MR imaging of left-ventricular function : novel image acquisition and analysis techniques.

Many cardiac diseases, such as myocardial ischemia, secondary to coronary artery disease, may be identified and localized through the analysis of cardiac deformations. Early efforts for quantifying ventricular wall motion used surgical implantation and tracking of radiopaque markers with X-ray imaging in canine hearts [1]. Such techniques are invasive and affect the regional motion pattern of the ventricular wall during the marker tracking process and, clearly are not feasible clinically. Noninvasive imaging techniques are vital and have been widely applied to the clinic. MRI is a noninvasive imaging technique with the capability to monitor and assess the progression of cardiovascular diseases (CVD) so that effective procedures for the care and treatment of patients can be developed by physicians and researchers. It is capable of providing 3D analysis of global and regional cardiac function with great accuracy and reproducibility. In the past few years, numerous efforts have been devoted to cardiac motion recovery and deformation analysis from MR imaging sequences. In order to assess cardiac function, there are two categories of indices that are used: global and regional indices. Global indices include ejection fraction, cavity volume, and myocardial mass [2]. They are important indices for cardiac disease diagnosis. However, these global indices are not specific for regional analysis. A quantitative assessment of regional parameters may prove beneficial for the diagnosis of disease and evaluation of severity and the quantification of treatment [3]. Local measures, such as wall deformation and strain in all regions of the heart, can provide objective regional quantification of ventricular wall function and relate to the location and extent of ischemic injury. This dissertation is concerned with the development of novel MR imaging techniques and image postprocessing algorithms to analyze left ventricular deformations. A novel pulse sequence, termed Orthogonal CSPAMM (OCSPAMM), has been proposed which results in the same acquisition time as SPAMM for 2D deformation estimation while keeping the main advantages of CSPAMM [4,5]: i.e., maintaining tag contrast through-out the ECG cycle. Different from CSPAMM, in OCSPAMM the second tagging pulse orientation is rotated 90 degrees relative to the first one so that motion information can be obtained simultaneously in two directions. This reduces the acquisition time by a factor of two as compared to the traditional CSPAMM, in which two separate imaging sequences are applied per acquisition. With the application of OCSPAMM, the effect of tag fading encountered in SPAMM tagging due to Tl relaxation is mitigated and tag deformations can be visualized for the entire cardiac cycle, including diastolic phases. A multilevel B-spline fitting method (MBS) has been proposed which incorporates phase-based displacement information for accurate calculation of 2D motion and strain from tagged MRI [6, 7]. The proposed method combines the advantages of continuity and smoothness of MBS, and makes use of phase information derived from tagged MR images. Compared to previous 2D B-spline-based deformation analysis methods, MBS has the following advantages: 1) It can simultaneously achieve a smooth deformation while accurately approximating the given data set; 2) Computationally, it is very fast; and 3) It can produce more accurate deformation results. Since the tag intersections (intersections between two tag lines) can be extracted accurately and are more or less distributed evenly over the myocardium, MBS has proven effective for 2D cardiac motion tracking. To derive phase-based displacements, 2D HARP and SinMod analysis techniques [8,9] were employed. By producing virtual tags from HARP /SinMod and calculating intersections of virtual tag lines, more data points are obtained. In the reference frame, virtual tag lines are the isoparametric curves of an undeformed 2D B-spline model. In subsequent frames, the locations of intersections of virtual tag lines over the myocardium are updated with phase-based displacement. The advantage of the technique is that in acquiring denser myocardial displacements, it uses both real and virtual tag line intersections. It is fast and more accurate than 2D HARP and SinMod tracking. A novel 3D sine wave modeling (3D SinMod) approach for automatic analysis of 3D cardiac deformations has been proposed [10]. An accelerated 3D complementary spatial modulation of magnetization (CSPAMM) tagging technique [11] was used to acquire complete 3D+t tagged MR data sets of the whole heart (3 dynamic CSPAMM tagged MRI volume with tags in different orientations), in-vivo, in 54 heart beats and within 3 breath-holds. In 3D SinMod, the intensity distribution around each pixel is modeled as a cosine wave front. The principle behind 3D SinMod tracking is that both phase and frequency for each voxel are determined directly from the frequency analysis and the displacement is calculated from the quotient of phase difference and local frequency. The deformation fields clearly demonstrate longitudinal shortening during systole. The contraction of the LV base towards the apex as well as the torsional motion between basal and apical slices is clearly observable from the displacements. 3D SinMod can automatically process the image data to derive measures of motion, deformations, and strains between consecutive pair of tagged volumes in 17 seconds. Therefore, comprehensive 4D imaging and postprocessing for determination of ventricular function is now possible in under 10 minutes. For validation of 3D SinMod, 7 3D+t CSPAMM data sets of healthy subjects have been processed. Comparison of mid-wall contour deformations and circumferential shortening results by 3D SinMod showed good agreement with those by 3D HARP. Tag lines tracked by the proposed technique were also compared with manually delineated ones. The average errors calculated for the systolic phase of the cardiac cycles were in the sub-pixel range

