Data-efficient deep representation learning

Current deep learning methods succeed in many data-intensive applications, but they are still not able to produce robust performance due to the lack of training samples. To investigate how to improve the performance of deep learning paradigms when training samples are limited, data-efficient deep representation learning (DDRL) is proposed in this study. DDRL as a sub area of representation learning mainly addresses the following problem: How can the performance of a deep learning method be maintained when the number of training samples is significantly reduced? This is vital for many applications where collecting data is highly costly, such as medical image analysis. Incorporating a certain kind of prior knowledge into the learning paradigm is key to achieving data efficiency. Deep learning as a sub-area of machine learning can be divided into three parts (locations) in its learning process, namely Data, Optimisation and Model. Integrating prior knowledge into these three locations is expected to bring data efficiency into a learning paradigm, which can dramatically increase the model performance under the condition of limited training data. In this thesis, we aim to develop novel deep learning methods for achieving data-efficient training, each of which integrates a certain kind of prior knowledge into three different locations respectively. We make the following contributions. First, we propose an iterative solution based on deep learning for medical image segmentation tasks, where dynamical systems are integrated into the segmentation labels in order to improve both performance and data efficiency. The proposed method not only shows a superior performance and better data efficiency compared to the state-of-the-art methods, but also has better interpretability and rotational invariance which are desired for medical imagining applications. Second, we propose a novel training framework which adaptively selects more informative samples for training during the optimization process. The adaptive selection or sampling is performed based on a hardness-aware strategy in the latent space constructed by a generative model. We show that the proposed framework outperforms a random sampling method, which demonstrates effectiveness of the proposed framework. Thirdly, we propose a deep neural network model which produces the segmentation maps in a coarse-to-fine manner. The proposed architecture is a sequence of computational blocks containing a number of convolutional layers in which each block provides its successive block with a coarser segmentation map as a reference. Such mechanisms enable us to train the network with limited training samples and produce more interpretable results.Open Acces

Bioresorbable coronary stents : non-invasive quantitative assessment of edge and intrastent plaque – a 256-slice computed tomography longitudinal study

Les bioresorbable stents (BRS), en français intitulés tuteurs coronariens biorésorbables, sont constitués d’un polymère biorésorbable, plutôt que de métal, et ne créent pas d’artéfacts métalliques significatifs en tomodensitométrie (TDM). Cela permet une meilleure évaluation de la plaque coronarienne sous ces tuteurs en TDM qu’avec les anciens tuteurs qui sont en métal. OBJECTIF: Évaluer l’évolution de la composition de la plaque, sa fraction lipidique (FL)— marqueur de vulnérabilité de la plaque, dans les 3 zones pré-tuteur (bord proximal), intra-tuteur et post-tuteur (bord distal), et le volume de la plaque entre 1 et 12 mois post-implantation de BRS. MÉTHODOLOGIE: Il s’agit d’une étude observationnelle longitudinale réalisée chez 27 patients consécutifs (âge moyen 59,7 +/- 8,6 ans) et recrutés prospectivement pour une imagerie par TDM 256-coupes à 1 et 12 mois post-implantation de BRS (35 tuteurs total). Les objectifs primaires sont: volume de plaque totale et de FL (mm3) comparés entre 1 et 12 mois. Afin de tenir compte de la corrélation intra-patient, des analyses de variance des modèles linéaires mixtes avec ou sans spline sont utilisés avec deux facteurs répétés temps et zone/bloc (1 bloc= 5 mm en axe longitudinal). La valeur % FL= volume absolu du FL/ volume total de la plaque. RÉSULTATS: Notre analyse par bloc ou par spline n’a pas démontré une différence significative dans les volumes de plaque ou des FL dans les zones pre- intra- and post-tuteur entre 1 et 12 mois. CONCLUSION: Notre étude a réussi à démontrer la faisabilité d’une analyse non-invasive quantitative répétée de la plaque coronarienne et de la lumière intra-tuteur avec l’utilisation de TDM 256 coupes. Cette étude pilote n’a pas démontré de différence significative dans les volumes des plaques et atténuation entre 1- et 12- mois de follow-up post-implantation de BRS. Notre méthode pourrait être appliquée à l’évaluation des différents structures ou profils pharmacologiques de ces tuteurs.Coronary bioresorbable stents (BRS) are made of a bioresorbable polymer rather than metal. Unlike metallic stents, BRS do not produce significant artifacts in computed tomography (CT) and are radiolucent in CT, making it possible to evaluate coronary plaque beneath an implanted stent. PURPOSE: The purpose of our study was to evaluate the volumes of plaque and low attenuation plaque components (LAP —a marker of plaque vulnerability) of pre-, intra- and post-stent plaque location between 1 and 12 months post-implantation. METHODS: In our prospective longitudinal study, we recruited 27 consecutive patients (mean age 59.7 +/- 8.6 years) with bioresorbable stents (n=35) for a 256-slice ECG-synchronized CT evaluation at 1 month and at 12 months post stent implantation. Total plaque volume (mm3) as well as absolute and relative (%) LAP volume per block in the pre-, intra- and post-stent zones were analyzed; comparison of 1 and 12 months post BRS implantation. Changes in these variables were assessed using mixed effects models with and without spline, which also accounted for correlation between repeated measurements with factors such as time and zone/block (1 block = 5 mm in longitudinal axis). The value % LAP= LAP absolute volume/ total plaque volume. RESULTS: Our block or spline model analysis showed no significant difference in plaque or LAP volumes in pre-, intra- and post-stent zones measured at 1 month and at 12 months. CONCLUSION: Our study demonstrates the feasibility of repeated non-invasive quantitative analysis of intrastent coronary plaque and in-stent lumen using a 256-channel CT scan. This pilot study did not show significant differences in plaque volume and attenuation between 1- and 12-month follow-up from stent implantation. The method we used could be applied to the evaluation of different stent structures or different pharmacological profiles of bioresorbable stents

