1,587 research outputs found

    Partitioning intensity inhomogeneity colour images via Saliency-based active contour

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    Partitioning or segmenting intensity inhomogeneity colour images is a challenging problem in computer vision and image shape analysis. Given an input image, the active contour model (ACM) which is formulated in variational framework is regularly used to partition objects in the image. A selective type of variational ACM approach is better than a global approach for segmenting specific target objects, which is useful for applications such as tumor segmentation or tissue classification in medical imaging. However, the existing selective ACMs yield unsatisfactory outcomes when performing the segmentation for colour (vector-valued) with intensity variations. Therefore, our new approach incorporates both local image fitting and saliency maps into a new variational selective ACM to tackle the problem. The euler-lagrange (EL) equations were presented to solve the proposed model. Thirty combinations of synthetic and medical images were tested. The visual observation and quantitative results show that the proposed model outshines the other existing models by average, with the accuracy of 2.23% more than the compared model and the Dice and Jaccard coefficients which were around 12.78% and 19.53% higher, respectively, than the compared model

    Using neurobiological measures to predict and assess trauma-focused psychotherapy outcome in youth with posttraumatic stress disorder

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    In this thesis we examined different predictive neurobiological measures of traumafocused psychotherapy response and investigated the biological mechanisms underlying trauma-focused psychotherapy response in youth with PTSD. Our results suggest that activity of the major neuroendocrine stress response systems and brain functional connectivity before treatment are indeed associated with trauma-focused treatment response. Moreover, trauma-focused psychotherapy response seems to be related to longitudinal changes in autonomic nervous system activity during stress and brain structure. Together, these findings improve our understanding of the relationship between neurobiological measures and traumafocused psychotherapy response in youth with PTSD. However, these insights have currently limited to no clinical value because the current state of evidence does not support implementation of neurobiological biomarkers for treatment selection and necessary trials of (augmentation) treatments targeting neurobiological mechanisms related to treatment response have not been performed yet. The way forward now, is to perform individual prediction studies in less heterogeneous patient samples and to perform developmentally informed long-term studies examining (neuro) developmental trajectories related to PTSD and treatment response. These studies are necessary to address whether neurobiological measures can eventually improve treatment outcome and reduce the burden of PTSD in affected youth

    Approximation of SBVSBV functions with possibly infinite jump set

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    We prove an approximation result for functions u∈SBV(Ω;Rm)u\in SBV(\Omega;\mathbb R^m) such that ∇u\nabla u is pp-integrable, 1≀p<∞1\leq p<\infty, and g0(∣[u]∣)g_0(|[u]|) is integrable over the jump set (whose Hn−1\mathcal H^{n-1} measure is possibly infinite), for some continuous, nondecreasing, subadditive function g0g_0, with g0−1(0)={0}g_0^{-1}(0)=\{0\}. The approximating functions uju_j are piecewise affine with piecewise affine jump set; the convergence is that of L1L^1 for uju_j and the convergence in energy for ∣∇uj∣p|\nabla u_j|^p and g([uj],Îœuj)g([u_j],\nu_{u_j}) for suitable functions gg. In particular, uju_j converges to uu BVBV-strictly, area-strictly, and strongly in BVBV after composition with a bilipschitz map. If in addition Hn−1(Ju)<∞\mathcal H^{n-1}(J_u)<\infty, we also have convergence of Hn−1(Juj)\mathcal H^{n-1}(J_{u_j}) to Hn−1(Ju)\mathcal H^{n-1}(J_u)

