Combining multiple dynamic models and deep learning architectures for tracking the left ventricle endocardium in ultrasound data

We present a new statistical pattern recognition approach for the problem of left ventricle endocardium tracking in ultrasound data. The problem is formulated as a sequential importance resampling algorithm such that the expected segmentation of the current time step is estimated based on the appearance, shape, and motion models that take into account all previous and current images and previous segmentation contours produced by the method. The new appearance and shape models decouple the affine and nonrigid segmentations of the left ventricle to reduce the running time complexity. The proposed motion model combines the systole and diastole motion patterns and an observation distribution built by a deep neural network. The functionality of our approach is evaluated using a dataset of diseased cases containing 16 sequences and another dataset of normal cases comprised of four sequences, where both sets present long axis views of the left ventricle. Using a training set comprised of diseased and healthy cases, we show that our approach produces more accurate results than current state-of-the-art endocardium tracking methods in two test sequences from healthy subjects. Using three test sequences containing different types of cardiopathies, we show that our method correlates well with interuser statistics produced by four cardiologists.Gustavo Carneiro and Jacinto C. Nasciment

Virtual Reality applied to biomedical engineering

Actualment, la realitat virtual esta sent tendència i s'està expandint a l'àmbit mèdic, fent possible l'aparició de nombroses aplicacions dissenyades per entrenar metges i tractar pacients de forma més eficient, així com optimitzar els processos de planificació quirúrgica. La necessitat mèdica i objectiu d'aquest projecte és fer òptim el procés de planificació quirúrgica per a cardiopaties congènites, que compren la reconstrucció en 3D del cor del pacient i la seva integració en una aplicació de realitat virtual. Seguint aquesta línia s’ha combinat un procés de modelat 3D d’imatges de cors obtinguts gracies al Hospital Sant Joan de Déu i el disseny de l’aplicació mitjançant el software Unity 3D gracies a l’empresa VISYON. S'han aconseguit millores en quant al software emprat per a la segmentació i reconstrucció, i s’han assolit funcionalitats bàsiques a l’aplicació com importar, moure, rotar i fer captures de pantalla en 3D de l'òrgan cardíac i així, entendre millor la cardiopatia que s’ha de tractar. El resultat ha estat la creació d'un procés òptim, en el que la reconstrucció en 3D ha aconseguit ser ràpida i precisa, el mètode d’importació a l’app dissenyada molt senzill, i una aplicació que permet una interacció atractiva i intuïtiva, gracies a una experiència immersiva i realista per ajustar-se als requeriments d'eficiència i precisió exigits en el camp mèdic

Analysis of strain in the human left ventricle using real-time 3D echocardiography and optical flow

Cardiovascular disease (CVD) consistently ranks among the leading causes of death in the United States. The most common subtype of CVD, ischemic heart disease, is a frequent precursor of myocardial infarction and heart failure, most commonly affecting the left ventricle (LV). Today, echocardiography is regarded as the gold standard in screening, diagnosis, and monitoring of LV dysfunction. But while global assessment of LV function tends to be quantitative, cardiologists with specific expertise still perform many regional evaluations subjectively. However, a more objective and quantitative measure of regional function – myocardial strain – has been developed and widely studied using 2D echocardiography. With recent developments in real-time 3D echocardiography (RT3DE), it has become possible to measure strain in its native 3D orientation as well. Our laboratory’s earlier work introduced the Optical Flow (OF) method of strain analysis, which was validated on simulated echocardiograms as well as through animal studies. The principal goal of this thesis is to translate this OF-based method of strain estimation from the research setting to the patient’s bedside. We have performed a series of studies to evaluate the feasibility, accuracy, and reproducibility of OF-based myocardial strain estimation in a routine clinical setting. The first investigation focused on the optimization of RT3DE acquisition and the OF processing pipeline for use in human subjects. Subsequently, we evaluated the capacity of this technique to distinguish abnormal strain patterns in patients with CVD and varying degrees of LV dysfunction. Our analysis revealed that segmental strain measures obtained by OF may have better sensitivity and specificity than the more commonly used global LV strains. Our third validation study examined the reproducibility of these strain measures in both healthy and diseased populations. We established that OF-based strain measures demonstrate repeatability comparable to that achieved by the latest commercial software commonly used in clinical research to estimate 2D or 3D strain. These studies were driven in large part by the absence of a ground truth or accepted gold standard of 3D strain measurements in the human LV. However, cardiac magnetic resonance imaging has had considerable success in measuring some forms of strain in the human LV. We therefore began to develop an image-processing pipeline to derive strain estimates from a new pulse sequence called 3D-DENSE. We further sought to improve the OF pipeline by automating the process of tracking the LV border. To this end, we developed a level-set based technique which tracks the LV endocardium. Our evaluation of its performance on RT3DE data confirmed that this method performs within the limits of inter-observer variability. Overall, our pilot studies of OF-based strain estimation demonstrate that the technique possesses several promising features for improving cardiologists’ ability to quantify and interpret the complex three-dimensional deformations of the human LV

Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review

The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more 20 comprehensive computational anatomical models has grown, leading to the creation of multi-organ models. Multi-organ approaches, unlike traditional organ-specific strategies, incorporate inter-organ relations into the model, thus leading to a more accurate representation of the complex human anatomy. Inter-organ relations are not only spatial, but also functional and physiological. Over the years, the strategies 25 proposed to efficiently model multi-organ structures have evolved from the simple global modeling, to more sophisticated approaches such as sequential, hierarchical, or machine learning-based models. In this paper, we present a review of the state of the art on multi-organ analysis and associated computation anatomy methodology. The manuscript follows a methodology-based classification of the different techniques 30 available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution models to the most recent deep learning-based approaches. With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of 35 medical imaging applications on the future of healthcare.Comment: Paper under revie

Exploring the use of routine healthcare data through process mining to inform the management of musculoskeletal diseases

Healthcare informatics can help address some of the challenges faced by both healthcare providers and patients. The medical domain is characterised by inherently complex and intricate issues, data can often be of poor quality and novel techniques are required. Process mining is a discipline that uses techniques to extract insights from event data, generated during the execution of processes. It has had good results in various branches of medical science but applications to musculoskeletal diseases remain largely unexplored. This research commenced with a review of the healthcare and technical literature and applied a variety of process mining techniques in order to investigate approaches to the healthcare plans of patients with musculoskeletal conditions. The analysis involved three datasets from: 1) a private hospital in Boston, US, where data was used to create disease trajectory models. Results suggest the method may be of interest to healthcare researchers, as it enables a more rapid modelling and visualisation; 2) a mobile healthcare application for patients receiving physiotherapy in Sheffield, UK, where data was used to identify possible indicators for health outcomes. After evaluation of the results, it was found that the indicators identified may be down to chance; and 3) the population of Wales to explore knee pain surgery pathways. Results suggest that process mining is an effective technique. This work demonstrates how routine healthcare data can be analysed using process mining techniques to provide insights that may benefit patients suffering with musculoskeletal conditions. This thesis explores how strict criteria for analysis can be performed. The work is intended to expand the breadth of process mining methods available to the data science community and has contributed by making recommendations for service utilisation within physiotherapy at Sheffield Hospital and helped to define a roadmap for a leading healthcare software company