Search

Aims The association of low endothelial shear stress (ESS) with high-risk plaque (HRP) has not been thoroughly investigated in humans. We investigated the local ESS and lumen remodelling patterns in HRPs using optical coherence tomography (OCT), developed the shear stress score , and explored its association with the prevalence of HRPs and clinical outcomes. Methods and results A total of 35 coronary arteries from 30 patients with stable angina or acute coronary syndrome (ACS) were reconstructed with three dimensional (3D) OCT. ESS was calculated using computational fluid dynamics and classified into low, moderate, and high in 3-mm-long subsegments. In each subsegment, (i) fibroatheromas (FAs) were classified into HRPs and non-HRPs based on fibrous cap (FC) thickness and lipid pool size, and (ii) lumen remodelling was classified into constrictive, compensatory, and expansive. In each artery the shear stress score was calculated as metric of the extent and severity of low ESS. FAs in low ESS subsegments had thinner FC compared with high ESS (89 ± 84 vs.138 ± 83 µm, P < 0.05). Low ESS subsegments predominantly co-localized with HRPs vs. non-HRPs (29 vs. 9%, P < 0.05) and high ESS subsegments predominantly with non-HRPs (9 vs. 24%, P < 0.05). Compensatory and expansive lumen remodelling were the predominant responses within subsegments with low ESS and HRPs. In non-stenotic FAs, low ESS was associated with HRPs vs. non-HRPs (29 vs. 3%, P < 0.05). Arteries with increased shear stress score had increased frequency of HRPs and were associated with ACS vs. stable angina. Conclusion Local low ESS and expansive lumen remodelling are associated with HRP. Arteries with increased shear stress score have increased frequency of HRPs and propensity to present with ACS.

Clinical practice guidelines (CPGs) constitute a systematically developed, critical body of medical knowledge which is compiled and maintained in order to assist healthcare professionals in decision making. They are available for diverse diseases/conditions and routinely used in many countries, providing reference material for healthcare delivery in clinical settings. As CPGs are paper-based, i.e. plain documents, there have been various approaches for their computerization and expression in a formal manner so that they can be incorporated in clinical information and decision support systems. Semantic Web technologies and ontologies have been extensively used for CPG formalization. In this paper, we present a novel method for the representation and execution of CPGs using OWL ontologies and SPARQL-based inference rules. The proposed approach is capable of expressing complex CPG constructs and can be used to express formalisms, such as negations, which are hard to express using ontologies alone. The encapsulation of SPARQL rules in the CPG ontology is based on the SPARQL Inference Notation (SPIN). The proposed representation of different aspects of CPGs, such as numerical comparisons, calculations, decision branches and state transitions, and their execution is demonstrated through the respective parts of comprehensive, though complex enough, CPGs for arterial hypertension management. The paper concludes by comparing the proposed approach with other relevant works, indicating its potential and limitations, as well as a future work directions.

Objectives The analysis of intracoronary optical coherence tomography (OCT) images is based on manual identification of the lumen contours and relevant structures. However, manual image segmentation is a cumbersome and time-consuming process, subject to significant intra- and inter-observer variability. This study aims to present and validate a fully-automated method for segmentation of intracoronary OCT images. Methods We studied 20 coronary arteries (mean length=39.7±10.0mm) from 20 patients who underwent a clinically-indicated cardiac catheterization. The OCT images (n=1812) were segmented manually, as well as with a fully-automated approach. A semi-automated variation of the fully-automated algorithm was also applied. Using certain lumen size and lumen shape characteristics, the fully- and semi-automated segmentation algorithms were validated over manual segmentation, which was considered as the gold standard. Results Linear regression and Bland–Altman analysis demonstrated that both the fully-automated and semi-automated segmentation had a very high agreement with the manual segmentation, with the semi-automated approach being slightly more accurate than the fully-automated method. The fully-automated and semi-automated OCT segmentation reduced the analysis time by more than 97% and 86%, respectively, compared to manual segmentation. Conclusions In the current work we validated a fully-automated OCT segmentation algorithm, as well as a semi-automated variation of it in an extensive "real-life" dataset of OCT images. The study showed that our algorithm can perform rapid and reliable segmentation of OCT images.