Early Detection of Doxorubicin-Induced Cardiotoxicity Using Combined Biomechanical Modeling and Multi-Parametric Cardiovascular MRI

RÉSUMÉ La chimiothérapie à la doxorubicine est efficace et est largement utilisée pour traiter la leucémie lymphoblastique aiguë. Toutefois, son efficacité est entravée par un large spectre de cardiotoxicités incluant des changements affectant à la fois la morphologie et la fonction du myocarde. Ces changements dépendent principalement de la dose cumulée administrée au patient. Actuellement, très peu de techniques sont disponibles pour détecter de telles cardiotoxicités. L'utilisation d’images de fibres musculaires (par exemple, à l’aide de l’imagerie des tenseurs de diffusion par IRM) ou des techniques d'imagerie 3D (par exemple, ciné DENSE IRM) sont des alternatives prometteuses, cependant, leur application en clinique est limitée en raison du temps d'acquisition d’images et les erreurs d'estimation qui en résultent. En revanche, l'utilisation de l'IRM multi-paramétrique ainsi que le ciné IRM sont des alternatives prometteuses, puisque ces techniques sont déjà disponibles au niveau clinique. L’IRM multiparamétrique incluant l’imagerie des temps de relaxation T1 et T2 peut être utile dans la détection des lésions dans le tissu du myocarde alors que l’imagerie ciné IRM peut être plus appropriée pour détecter les changements fonctionnels au sein du myocarde. La combinaison de ces deux techniques peut également permettre une caractérisation complète de la fonction du tissu myocardique. Dans ce projet, l'utilisation des temps de relaxation T1 pré- et post-gadolinium et T2 est d'abord évaluée et proposée pour détecter les dommages myocardiques induits par la chimiothérapie à la doxorubicine. En second lieu, l'utilisation de patrons 2D de déplacements myocardiques est évaluée dans le cadre de la détection des dommages myocardiques et altération fonctionnelle due au traitement à la doxorubicine. Enfin, l'utilisation de la modélisation par éléments finis, incluant les contraintes et déformations mécaniques est proposée pour évaluer les changements dans les propriétés mécaniques au niveau du myocarde, avec l’hypothèse que le traitement à base de doxorubicine induit des changements importants à la fois dans le tissu et au niveau de la fonction myocardique. Dans notre cohorte de survivants de cancer, des changements myocardiques locaux ont été trouvés entre le groupe à risque standard et le groupe à risque élevé lorsque le T1 pré-gadolinium fut utilisé. Ces changements ont été amplifiés avec l’utilisation d’agent de contraste tel que confirmé par le coefficient de partition, ce qui suggère que l’utilisation du T1 post-gadolonium et le coefficient de----------ABSTRACT Doxorubicin chemotherapy is effective and widely used to treat acute lymphoblastic leukemia. However, its effectiveness is hampered by a wide spectrum of dose-dependent cardiotoxicity including both morphological and functional changes affecting the myocardium. Currently, very few techniques are available for detecting such cardiotoxic effect. The use of muscle fibers orientation (e.g., diffusion tensor imaging DT-MRI) or 3D imaging techniques (e.g., cine DENSE MRI) are possible alternatives, however, their clinical application is limited due to the acquisition time and their estimation errors. In contrast, the use of multi-parametric MRI along with cine MRI is a promising alternative, since theses techniques are already available at a clinical level. Multiparametric MRI including T1 and T2 imaging may be helpful in detecting myocardial tissue damage, while cine MRI may be more appropriate to detect functional changes within the myocardium. The combination of these two techniques may further allow an extensive characterization of myocardial tissue function. In this doctoral project, the use of pre- and post-gadolinium T1 and T2 relaxation times is firstly assessed and proposed to detect myocardial damage induced by doxorubicin chemotherapy. Secondly, the use of 2D myocardial displacement patterns is assessed in detecting myocardial damage and functional alteration due to doxorubicin-based treatment. Finally, the use of finite element modeling including mechanical strains and stresses to evaluate mechanical properties changes within the myocardium is alternatively proposed, assuming that doxorubicin-based treatment induces significant changes to both myocardial tissue morphology and function. In our cohort of cancer survivors, local myocardial changes were found between standard risk and high risks group using pre-gadolinium T1 relaxation times. These changes were further amplified with gadolinium enhancement, as confirmed by the use of partition coefficient, suggesting this MRI parameter along with partition coefficient as candidates imaging markers of doxorubicin induced cardiomyopathy. The use of T2 on the other hand showed that the high risk group of cancer survivors had higher T2 relaxation times compared to the standard risk group and similar to reported values. Though, a larger cohort of cancer survivors may be required to assess the use of T1 and T2 relaxation time as possible indices for myocardial tissue damage in the onset of doxorubicin-induced cardiotoxicity