A Fuzzy Belief-Desire-Intention Model for Agent-Based Image Analysis

Recent methods of image analysis in remote sensing lack a sufficient grade of robustness and transferability. Methods such as object-based image analysis (OBIA) achieve satisfying results on single images. However, the underlying rule sets for OBIA are usually too complex to be directly applied on a variety of image data without any adaptations or human interactions. Thus, recent research projects investigate the potential for integrating the agent-based paradigm with OBIA. Agent-based systems are highly adaptive and therefore robust, even under varying environmental conditions. In the context of image analysis, this means that even if the image data to be analyzed varies slightly (e.g., due to seasonal effects, different locations, atmospheric conditions, or even a slightly different sensor), agent-based methods allow to autonomously adapt existing analysis rules or segmentation results according to changing imaging situations. The basis for individual software agents’ behavior is a so-called believe-desire-intention (BDI) model. Basically, the BDI describes for each individual agent its goal(s), its assumed current situation, and some action rules potentially supporting each agent to achieve its goals. The chapter introduces a believe-desire-intention (BDI) model based on fuzzy rules in the context of agent-based image analysis, which extends the classic OBIA paradigm by the agent-based paradigm

Carotid Artery Wall Imaging: Perspective and Guidelines from the ASNR Vessel Wall Imaging Study Group and Expert Consensus Recommendations of the American Society of Neuroradiology

SUMMARY: Identification of carotid artery atherosclerosis is conventionally based on measurements of luminal stenosis and surface irregularities using in vivo imaging techniques including sonography, CT and MR angiography, and digital subtraction angiography. However, histopathologic studies demonstrate considerable differences between plaques with identical degrees of stenosis and indicate that certain plaque features are associated with increased risk for ischemic events. The ability to look beyond the lumen using highly developed vessel wall imaging methods to identify plaque vulnerable to disruption has prompted an active debate as to whether a paradigm shift is needed to move away from relying on measurements of luminal stenosis for gauging the risk of ischemic injury. Further evaluation in randomized clinical trials will help to better define the exact role of plaque imaging in clinical decision-making. However, current carotid vessel wall imaging techniques can be informative. The goal of this article is to present the perspective of the ASNR Vessel Wall Imaging Study Group as it relates to the current status of arterial wall imaging in carotid artery disease

International Union of Angiology (IUA) consensus paper on imaging strategies in atherosclerotic carotid artery imaging: From basic strategies to advanced approaches