There is an ongoing research and clinical interest in the development of reliable and easily accessible software for the 3D reconstruction of coronary arteries. In this work, we present the architecture and validation of IVUSAngio Tool, an application which performs fast and accurate 3D reconstruction of the coronary arteries by using intravascular ultrasound (IVUS) and biplane angiography data. The 3D reconstruction is based on the fusion of the detected arterial boundaries in IVUS images with the 3D IVUS catheter path derived from the biplane angiography. The IVUSAngio Tool suite integrates all the intermediate processing and computational steps and provides a user-friendly interface. It also offers additional functionality, such as automatic selection of the end-diastolic IVUS images, semi-automatic and automatic IVUS segmentation, vascular morphometric measurements, graphical visualization of the 3D model and export in a format compatible with other computer-aided design applications. Our software was applied and validated in 31 human coronary arteries yielding quite promising results. Collectively, the use of IVUSAngio Tool significantly reduces the total processing time for 3D coronary reconstruction. IVUSAngio Tool is distributed as free software, publicly available to download and use.

Ontology alignment is the process where two different ontologies that usually describe similar domains are ‘aligned’, i.e. a set of correspondences between their entities, regarding semantic equivalence, is determined. In order to identify these correspondences several methods have been proposed in literature. The most common features that these methods employ are string-, lexical-, structure- and semantic-based features for which several approaches have been developed. However, what hasn’t been investigated is the usage of visual-based features for determining entity similarity. Nowadays the existence of several resources that map lexical concepts onto images allows for exploiting visual features for this purpose. In this paper, a novel method, defining a visual-based similarity metric for ontology matching, is presented. Each ontological entity is associated with sets of images. State of the art visual feature extraction, clustering and indexing for computing the visual-based similarity between entities is employed. An adaptation of a Wordnet-based matching algorithm to exploit the visual similarity is also proposed. The proposed visual similarity approach is compared with standard metrics and demonstrates promising results.

Background: High risk plaque accounts for the majority of acute coronary events. Low endothelial shear stress (ESS) is a key factor of the natural history of atherosclerosis. The role of ESS in high risk plaque formation is not well studied in man. Hypothesis: To explore the association of low ESS with high risk plaque and to identify the ESS milieu and vascular remodeling response in high risk vs. non high risk plaque. Methods: 35 coronary arteries from 30 patients were 3D reconstructed with fusion of coronary angiography and optical coherence tomography (Fig A-D) . ESS was calculated in the 3D reconstructed arteries using computational fluid dynamics (Fig E) and classified into low, moderate and high in 3 mm long segments. In each segment: i) fibroatheromas were classified into high risk and non high risk based on fibrous cap thickness and lipid pool size ii) vascular remodeling was classified into constrictive, compensatory and expansive. Results: Fibroatheromas in low ESS segments had significantly thinner fibrous cap compared to high ESS segments (89±84 vs.138±83 μm, pFig F ). Compensatory and expansive remodeling was the predominant remodeling response in low ESS segments containing high risk plaques. In non-stenotic fibroatheromas (expansive or compensatory remodeling) low ESS was predominantly associated with high risk plaques (29 vs. 3%, p(Fig F) . Conclusions: Novel combined anatomic and functional imaging with 3D OCT showed that low ESS and non-constrictive remodeling are associated with high risk plaque in man. Further studies are needed to assess the role of ESS and vascular remodeling in high risk plaque rupture and precipitation of clinical outcomes.

Background Geometrically-correct 3D OCT is a new imaging modality with the potential to investigate the association of local hemodynamic microenvironment with OCT-derived high-risk features. We aimed to describe the methodology of 3D OCT and investigate the accuracy, inter- and intra-observer agreement of 3D OCT in reconstructing coronary arteries and calculating ESS, using 3D IVUS and 3D QCA as references. Methods-Results 35 coronary artery segments derived from 30 patients were reconstructed in 3D space using 3D OCT. 3D OCT was validated against 3D IVUS and 3D QCA. The agreement in artery reconstruction among 3D OCT, 3D IVUS and 3D QCA was assessed in 3-mm-long subsegments using lumen morphometry and ESS parameters. The inter- and intra-observer agreement of 3D OCT, 3D IVUS and 3D QCA were assessed in a representative sample of 61 subsegments (n = 5 arteries). The data processing times for each reconstruction methodology were also calculated. There was a very high agreement between 3D OCT vs. 3D IVUS and 3D OCT vs. 3D QCA in terms of total reconstructed artery length and volume, as well as in terms of segmental morphometric and ESS metrics with mean differences close to zero and narrow limits of agreement (Bland–Altman analysis). 3D OCT exhibited excellent inter- and intra-observer agreement. The analysis time with 3D OCT was significantly lower compared to 3D IVUS. Conclusions Geometrically-correct 3D OCT is a feasible, accurate and reproducible 3D reconstruction technique that can perform reliable ESS calculations in coronary arteries.