    Segment Anything Model (SAM) for Radiation Oncology

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    In this study, we evaluate the performance of the Segment Anything Model (SAM) model in clinical radiotherapy. We collected real clinical cases from four regions at the Mayo Clinic: prostate, lung, gastrointestinal, and head \& neck, which are typical treatment sites in radiation oncology. For each case, we selected the OARs of concern in radiotherapy planning and compared the Dice and Jaccard outcomes between clinical manual delineation, automatic segmentation using SAM's "segment anything" mode, and automatic segmentation using SAM with box prompt. Our results indicate that SAM performs better in automatic segmentation for the prostate and lung regions, while its performance in the gastrointestinal and head \& neck regions was relatively inferior. When considering the size of the organ and the clarity of its boundary, SAM displays better performance for larger organs with clear boundaries, such as the lung and liver, and worse for smaller organs with unclear boundaries, like the parotid and cochlea. These findings align with the generally accepted variations in difficulty level associated with manual delineation of different organs at different sites in clinical radiotherapy. Given that SAM, a single trained model, could handle the delineation of OARs in four regions, these results also demonstrate SAM's robust generalization capabilities in automatic segmentation for radiotherapy, i.e., achieving delineation of different radiotherapy OARs using a generic automatic segmentation model. SAM's generalization capabilities across different regions make it technically feasible to develop a generic model for automatic segmentation in radiotherapy

    Development of Quantitative Bone SPECT Analysis Methods for Metastatic Bone Disease

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    Prostate cancer is one of the most prevalent types of cancer in males in the United States. Bone is a common site of metastases for metastatic prostate cancer. However, bone metastases are often considered “unmeasurable” using standard anatomic imaging and the RECIST 1.1 criteria. As a result, response to therapy is often suboptimally evaluated by visual interpretation of planar bone scintigraphy with response criteria related to the presence or absence of new lesions. With the commercial availability of quantitative single-photon emission computed tomography (SPECT) methods, it is now feasible to establish quantitative metrics of therapy response by skeletal metastases. Quantitative bone SPECT (QBSPECT) may provide the ability to estimate bone lesion uptake, volume, and the number of lesions more accurately than planar imaging. However, the accuracy of activity quantification in QBSPECT relies heavily on the precision with which bone metastases and bone structures are delineated. In this research, we aim at developing automated image segmentation methods for fast and accurate delineation of bone and bone metastases in QBSPECT. To begin, we developed registration methods to generate a dataset of realistic and anatomically-varying computerized phantoms for use in QBSPECT simulations. Using these simulations, we develop supervised computer-automated segmentation methods to minimize intra- and inter-observer variations in delineating bone metastases. This project provides accurate segmentation techniques for QBSPECT and paves the way for the development of QBSPECT methods for assessing bone metastases’ therapy response

    A Geometric Flow Approach for Segmentation of Images with Inhomongeneous Intensity and Missing Boundaries

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    Image segmentation is a complex mathematical problem, especially for images that contain intensity inhomogeneity and tightly packed objects with missing boundaries in between. For instance, Magnetic Resonance (MR) muscle images often contain both of these issues, making muscle segmentation especially difficult. In this paper we propose a novel intensity correction and a semi-automatic active contour based segmentation approach. The approach uses a geometric flow that incorporates a reproducing kernel Hilbert space (RKHS) edge detector and a geodesic distance penalty term from a set of markers and anti-markers. We test the proposed scheme on MR muscle segmentation and compare with some state of the art methods. To help deal with the intensity inhomogeneity in this particular kind of image, a new approach to estimate the bias field using a fat fraction image, called Prior Bias-Corrected Fuzzy C-means (PBCFCM), is introduced. Numerical experiments show that the proposed scheme leads to significantly better results than compared ones. The average dice values of the proposed method are 92.5%, 85.3%, 85.3% for quadriceps, hamstrings and other muscle groups while other approaches are at least 10% worse.Comment: Presented at CVIT 2023 Conference. Accepted to Journal of Image and Graphic