Cardiovascular disease (CVD) is the leading cause of mortality and disability in developed countries. According to WHO, an estimated 17.9 million people died from CVDs in 2019, representing 32% of all global deaths. Of these deaths, 85% were due to major adverse cardiac and cerebral events. Early detection and care for individuals at high risk could save lives, alleviate suffering, and diminish economic burden associated with these diseases. Carotid artery disease is not only a well-established risk factor for ischemic stroke, contributing to 10%–20% of strokes or transient ischemic attacks (TIAs), but it is also a surrogate marker of generalized atherosclerosis and a predictor of cardiovascular events. In addition to diligent history, physical examination, and laboratory detection of metabolic abnormalities leading to vascular changes, imaging of carotid arteries adds very important information in assessing stroke and overall cardiovascular risk. Spanning from carotid intima-media thickness (IMT) measurements in arteriopathy to plaque burden, morphology and biology in more advanced disease, imaging of carotid arteries could help not only in stroke prevention but also in ameliorating cardiovascular events in other territories (e.g. in the coronary arteries). While ultrasound is the most widely available and affordable imaging methods, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), their combination and other more sophisticated methods have introduced novel concepts in detection of carotid plaque characteristics and risk assessment of stroke and other cardiovascular events. However, in addition to robust progress in usage of these methods, all of them have limitations which should be taken into account. The main purpose of this consensus document is to discuss pros but also cons in clinical, epidemiological and research use of all these techniques

Quantification of atherosclerotic plaque in the elderly with positron emission tomography/computed tomography

L'athérosclérose est une maladie cardiovasculaire inflammatoire qui est devenue la première cause de morbidité et de mortalité dans les pays développés et parmi les principales causes d’invalidité au monde. Elle se caractérise par l’épaississement de la paroi vasculaire artérielle suite à l'accumulation de lipides et le dépôt d'autres substances au niveau de l’intima (endothélium) pour former la plaque d’athérome. Avec l'âge, cette plaque peut grossir, se calcifier et ainsi rétrécir le calibre de l'artère pour diminuer son débit et à un stade avancé de la maladie, elle peut se rompre et obstruer les petites artères dans n'importe quelle partie du corps causant des complications aigues, y compris la mort soudaine. L'objectif de cette thèse est de pouvoir détecter l'inflammation de la plaque athérosclérotique quantitativement avec la TEP/TDM dans le but de prévenir son détachement. Les mesures avec la TDM et la TEP avec le 18F-FDG ont été acquises chez des sujets humains âgés de 65 à 85 ans. Des analyses quantitatives ont été conduites sur les images de TDM en fonction de l'intensité et des étendues des calcifications, et sur les images de la TEP pour évaluer le métabolisme des plaques. L'effet des traitements par les statines a aussi été étudié. Au-delà la couverture de cette étude de façon détaillée au niveau physiologique en corrélant différents paramètres des plaques, et au niveau méthodologique en utilisant de nouvelles approches pour l'analyse pharmacocinétique, il en ressort principalement la suggestion de la détection de la vulnérabilité de la plaque artérielle par la TDM, plus disponible et moins coûteuse, en remplacement des analyses biochimiques, surtout la protéine C-réactive (CRP) considérée être la méthode standard.Abstract : Atherosclerosis is an inflammatory cardiovascular disease considered the leading cause of morbidity and mortality in developed countries and among the leading causes of disability worldwide. It is characterized by the thickening of the arterial vascular wall due to the accumulation of lipids and the deposition of other substances in the intima (endothelium) to form atheroma plaque. With age, this plaque can grow larger, calcify and thus narrow the size of the artery to decrease blood flow and at an advanced stage of the disease, it can rupture, be transported by blood and block the small arteries in any part of the body causing acute complications, including sudden death. The objective of this thesis was to be able to detect the inflammation of the atherosclerotic plaque quantitatively with PET/CT in order to prevent its detachment. Measurements with CT and PET with 18F-FDG were acquired in human subjects aged 65 to 85 years. Quantitative analyzes were performed on CT images based on the intensity and extent of calcifications, and on PET images to assess plaque metabolism. The effect of statin treatments has also been studied. Beyond the coverage of this study in a detailed manner at the physiological level by correlating different parameters of the plaques, and at the methodological level by using new approaches for pharmacokinetic analysis, it mainly emerges the suggestion for the detection of the vulnerability of the arterial plaque by CT alone, more available and less expensive, replacing biochemical analyzes, especially Creactive protein (CRP) considered to be the standard method

Carotid plaque imaging and the risk of atherosclerotic cardiovascular disease

Carotid artery plaque is a measure of atherosclerosis and is associated with future risk of atherosclerotic cardiovascular disease (ASCVD), which encompasses coronary, cerebrovascular, and peripheral arterial diseases. With advanced imaging techniques, computerized tomography (CT) and magnetic resonance imaging (MRI) have shown their potential superiority to routine ultrasound to detect features of carotid plaque vulnerability, such as intraplaque hemorrhage (IPH), lipid-rich necrotic core (LRNC), fibrous cap (FC), and calcification. The correlation between imaging features and histological changes of carotid plaques has been investigated. Imaging of carotid features has been used to predict the risk of cardiovascular events. Other techniques such as nuclear imaging and intra-vascular ultrasound (IVUS) have also been proposed to better understand the vulnerable carotid plaque features. In this article, we review the studies of imaging specific carotid plaque components and their correlation with risk scores