Established and Emerging Cardiovascular Magnetic Resonance Imaging Techniques in the Evaluation of Subclinical Cardiovascular Disease

Introduction: Cardiovascular disease (CVD) remains the number one cause of mortality in the world for both men and women, thus improving its diagnosis and treatment is a priority. Rheumatoid Arthritis (RA) is a common auto-immune disease associated with high rates of CVD. Cardiovascular magnetic resonance (CMR) offers a multi-parametric, quantitative approach to the assessment of the heart and cardiovascular system with a host of techniques allowing assessment of anatomy, ventricular function, myocardial composition, myocardial perfusion, vascular performance and myocardial metabolism during a single scan. Its quantitative nature and lack of ionising radiation lend themselves ideally to the longitudinal study of subclinical CVD. Aims: To assess 1) whether blood longitudinal relaxation (T1) can be used to estimate blood haematocrit value to allow calculation of extracellular myocardial volume fraction (ECV), 2) whether CMR feature tracking (CMR-FT) is a feasible means of assessing aortic stiffness, 3) whether global longitudinal strain (GLS) is reduced in patients with prior MI with preserved left ventricular ejection fraction versus healthy controls, 4) whether aortic stiffness is present at all time points in the disease course of RA and 5) whether subclinical CV abnormalities in newly diagnosed RA improve with treatment and if the anti-tumour necrosis α Inhibitor Etanercept offers additional benefit over standard treatment. Methods: Patients were recruited between and February 2011 and February 2017. All patients underwent a comprehensive, multi-parametric CMR study including cine and late Gadolinium enhancement imaging at either 1.5 or 3.0T. Results: 1) estimation of blood haematocrit from blood T1 value provides accurate estimation of ‘synthetic’ ECV, 2) CMR-FT assessment of aortic stiffness is feasible and provides reproducible values for descending and ascending aortic strain values, 3) GLS is reduced in prior MI patients versus healthy controls (-17.3 ± 3.7% versus -19.3 ± 1.9% respectively, p=0.012). A GLS cut-off value of 18% correctly identifies prior MI with a sensitivity of 60% and specificity of 72.5%, 4) Aortic stiffness is evident in at risk RA individuals, newly diagnosed RA as well as established RA and 5) Aortic distensibility and left ventricular mass improve significantly in newly diagnosed RA patients following treatment. Etanercept appears to offer additional benefit over standard treatment evidenced by numerical improvements in aortic distensibility

Cardiovascular Magnetic Resonance Imaging for the Investigation of Ischaemic Heart Disease