    A Novel Mesoscale Modeling of Fracture in Nuclear Fuels

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    Nuclear fuels power all the commercial carbon-free energy plants and contribute to the fight against climate change. A comprehensive understanding of the fracture behavior of nuclear fuels during normal and transient conditions is still lacking. In this work, we developed a novel Multiphysics mesoscale model that could predict the fracture in different nuclear fuels under various conditions by employing the phase-field modeling of fracture. This work introduces both (1) An experimental study to understand the fuel fracturing behavior of sintered UO₂ pellets when exposed to thermal shock, and (2) A Multiphysics phase-field fracture model capable of simulating this process. The model could successfully capture the formation and evolution of cracks in UO₂ fuel pellets due to a thermal shock without any ad hoc or a priori assumptions of cracking size, site, thickness, or morphologies. The model was able to capture the overall fracture trends of the corresponding experimental data. Moreover, the model further improved and utilized multi-set order parameters that allowed the simulation of the concurrent crack propagation and microstructure evolution. The new model technique is then validated and employed in an evolved nuclear-grade graphite microstructure to determine the effect of such heterogeneity on the formation and propagation of the cracks along with the variations in the materials' mechanical properties. This new technique advances the fundamental understanding of nuclear fuel behavior under normal and transient conditions and provides a predictive modeling tool for deriving physics-based criteria for the fracture behavior of ₂the current and the new nuclear fuel designs

    To Serve and Deplete: Exploring Outcomes of Emotional Exhaustion in Servant Leadership and the Moderating Role of Self-Leadership

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    This study explores the outcomes of emotional exhaustion in servant leadership, a leadership style through which leaders enact influence by serving, empowering, and developing others—that is, \u27leading from behind.\u27 Drawing on conservation of resources theory, I explored how servant leadership behaviors may adversely affect the practicing leader through emotional exhaustion and laterally influence the subsequent outcomes of work disengagement and work–family conflict. Additionally, I investigated the moderating influence of self-leadership on the relationship between servant leadership and emotional exhaustion. I tested a moderated mediation model with regression analysis using two independent samples of organizational leaders surveyed at two different time points (Sample 1: N = 79; Sample 2: N = 379). The findings did not support a positive relationship between servant leadership and emotional exhaustion but did support positive relationships between emotional exhaustion and work disengagement, and emotional exhaustion and work–family conflict. Several limitations of the study are discussed, along with implications and future research directions

    Application of knowledge management principles to support maintenance strategies in healthcare organisations