Quantifying Atherosclerosis: IVUS Imaging For Lumen Border Detection And Plaque Characterization

The importance of atherosclerotic disease in coronary artery has been a subject of study for many researchers in the past decade. In brief, the aim is to understand progression of such a disease, detect plaques at risks (vulnerable plaques), and treat them selectively to prevent mortality and immobility. Consequently, several imaging modalities have been developed and among them intravascular ultrasound (IVUS) has been of particular interest since it provides useful information about tissues microstructures and images with sufficient penetration as well as resolution. In general, the ultimate goal is to provide interventional cardiologists with reliable clinical tools so they can identify vulnerable plaques, make decisions confidently, choose the most appropriate drugs or implant devices (i.e. stent), and stabilize them during catheterization procedures with minimal risk. In this work, we review existing atherosclerotic tissue characterization algorithms including the state-of-the-art virtual histology (VH) framework, which has been implemented in the Volcano (Rancho Cordova, CA) IVUS clinical scanners using 64-elements 20 MHz phased-array transducer. Initially, we intended to extend this technique for data acquired with 40 MHz single-element transducers. For this reason, we started acquiring in vitro IVUS data and studied involved challenges from specimen preparation toward classification. We observed inconsistency among extracted features along with transducer's spectral parameters (i.e. bandwidth, center frequency). This, in addition to infeasibility of construction of reliable training dataset due to heterogeneity of atherosclerotic tissues motivated us to develop an unsupervised texture-based atherosclerotic tissue characterization algorithm. We proposed a two-dimensional multiscale wavelet-based algorithm to expand IVUS backscattered signals and/or grayscale images onto orthogonal symmetric quadrature mirror filters (QMF) such as Lemarie-Battle. At the bottom of decomposition tree, we employed ISODATA to cluster enveloped detected features in an unsupervised fashion and classify atherosclerotic plaque constitutes into fibrotic, lipidic, calcified, and no tissues. For the first time, we studied numbers of factors that were necessary for extension of in vitro derived classifier for in vivo applications such as reliability of classified tissues behind arc of calcified plaques and effects of pressure changes as well as flowing blood on constructed tissue color maps, called prognosis histology (PH) images. The second half of this dissertation is devoted to automatic detection of lumen borders in IVUS grayscale images acquired with high frequency (40 MHz up) transducers where more scattering exhibited within lumen area that makes the problem of interest more challenging. We established our framework on three-dimensional expansion of IVUS sub-volumes onto orthonormal brushlet basis function. The rational behind our framework was presence of incoherent (i.e. blood) versus coherent (i.e. plaque, surrounding fat) textural patterns along pullback direction, which was motivated by what an interventional cardiologist does to locate the lumen border visually by going back and forth among IVUS frames. We studied the feasibility of brushlet analysis through filtering blood speckles and supervised classification of blood versus non-blood regions. Our preliminary study confirmed that the most informative features reside in the innermost cubes, representing low-frequency components in transformed domain. Finally, we explored that tissue responses to IVUS signals are proportionally preserved in brushlet coefficients and it enabled us to classify blood regions in complex brushlet space. Subsequently, we employed surface function actives (SFA) to estimate the lumen borders after regularization. In a comparison study, we quantified our results with two of existing algorithms, employing IVUS grayscale images acquired with 40 MHz and 45 MHz single-element transducers. Overall, our proposed algorithm outperformed and the resulting automated detected borders showed good correlation with manually traced borders by an expert

Intravascular Ultrasound

Intravascular ultrasound (IVUS) is a cardiovascular imaging technology using a specially designed catheter with a miniaturized ultrasound probe for the assessment of vascular anatomy with detailed visualization of arterial layers. Over the past two decades, this technology has developed into an indispensable tool for research and clinical practice in cardiovascular medicine, offering the opportunity to gather diagnostic information about the process of atherosclerosis in vivo, and to directly observe the effects of various interventions on the plaque and arterial wall. This book aims to give a comprehensive overview of this rapidly evolving technique from basic principles and instrumentation to research and clinical applications with future perspectives