Introduction: Coronary artery disease (CAD) remains the number one cause of mortality worldwide; improving diagnosis and treatment is a priority. Multi- parametric cardiovascular magnetic resonance (CMR) offers quantitative assessment of the cardiovascular system with a variety of techniques allowing assessment of anatomy, function, myocardial composition and perfusion during a single scan. Aims: To assess 1.) diagnostic accuracy of visual and quantitative perfusion CMR to single-photon emission computed tomography (MPS-SPECT) in patients with left main stem CAD. 2.) the hypothesis that patients with ischaemic (ICM) and non-ischaemic cardiomyopathy (NICM) have different torsion and strain parameters 3.) development and validation of a contemporary multivariable risk model of CAD from a large population undergoing X-ray angiography. 4.) a rapid 3D mDIXON pulse sequence for image quality and quantitation of MI. 5.) T1 rho prepared (T1ρ) dark blood sequence and compare to blood nulled PSIR (BN) and standard myocardium nulled PSIR (MN) for detection and quantification of scar. Methods: Patients were recruited between 2008 and 2017. Patients in chapters 3,4,6,7 underwent multi-parametric CMR including late gadolinium enhancement (LGE) imaging at 1.5 or 3.0T. Patients in chapter 5 underwent angiography. Results: 1.) CMR demonstrated significantly higher area under the curve for detection of LMS or equivalent disease over MPS-SPECT(P=0.0001). 2.) Despite no difference in LV dimensions, EF and strain between ICM and NICM, NICM patients had significantly lower LV twist(P=0.023) and torsion(P=0.017) compared to ICM. 3.) The developed model discriminated well and was well-calibrated. Diamond and Forrester and Duke scores substantially over-predicted CAD risk, whilst CAD Consortium risk models slightly under-estimated risk. 4.) Image quality was comparable between 3D and 2D LGE(P=0.162). Time for 3D image acquisition was only 5% of the time required for a standard 2D acquisition. 5.) CNRscar-blood was significantly increased for BN and T1ρ compared to MN LGE. BN LGE demonstrated significantly higher reader confidence scores

Apprentissage automatique pour simplifier l’utilisation de banques d’images cardiaques

The recent growth of data in cardiac databases has been phenomenal. Cleveruse of these databases could help find supporting evidence for better diagnosis and treatment planning. In addition to the challenges inherent to the large quantity of data, the databases are difficult to use in their current state. Data coming from multiple sources are often unstructured, the image content is variable and the metadata are not standardised. The objective of this thesis is therefore to simplify the use of large databases for cardiology specialists withautomated image processing, analysis and interpretation tools. The proposed tools are largely based on supervised machine learning techniques, i.e. algorithms which can learn from large quantities of cardiac images with groundtruth annotations and which automatically find the best representations. First, the inconsistent metadata are cleaned, interpretation and visualisation of images is improved by automatically recognising commonly used cardiac magnetic resonance imaging views from image content. The method is based on decision forests and convolutional neural networks trained on a large image dataset. Second, the thesis explores ways to use machine learning for extraction of relevant clinical measures (e.g. volumes and masses) from3D and 3D+t cardiac images. New spatio-temporal image features are designed andclassification forests are trained to learn how to automatically segment the main cardiac structures (left ventricle and left atrium) from voxel-wise label maps. Third, a web interface is designed to collect pairwise image comparisons and to learn how to describe the hearts with semantic attributes (e.g. dilation, kineticity). In the last part of the thesis, a forest-based machinelearning technique is used to map cardiac images to establish distances and neighborhoods between images. One application is retrieval of the most similar images.L'explosion récente de données d'imagerie cardiaque a été phénoménale. L'utilisation intelligente des grandes bases de données annotées pourrait constituer une aide précieuse au diagnostic et à la planification de thérapie. En plus des défis inhérents à la grande taille de ces banques de données, elles sont difficilement utilisables en l'état. Les données ne sont pas structurées, le contenu des images est variable et mal indexé, et les métadonnées ne sont pas standardisées. L'objectif de cette thèse est donc le traitement, l'analyse et l'interprétation automatique de ces bases de données afin de faciliter leur utilisation par les spécialistes de cardiologie. Dans ce but, la thèse explore les outils d'apprentissage automatique supervisé, ce qui aide à exploiter ces grandes quantités d'images cardiaques et trouver de meilleures représentations. Tout d'abord, la visualisation et l'interprétation d'images est améliorée en développant une méthode de reconnaissance automatique des plans d'acquisition couramment utilisés en imagerie cardiaque. La méthode se base sur l'apprentissage par forêts aléatoires et par réseaux de neurones à convolution, en utilisant des larges banques d'images, où des types de vues cardiaques sont préalablement établies. La thèse s'attache dans un deuxième temps au traitement automatique des images cardiaques, avec en perspective l'extraction d'indices cliniques pertinents. La segmentation des structures cardiaques est une étape clé de ce processus. A cet effet une méthode basée sur les forêts aléatoires qui exploite des attributs spatio-temporels originaux pour la segmentation automatique dans des images 3Det 3D+t est proposée. En troisième partie, l'apprentissage supervisé de sémantique cardiaque est enrichi grâce à une méthode de collecte en ligne d'annotations d'usagers. Enfin, la dernière partie utilise l'apprentissage automatique basé sur les forêts aléatoires pour cartographier des banques d'images cardiaques, tout en établissant les notions de distance et de voisinage d'images. Une application est proposée afin de retrouver dans une banque de données, les images les plus similaires à celle d'un nouveau patient