This paper presents a distributed multi sensor data processing and fusion system providing sophisticated surveillance capabilities in the urban environment. The system enables visual/non-visual event detection, situation assessment, and semantic event-based reasoning for force protection and civil surveillance applications. The novelties lie in the high level system view approach, not only concentrating on data fusion methodologies per se, but rather on a holistic view of sensor data fusion that provides both lower (sensor) level and higher level (semantic) fusion. At the same time, we concentrate on easy and quick extensibility with new sensors and processing capabilities. The system also makes provisions for visualizing and processing space-time alerts from sensor detections up to high level alerts based on rule-based semantic reasoning over sensor data and fusion events. The proposed architecture has been validated in a number of different synthetic and live urban scenarios.

This paper introduces a novel sensor information fusion system enabling security and surveillance in large scale sensor saturated urban environments. The system is built over state-of-the art sensor networks middleware and provides information fusion at multiple layers. A distinguishing characteristic of the system is that it support seamless integration with semantic web middleware (including ontologies and inference mechanisms), which enable intelligent high-level accurate reasoning. This is a key functionality for efficient surveillance in large scale environment, where manual inspection of individual tracking systems becomes extremely resourceful and overall impractical. A proof-of-concept implementation of the system manifests its benefits and technical challenges, while also outlining lessons learnt.

In the Business Process modeling area, the Business Process Execution Language (BPEL) seems to be gaining ground in favor of its other competitors. The simplicity of the protocol coupled with its ability to hide the complex mechanisms that lie underneath along with advanced control features and integration with other concrete standards as XPath and XSLT have contributed to its acceptance. BPEL has been thoroughly investigated in literature as a solution to workflow management in an enterprise environment. In this paper, an intrinsic property of BPEL, related to compensation handling, is investigated as an alternative approach to two-phase commit used in distributed transactions. The applicability of the proposed solution is demonstrated on a travel booking scenario and interesting conclusions are drawn regarding the system’s response in cases of service failures.

Nowadays, an increasingly growing demand for the creation of digital multimedia libraries is arising, as huge amounts of digital visual content are becoming available. The content that resides in these libraries should be easily retrievable and classified in order to be fully accessible. This paper introduces a hybrid multimedia retrieval model accompanied by the presentation of a search engine that is capable of retrieving visual content cultural heritage multimedia libraries as in three modes: (i) based on semantic annotation with the help of an ontology; (ii) based on the visual features with a view to finding similar content; and (iii) based on the combination of these two strategies. The main novelty is the way in which these two co-operate transparently during the evaluation of a single query in a hybrid fashion, making recommendations to the user and retrieving content that is both visually and semantically similar.

The work presented in this paper is part of a system able to perform risk classification of patients based on medical image analysis and on the semantically structured information of patient data from medical records and biochemical data. More specifically, the paper focuses on Intravascular Ultrasound (IVUS) image processing and the automated segmentation developed to extract the useful arterial boundaries. This is coupled with the design and implementation of a semantic reasoning-enabled knowledge base in OWL that integrates data from heterogeneous sources and incorporates functionality for DL classification. Performance evaluation of both IVUS image processing and knowledge base is discussed.

This paper introduces a hybrid multimedia retrieval model that is capable of retrieving cultural heritage multimedia content, based on their semantic annotation with the help of an ontology and on low level visual features with a view to finding similar content. The main novelty is the way in which these techniques cooperate transparently during the evaluation of a single query in a hybrid fashion, making recommendations to the user. A search engine has been developed implementing this model, which is capable of searching through cultural heritage multimedia collections, and indicative examples are discussed, along with insights into its performance.

Ontology alignment is the process where two different ontologies that usually describe similar domains are ’aligned’, i.e. a set of correspondences between their entities, regarding semantic equivalence, is determined. In order to identify these correspondences several methods and metrics that measure semantic equivalence have been proposed in literature. The most common features that these metrics employ are string-, lexical-, structure- and semantic-based similarities for which several approaches have been developed. However, what hasn’t been investigated is the usage of visual-based features for determining entity similarity in cases where images are associated with concepts. Nowadays the existence of several resources (e.g. ImageNet) that map lexical concepts onto images allows for exploiting visual similarities for this purpose. In this paper, a novel approach for ontology matching based on visual similarity is presented. Each ontological entity is associated with sets of images, retrieved through ImageNet or web-based search, and state of the art visual feature extraction, clustering and indexing for computing the similarity between entities is employed. An adaptation of a popular Wordnet-based matching algorithm to exploit the visual similarity is also proposed. Our method is compared with traditional metrics against a standard ontology alignment benchmark dataset and demonstrates promising results. This work was supported by MULTISENSOR (contract no. FP7-610411) and KRISTINA (contract no./nH2020-645012) projects, partially funded by the European Commission.