Combination of the Level-Set Methods with the Contourlet Transform for the Segmentation of the IVUS Images

Intravascular ultrasound (IVUS) imaging is a catheter-based medical methodology establishing itself as a useful modality for studying atherosclerosis. The detection of lumen and media-adventitia boundaries in IVUS images constitutes an essential step towards the reliable quantitative diagnosis of atherosclerosis. In this paper, a novel scheme is proposed to automatically detect lumen and media-adventitia borders. This segmentation method is based on the level-set model and the contourlet multiresolution analysis. The contourlet transform decomposes the original image into low-pass components and band-pass directional bands. The circular hough transform (CHT) is adopted in low-pass bands to yield the initial lumen and media-adventitia contours. The anisotropic diffusion filtering is then used in band-pass subbands to suppress noise and preserve arterial edges. Finally, the curve evolution in the level-set functions is used to obtain final contours. The proposed method is experimentally evaluated via 20 simulated images and 30 real images from human coronary arteries. It is demonstrated that the mean distance error and the relative mean distance error have increased by 5.30 pixels and 7.45%, respectively, as compared with those of a recently traditional level-set model. These results reveal that the proposed method can automatically and accurately extract two vascular boundaries

Computer Vision Techniques for Transcatheter Intervention

Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and treatment of cardiovascular diseases. For example, TAVI is an alternative to AVR for the treatment of severe aortic stenosis and TAFA is widely used for the treatment and cure of atrial fibrillation. In addition, catheter-based IVUS and OCT imaging of coronary arteries provides important information about the coronary lumen, wall and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial for the evaluation and treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation, motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods.We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence it is important to understand the application domain, clinical background, and imaging modality so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on background information of transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area

Assessment of the nanomechanical properties of healthy and atherosclerotic coronary arteries by atomic force microscopy

Coronary atherosclerosis is a major cause of mortality and morbidity worldwide. Despite its systemic nature, atherosclerotic plaques form and develop at “predilection” sites often associated with disturbed biomechanical forces. Therefore, computational approaches that analyse the biomechanics (blood flow and tissue mechanics) of atherosclerotic plaques have come to the forefront over the last 20 years. Assignment of appropriate material properties is an integral part of the simulation process. Current approaches for derivation of material properties rely on macro-mechanical testing and are agnostic to local variations of plaque stiffness to which collagen microstructure plays an important role. In this work we used Atomic Force Microscopy to measure the stiffness of healthy and atherosclerotic coronary arteries and we hypothesised that are those are contingent on the local microstructure. Given that the optimal method for studying mechanics of arterial tissue with this method has not been comprehensively established, an indentation protocol was firstly developed and optimised for frozen tissue sections as well as a co-registration framework with the local collagen microstructure utilising the same tissue section for mechanical testing and histological staining for collagen. Overall, the mechanical properties (Young’s Modulus) of the healthy vessel wall (median = 11.0 kPa, n=1379 force curves) were found to be significantly stiffer (p=1.3410-10) than plaque tissue (median=4.3 kPa, n=1898 force curves). Within plaques, lipid-rich areas (median=2.2 kPa, n=392 force curves) were found significantly softer (p=1.4710-4) than areas rich in collagen, such as the fibrous cap (median=4.9 kPa, n=1506 force curves). No statistical difference (p=0.89) was found between measurements in the middle of the fibrous cap (median=4.8 kPa, n=868 force curves) and the cap shoulder (median=5.1 kPa, n=638 force curves). Macro-mechanical testing methods dominate the entire landscape of material testing techniques. Plaques are very heterogenous in composition and macro-mechanical methods are agnostic to microscale variations in plaque stiffness. Mechanical testing by indentation may be better suited to quantify local variations in plaque stiffness, that are potent drivers of plaque rupture.Open Acces

Lumen-intima and media-adventitia segmentation in IVUS images using supervised classifications of arterial layers and morphological structures

Background: Intravascular ultrasound (IVUS) provides axial grey-scale images of blood vessels. The large number of images require automatic analysis, specifically to identify the lumen and outer vessel wall. However, the high amount of noise, the presence of artifacts and anatomical structures, such as bifurcations, calcifications and fibrotic plaques, usually hinder the proper automatic segmentation of the vessel wall. Methods: Lumen, media, adventitia and surrounding tissues are automatically detected using Support Vector Machines (SVMs). The classification performance of the SVMs vary according to the kind of structure present within each region of the image. Random Forest (RF) is used to detect different morphological structures and to modify the initial layer classification depending on the detected structure. The resulting classification maps are fed into a segmentation method based on deformable contours to detect lumen-intima (LI) and media-adventitia (MA) interfaces. Results: The modifications in the layer classifications according to the presence of structures proved to be effective improving LI and MA segmentations. The proposed method reaches a Jaccard Measure (JM) of 0.88 ± 0.08 for LI segmentation, compared with 0.88 ± 0.05 of a semiautomatic method. When looking at MA, our method reaches a JM of 0.84 ± 0.09, and outperforms previous automatic methods in terms of HD, with 0.51mm ± 0.30. Conclusions: A simple modification to the arterial layer classification produces results that match and improve state-of-the-art fully-automatic segmentation methods for LI and MA in 20MHz IVUS images. For LI segmentation, the proposed automatic method performs accurately as semi-automatic methods. For MA segmentation, our method matched the quality of state-of-the-art automatic methods described in the literature. Furthermore, our implementation is modular and open-source, allowing for future extensions and improvements.Fil: Lo Vercio, Lucas. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; ArgentinaFil: del Fresno, Mirta Mariana. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; ArgentinaFil: Larrabide, Ignacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentin

Geometrical Modeling and Numerical Simulation of Heterogeneous Materials

The discretization of the considered body or material by finite elements is a crucial part of the Finite Element Method in addition to the material modeling and the element formulation. Thereby, the treatment of heterogeneous materials is an advanced challenge, because the interfaces between the individual constituents must be taken into account in addition to the free surfaces. In many materials these interfaces exhibit complex geometries, since they are built up in growth and transformation processes. In the present work the numerical analysis of such materials is presented starting from the geometrical construction and ending up with the evaluation of the computational results. The first part is concerned with the simulation of diseased blood vessels and focusses on the reconstruction of patient-specific arterial geometries. The results of two- and three-dimensional finite element simulations show the field of application of the presented method. The consideration of the heterogeneity of modern two-phase steels in the numerical simulation is given in the following part of the present work. Therein the focus is on the application of geometrically simplified structures, which exhibit a similar mechanical response compared to the real microstructure. The applicability of the proposed method is shown in different boundary value problems using a direct micro-macro transition approach.Bei Simulationen unter Verwendung der Finite-Elemente-Methode spielt neben der Materialmodellierung und Elementformulierung die Diskretisierung des zu untersuchenden Körpers oder Materials durch finite Elemente eine große Rolle. Diese Aufgabe wird erschwert, wenn es sich um heterogene Materialien handelt. Bei diesen müssen zusätzlich zu den äußeren freien Oberflächen die inneren Grenzschichten zwischen den jeweiligen Individuen berücksichtigt werden. In vielen Materialien sind diese Grenzflächen durch Wachstums- oder Umwandlungsprozesse entstanden, können somit auch komplexe Strukturen aufweisen und erschweren die geometrische Beschreibung. Die vorliegende Arbeit beschäftigt sich im Wesentlichen mit der numerischen Analyse solcher Materialien ausgehend von der Konstruktion der Geometrien bis hin zur Auswertung der Simulationsergebnisse. Der erste Teil der Arbeit beschäftigt sich mit der Simulation von erkrankten Blutgefäßen und geht dort vor allem auf die Rekonstruktion von patienten-spezifischen Arteriengeometrien ein. Die Ergebnisse von zwei- und dreidimensionalen FE-Berechnung verdeutlichen das Einsatzgebiet der vorgestellten Methodik. Die Berücksichtigung der Heterogenität moderner Zweiphasenstähle in der numerischen Simulation wird im anschließenden Teil der Arbeit vorgestellt. Hierbei liegt der Schwerpunkt auf dem Einsatz von geometrisch vereinfachten Ersatzstrukturen, die ein vergleichbares mechanisches Antwortverhalten zur realen Mikrostruktur liefern. Die Anwendbarkeit dieser Methode wird in verschiedenen Randwertproblemen unter Einsatz eines direkten Mikro-Makro Übergangs gezeigt

Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features

Atherosclerotic plaque rupture is the most common mechanism responsible for a majority of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound (VH-IVUS) images are clinically available for visualising colour-coded coronary artery tissue. However, it has limitations in terms of providing clinically relevant information for identifying vulnerable plaque. The aim of this research is to improve the identification of TCFA using VH-IVUS images. To more accurately segment VH-IVUS images, a semi-supervised model is developed by means of hybrid K-means with Particle Swarm Optimisation (PSO) and a minimum Euclidean distance algorithm (KMPSO-mED). Another novelty of the proposed method is fusion of different geometric and informative texture features to capture the varying heterogeneity of plaque components and compute a discriminative index for TCFA plaque, while the existing research on TCFA detection has only focused on the geometric features. Three commonly used statistical texture features are extracted from VH-IVUS images: Local Binary Patterns (LBP), Grey Level Co-occurrence Matrix (GLCM), and Modified Run Length (MRL). Geometric and texture features are concatenated in order to generate complex descriptors. Finally, Back Propagation Neural Network (BPNN), kNN (K-Nearest Neighbour), and Support Vector Machine (SVM) classifiers are applied to select the best classifier for classifying plaque into TCFA and Non-TCFA. The present study proposes a fast and accurate computer-aided method for plaque type classification. The proposed method is applied to 588 VH-IVUS images obtained from 10 patients. The results prove the superiority of the proposed method, with accuracy rates of 98.61% for TCFA plaque.This research was funded by Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-02G31, and the Ministry of Higher Education Malaysia (MOHE) under the Fundamental Research Grant Scheme (FRGS Vot-4F551) for the completion of the research. The work and the contribution were also supported by the project Smart Solutions in Ubiquitous Computing Environments, Grant Agency of Excellence, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (under ID: UHK-FIM-GE-2018). Furthermore, the research is also partially supported by the Spanish Ministry of Science, Innovation and Universities with FEDER funds in the project TIN2016-75850-R

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

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

Coronary Plaque Boundary Enhancement in IVUS Image by Using a Modified Perona-Malik Diffusion Filter

We propose a modified Perona-Malik diffusion (PMD) filter to enhance a coronary plaque boundary by considering the conditions peculiar to an intravascular ultrasound (IVUS) image. The IVUS image is commonly used for a diagnosis of acute coronary syndrome (ACS). The IVUS image is however very grainy due to heavy speckle noise. When the normal PMD filter is applied for speckle noise reduction in the IVUS image, the coronary plaque boundary becomes vague. For this problem, we propose a modified PMD filter which is designed in special reference to the coronary plaque boundary detection. It can then not only reduce the speckle noise but also enhance clearly the coronary plaque boundary. After applying the modified PMD filter to the IVUS image, the coronary plaque boundaries are successfully detected further by applying the Takagi-Sugeno fuzzy model. The accuracy of the proposed method has been confirmed numerically by the experiments