From Fully-Supervised Single-Task to Semi-Supervised Multi-Task Deep Learning Architectures for Segmentation in Medical Imaging Applications

Medical imaging is routinely performed in clinics worldwide for the diagnosis and treatment of numerous medical conditions in children and adults. With the advent of these medical imaging modalities, radiologists can visualize both the structure of the body as well as the tissues within the body. However, analyzing these high-dimensional (2D/3D/4D) images demands a significant amount of time and effort from radiologists. Hence, there is an ever-growing need for medical image computing tools to extract relevant information from the image data to help radiologists perform efficiently. Image analysis based on machine learning has pivotal potential to improve the entire medical imaging pipeline, providing support for clinical decision-making and computer-aided diagnosis. To be effective in addressing challenging image analysis tasks such as classification, detection, registration, and segmentation, specifically for medical imaging applications, deep learning approaches have shown significant improvement in performance. While deep learning has shown its potential in a variety of medical image analysis problems including segmentation, motion estimation, etc., generalizability is still an unsolved problem and many of these successes are achieved at the cost of a large pool of datasets. For most practical applications, getting access to a copious dataset can be very difficult, often impossible. Annotation is tedious and time-consuming. This cost is further amplified when annotation must be done by a clinical expert in medical imaging applications. Additionally, the applications of deep learning in the real-world clinical setting are still limited due to the lack of reliability caused by the limited prediction capabilities of some deep learning models. Moreover, while using a CNN in an automated image analysis pipeline, it’s critical to understand which segmentation results are problematic and require further manual examination. To this extent, the estimation of uncertainty calibration in a semi-supervised setting for medical image segmentation is still rarely reported. This thesis focuses on developing and evaluating optimized machine learning models for a variety of medical imaging applications, ranging from fully-supervised, single-task learning to semi-supervised, multi-task learning that makes efficient use of annotated training data. The contributions of this dissertation are as follows: (1) developing a fully-supervised, single-task transfer learning for the surgical instrument segmentation from laparoscopic images; and (2) utilizing supervised, single-task, transfer learning for segmenting and digitally removing the surgical instruments from endoscopic/laparoscopic videos to allow the visualization of the anatomy being obscured by the tool. The tool removal algorithms use a tool segmentation mask and either instrument-free reference frames or previous instrument-containing frames to fill in (inpaint) the instrument segmentation mask; (3) developing fully-supervised, single-task learning via efficient weight pruning and learned group convolution for accurate left ventricle (LV), right ventricle (RV) blood pool and myocardium localization and segmentation from 4D cine cardiac MR images; (4) demonstrating the use of our fully-supervised memory-efficient model to generate dynamic patient-specific right ventricle (RV) models from cine cardiac MRI dataset via an unsupervised learning-based deformable registration field; and (5) integrating a Monte Carlo dropout into our fully-supervised memory-efficient model with inherent uncertainty estimation, with the overall goal to estimate the uncertainty associated with the obtained segmentation and error, as a means to flag regions that feature less than optimal segmentation results; (6) developing semi-supervised, single-task learning via self-training (through meta pseudo-labeling) in concert with a Teacher network that instructs the Student network by generating pseudo-labels given unlabeled input data; (7) proposing largely-unsupervised, multi-task learning to demonstrate the power of a simple combination of a disentanglement block, variational autoencoder (VAE), generative adversarial network (GAN), and a conditioning layer-based reconstructor for performing two of the foremost critical tasks in medical imaging — segmentation of cardiac structures and reconstruction of the cine cardiac MR images; (8) demonstrating the use of 3D semi-supervised, multi-task learning for jointly learning multiple tasks in a single backbone module – uncertainty estimation, geometric shape generation, and cardiac anatomical structure segmentation of the left atrial cavity from 3D Gadolinium-enhanced magnetic resonance (GE-MR) images. This dissertation summarizes the impact of the contributions of our work in terms of demonstrating the adaptation and use of deep learning architectures featuring different levels of supervision to build a variety of image segmentation tools and techniques that can be used across a wide spectrum of medical image computing applications centered on facilitating and promoting the wide-spread computer-integrated diagnosis and therapy data science

Automated assessment of echocardiographic image quality using deep convolutional neural networks

