2,350 research outputs found

    Optimización en GPU de algoritmos para la mejora del realce y segmentación en imágenes hepáticas

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    This doctoral thesis deepens the GPU acceleration for liver enhancement and segmentation. With this motivation, detailed research is carried out here in a compendium of articles. The work developed is structured in three scientific contributions, the first one is based upon enhancement and tumor segmentation, the second one explores the vessel segmentation and the last is published on liver segmentation. These works are implemented on GPU with significant speedups with great scientific impact and relevance in this doctoral thesis The first work proposes cross-modality based contrast enhancement for tumor segmentation on GPU. To do this, it takes target and guidance images as an input and enhance the low quality target image by applying two dimensional histogram approach. Further it has been observed that the enhanced image provides more accurate tumor segmentation using GPU based dynamic seeded region growing. The second contribution is about fast parallel gradient based seeded region growing where static approach has been proposed and implemented on GPU for accurate vessel segmentation. The third contribution describes GPU acceleration of Chan-Vese model and cross-modality based contrast enhancement for liver segmentation

    Time-dependent topological snalysis for cardiovascular disease diagnosis using magnetic resonance

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    Treballs finals del Màster en Matemàtica Avançada, Facultat de Matemàtiques, Universitat de Barcelona: Curs: 2022-2023. Director: Carles Casacuberta i Polyxeni Gkontra[en] The present research project aims to study the topology of time varying Cardiovascular Magnetic Resonance images (CMR) for disease diagnosis. CMR is a non-invasive technique that involves the acquisition of multiple 3D images at different cardiac phases throughout the cardiac cycle. Nonetheless, conventional assessment of CMR images typically involves the quantification of parameters related to the volumes, and more recently to the shape and texture by means of radiomics (Raisi-Estabragh, 2020), of the cardiac chambers at only two static time-point points: the end-systole and the enddiastole. Therefore, potentially rich information regarding the cardiac function and structure from other phases of the cardiac cycle might be lost. To overcome this limitation, we propose to leverage Topological Data Analysis (TDA) to optimally exploit information from the entire cardiac cycle, by measuring the variation of persistence descriptors. This approach seems promising since a time series might not exhibit relevant geometrical features in its respective point cloud embedding, but it may rather display topological cyclic patterns and their respective variations that can be captured with the proposed machinery. Subsequently, the novel TDA-based CMR descriptors encompassing the entire cardiac cycle are used to feed supervised machine learning classifiers for cardiovascular disease diagnosis. A full framework from data gathering, to image processing, mathematical modelling and classifier implementation is presented for this purpose. The performance of the proposed approach based on TDA features and ML is limited. Nonetheless, the approach could be easily adapted to other diseases and scenario where the integration of ML and TDA could be more beneficial

    POST-IVUS: A perceptual organisation-aware selective transformer framework for intravascular ultrasound segmentation

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    Intravascular ultrasound (IVUS) is recommended in guiding coronary intervention. The segmentation of coronary lumen and external elastic membrane (EEM) borders in IVUS images is a key step, but the manual process is time-consuming and error-prone, and suffers from inter-observer variability. In this paper, we propose a novel perceptual organisation-aware selective transformer framework that can achieve accurate and robust segmentation of the vessel walls in IVUS images. In this framework, temporal context-based feature encoders extract efficient motion features of vessels. Then, a perceptual organisation-aware selective transformer module is proposed to extract accurate boundary information, supervised by a dedicated boundary loss. The obtained EEM and lumen segmentation results will be fused in a temporal constraining and fusion module, to determine the most likely correct boundaries with robustness to morphology. Our proposed methods are extensively evaluated in non-selected IVUS sequences, including normal, bifurcated, and calcified vessels with shadow artifacts. The results show that the proposed methods outperform the state-of-the-art, with a Jaccard measure of 0.92 for lumen and 0.94 for EEM on the IVUS 2011 open challenge dataset. This work has been integrated into a software QCU-CMS2 to automatically segment IVUS images in a user-friendly environment

    Thirty-third Annual Symposium of Trinity College Research

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    2020 annual volume of abstracts for science research projects conducted by students at Trinity College

    Population neuroimaging:generation of a comprehensive data resource within the ALSPAC pregnancy and birth cohort

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    Neuroimaging offers a valuable insight into human brain development by allowing in vivo assessment of structure, connectivity and function. Multimodal neuroimaging data have been obtained as part of three sub-studies within the Avon Longitudinal Study of Parents and Children, a prospective multigenerational pregnancy and birth cohort based in the United Kingdom. Brain imaging data were acquired when offspring were between 18 and 24 years of age, and included acquisition of structural, functional and magnetization transfer magnetic resonance, diffusion tensor, and magnetoencephalography imaging. This resource provides a unique opportunity to combine neuroimaging data with extensive phenotypic and genotypic measures from participants, their mothers, and fathers

    A Method for Detecting Murmurous Heart Sounds based on Self-similar Properties

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    A heart murmur is an atypical sound produced by the flow of blood through the heart. It can be a sign of a serious heart condition, so detecting heart murmurs is critical for identifying and managing cardiovascular diseases. However, current methods for identifying murmurous heart sounds do not fully utilize the valuable insights that can be gained by exploring intrinsic properties of heart sound signals. To address this issue, this study proposes a new discriminatory set of multiscale features based on the self-similarity and complexity properties of heart sounds, as derived in the wavelet domain. Self-similarity is characterized by assessing fractal behaviors, while complexity is explored by calculating wavelet entropy. We evaluated the diagnostic performance of these proposed features for detecting murmurs using a set of standard classifiers. When applied to a publicly available heart sound dataset, our proposed wavelet-based multiscale features achieved comparable performance to existing methods with fewer features. This suggests that self-similarity and complexity properties in heart sounds could be potential biomarkers for improving the accuracy of murmur detection

    Circ Res

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    Atrial fibrillation (AF) is the most common sustained arrhythmia in humans. The mechanisms that govern AF initiation and persistence are highly complex, of dynamic nature, and involve interactions across multiple temporal and spatial scales in the atria. This article aims to review the mathematical modeling and computer simulation approaches to understanding AF mechanisms and aiding in its management. Various atrial modeling approaches are presented, with descriptions of the methodological basis and advancements in both lower-dimensional and realistic geometry models. A review of the most significant mechanistic insights made by atrial simulations is provided. The article showcases the contributions that atrial modeling and simulation have made not only to our understanding of the pathophysiology of atrial arrhythmias, but also to the development of AF management approaches. A summary of the future developments envisioned for the field of atrial simulation and modeling is also presented. The review contends that computational models of the atria assembled with data from clinical imaging modalities that incorporate electrophysiological and structural remodeling could become a first line of screening for new AF therapies and approaches, new diagnostic developments, and new methods for arrhythmia prevention.DP1 HL123271/HL/NHLBI NIH HHS/United StatesDP1HL123271/DP/NCCDPHP CDC HHS/United States2015-04-25T00:00:00Z24763468PMC4043630vault:242
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