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    Healthcare is a vital service that touches people's lives on a daily basis by providing treatment and resolving patients' health problems through the staff. Human lives are ultimately dependent on the skilled hands of the staff and those who manage the infrastructure that supports the daily operations of the service, making it a compelling reason for a dedicated research study. However, the UK healthcare sector is undergoing rapid changes, driven by rising costs, technological advancements, changing patient expectations, and increasing pressure to deliver sustainable healthcare. With the global rise in healthcare challenges, the need for sustainable healthcare delivery has become imperative. Sustainable healthcare delivery requires the integration of various practices that enhance the efficiency and effectiveness of healthcare infrastructural assets. One critical area that requires attention is the management of healthcare facilities. Healthcare facilitiesis considered one of the core elements in the delivery of effective healthcare services, as shortcomings in the provision of facilities management (FM) services in hospitals may have much more drastic negative effects than in any other general forms of buildings. An essential element in healthcare FM is linked to the relationship between action and knowledge. With a full sense of understanding of infrastructural assets, it is possible to improve, manage and make buildings suitable to the needs of users and to ensure the functionality of the structure and processes. The premise of FM is that an organisation's effectiveness and efficiency are linked to the physical environment in which it operates and that improving the environment can result in direct benefits in operational performance. The goal of healthcare FM is to support the achievement of organisational mission and goals by designing and managing space and infrastructural assets in the best combination of suitability, efficiency, and cost. In operational terms, performance refers to how well a building contributes to fulfilling its intended functions. Therefore, comprehensive deployment of efficient FM approaches is essential for ensuring quality healthcare provision while positively impacting overall patient experiences. In this regard, incorporating knowledge management (KM) principles into hospitals' FM processes contributes significantly to ensuring sustainable healthcare provision and enhancement of patient experiences. Organisations implementing KM principles are better positioned to navigate the constantly evolving business ecosystem easily. Furthermore, KM is vital in processes and service improvement, strategic decision-making, and organisational adaptation and renewal. In this regard, KM principles can be applied to improve hospital FM, thereby ensuring sustainable healthcare delivery. Knowledge management assumes that organisations that manage their organisational and individual knowledge more effectively will be able to cope more successfully with the challenges of the new business ecosystem. There is also the argument that KM plays a crucial role in improving processes and services, strategic decision-making, and adapting and renewing an organisation. The goal of KM is to aid action – providing "a knowledge pull" rather than the information overload most people experience in healthcare FM. Other motivations for seeking better KM in healthcare FM include patient safety, evidence-based care, and cost efficiency as the dominant drivers. The most evidence exists for the success of such approaches at knowledge bottlenecks, such as infection prevention and control, working safely, compliances, automated systems and reminders, and recall based on best practices. The ability to cultivate, nurture and maximise knowledge at multiple levels and in multiple contexts is one of the most significant challenges for those responsible for KM. However, despite the potential benefits, applying KM principles in hospital facilities is still limited. There is a lack of understanding of how KM can be effectively applied in this context, and few studies have explored the potential challenges and opportunities associated with implementing KM principles in hospitals facilities for sustainable healthcare delivery. This study explores applying KM principles to support maintenance strategies in healthcare organisations. The study also explores the challenges and opportunities, for healthcare organisations and FM practitioners, in operationalising a framework which draws the interconnectedness between healthcare. The study begins by defining healthcare FM and its importance in the healthcare industry. It then discusses the concept of KM and the different types of knowledge that are relevant in the healthcare FM sector. The study also examines the challenges that healthcare FM face in managing knowledge and how the application of KM principles can help to overcome these challenges. The study then explores the different KM strategies that can be applied in healthcare FM. The KM benefits include improved patient outcomes, reduced costs, increased efficiency, and enhanced collaboration among healthcare professionals. Additionally, issues like creating a culture of innovation, technology, and benchmarking are considered. In addition, a framework that integrates the essential concepts of KM in healthcare FM will be presented and discussed. The field of KM is introduced as a complex adaptive system with numerous possibilities and challenges. In this context, and in consideration of healthcare FM, five objectives have been formulated to achieve the research aim. As part of the research, a number of objectives will be evaluated, including appraising the concept of KM and how knowledge is created, stored, transferred, and utilised in healthcare FM, evaluating the impact of organisational structure on job satisfaction as well as exploring how cultural differences impact knowledge sharing and performance in healthcare FM organisations. This study uses a combination of qualitative methods, such as meetings, observations, document analysis (internal and external), and semi-structured interviews, to discover the subjective experiences of healthcare FM employees and to understand the phenomenon within a real-world context and attitudes of healthcare FM as the data collection method, using open questions to allow probing where appropriate and facilitating KM development in the delivery and practice of healthcare FM. The study describes the research methodology using the theoretical concept of the "research onion". The qualitative research was conducted in the NHS acute and non-acute hospitals in Northwest England. Findings from the research study revealed that while the concept of KM has grown significantly in recent years, KM in healthcare FM has received little or no attention. The target population was fifty (five FM directors, five academics, five industry experts, ten managers, ten supervisors, five team leaders and ten operatives). These seven groups were purposively selected as the target population because they play a crucial role in KM enhancement in healthcare FM. Face-to-face interviews were conducted with all participants based on their pre-determined availability. Out of the 50-target population, only 25 were successfully interviewed to the point of saturation. Data collected from the interview were coded and analysed using NVivo to identify themes and patterns related to KM in healthcare FM. The study is divided into eight major sections. First, it discusses literature findings regarding healthcare FM and KM, including underlying trends in FM, KM in general, and KM in healthcare FM. Second, the research establishes the study's methodology, introducing the five research objectives, questions and hypothesis. The chapter introduces the literature on methodology elements, including philosophical views and inquiry strategies. The interview and data analysis look at the feedback from the interviews. Lastly, a conclusion and recommendation summarise the research objectives and suggest further research. Overall, this study highlights the importance of KM in healthcare FM and provides insights for healthcare FM directors, managers, supervisors, academia, researchers and operatives on effectively leveraging knowledge to improve patient care and organisational effectiveness
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