Myocardial ischemia tops the list of causes of death around the globe, but its diagnosis and early detection thrives on clinical echocardiography. Although echocardiography presents a huge advantage of a non-intrusive, low-cost point of care diagnosis, its image quality is inherently subjective with strong dependence on operators’ experience level and acquisition skill. In some countries, echo specialists are mandated to supplementary years of training to achieve ‘gold standard’ free-hand acquisition skill without which exacerbates the reliability of echocardiogram and increases possibility for misdiagnosis. These drawbacks pose significant challenges to adopting echocardiography as authoritative modalities for cardiac diagnosis. However, the prevailing and currently adopted solution is to manually carry out quality evaluation where an echocardiography specialist visually inspects several acquired images to make clinical decisions of its perceived quality and prognosis. This is a lengthening process and laced with variability of opinion consequently affection diagnostic responses. The goal of the research is to provide a multi-discipline, state-of-the-art solution that allows objective quality assessment of echocardiogram and to guarantee the reliability of clinical quantification processes. Computer graphic processing unit simulations, medical imaging analysis and deep convolutional neural network models were employed to achieve this goal. From a finite pool of echocardiographic patient datasets, 1650 random samples of echocardiogram cine-loops from different patients with age ranges from 17 and 85 years, who had undergone echocardiography between 2010 and 2020 were evaluated. We defined a set of pathological and anatomical criteria of image quality by which apical-four and parasternal long axis frames can be evaluated with feasibility for real-time optimization. The selected samples were annotated for multivariate model developments and validation of predicted quality score per frame. The outcome presents a robust artificial intelligence algorithm that indicate frames’ quality rating, real-time visualisation of element of quality and updates quality optimization in real-time. A prediction errors of 0.052, 0.062, 0.069, 0.056 for visibility, clarity, depth-gain, and foreshortening attributes were achieved, respectively. The model achieved a combined error rate of 3.6% with average prediction speed of 4.24 ms per frame. The novel method established a superior approach to two-dimensional image quality estimation, assessment, and clinical adequacy on acquisition of echocardiogram prior to quantification and diagnosis of myocardial infarction

Characterization and modelling of complex motion patterns

Movement analysis is the principle of any interaction with the world and the survival of living beings completely depends on the effciency of such analysis. Visual systems have remarkably developed eficient mechanisms that analyze motion at different levels, allowing to recognize objects in dynamical and cluttered environments. In artificial vision, there exist a wide spectrum of applications for which the study of complex movements is crucial to recover salient information. Yet each domain may be different in terms of scenarios, complexity and relationships, a common denominator is that all of them require a dynamic understanding that captures the relevant information. Overall, current strategies are highly dependent on the appearance characterization and usually they are restricted to controlled scenarios. This thesis proposes a computational framework that is inspired in known motion perception mechanisms and structured as a set of modules. Each module is in due turn composed of a set of computational strategies that provide qualitative and quantitative descriptions of the dynamic associated to a particular movement. Diverse applications were herein considered and an extensive validation was performed for each of them. Each of the proposed strategies has shown to be reliable at capturing the dynamic patterns of different tasks, identifying, recognizing, tracking and even segmenting objects in sequences of video.Resumen. El análisis del movimiento es el principio de cualquier interacción con el mundo y la supervivencia de los seres vivos depende completamente de la eficiencia de este tipo de análisis. Los sistemas visuales notablemente han desarrollado mecanismos eficientes que analizan el movimiento en diferentes niveles, lo cual permite reconocer objetos en entornos dinámicos y saturados. En visión artificial existe un amplio espectro de aplicaciones para las cuales el estudio de los movimientos complejos es crucial para recuperar información saliente. A pesar de que cada dominio puede ser diferente en términos de los escenarios, la complejidad y las relaciones de los objetos en movimiento, un común denominador es que todos ellos requieren una comprensión dinámica para capturar información relevante. En general, las estrategias actuales son altamente dependientes de la caracterización de la apariencia y por lo general están restringidos a escenarios controlados. Esta tesis propone un marco computacional que se inspira en los mecanismos de percepción de movimiento conocidas y esta estructurado como un conjunto de módulos. Cada módulo esta a su vez compuesto por un conjunto de estrategias computacionales que proporcionan descripciones cualitativas y cuantitativas de la dinámica asociada a un movimiento particular. Diversas aplicaciones fueron consideradas en este trabajo y una extensa validación se llevó a cabo para cada uno de ellas. Cada una de las estrategias propuestas ha demostrado ser fiable en la captura de los patrones dinámicos de diferentes tareas identificando, reconociendo, siguiendo e incluso segmentando objetos en secuencias de video.Doctorad