponding regions exist in both images. If this assumption is violated by e.g. pathologies most approaches encounter problems. The registration approach proposed is based on probabilistic correspondences of sparse image representations and enables a robust handling of potentially missing correspondences. A maximum a-posteriori framework is used to derive an optimization criterion with respect to deformation parameters that aim to reduce the shape and appearance differences between the registered images. A multi-resolution approach speeds up the optimization and increases the robustness of the registration. The computed probabilistic correspondences enable the approach to deal with missing correspondences in the images. Furthermore, they provide additional information about the quality of fit and potentially non-corresponding/pathological image regions. The approach is compared to two state-of-the-art registration methods using MR brain and cardiac images: a variational intensity-based registration algorithm and a feature-based registration approach using a discrete optimization scheme. The comprehensive quantitative evaluation using additional simulated stroke lesions shows a significantly higher accuracy and robustness of the proposed approach. Furthermore, the correspondence probability maps were used to characterize pathological regions in the MRI brain data.