Fast fully automatic myocardial segmentation in 4D cine cardiac magnetic resonance datasets

Dissertação de mestrado integrado em Engenharia BiomédicaCardiovascular diseases (CVDs) are the leading cause of death in the world, representing 30% of all global deaths. Among others, assessment of the left ventricular (LV) morphology and global function using non-invasive cardiac imaging is an interesting technique for diagnosis and treatment follow-up of patients with CVDs. Nowadays, cardiac magnetic resonance (CMR) imaging is the gold-standard technique for the quantification of LV volumes, mass and ejection fraction, requiring the delineation of endocardial and epicardial contours of the left ventricle from cine MR images. In clinical practice, the physicians perform this segmentation manually, being a tedious, time consuming and unpractical task. Even though several (semi-)automated methods have been presented for LV CMR segmentation, fast, automatic and optimal boundaries assessment is still lacking, usually requiring the physician to manually correct the contours. In the present work, we propose a novel fast fully automatic 3D+time LV segmentation framework for CMR datasets. The proposed framework presents three conceptual blocks: 1) an automatic 2D mid-ventricular initialization and segmentation; 2) an automatic stack initialization followed by a 3D segmentation at the end-diastolic phase; and 3) a tracking procedure to delineate both endo and epicardial contours throughout the cardiac cycle. In each block, specific CMR-targeted algorithms are proposed for the different steps required. Hereto, we propose automatic and feasible initialization procedures. Moreover, we adapt the recent B-spline Explicit Active Surfaces (BEAS) framework to the properties of CMR image segmentation by integrating dedicated energy terms and making use of a cylindrical coordinate system that better fits the topology of CMR data. At last, two tracking methods are presented and compared. The proposed framework has been validated on 45 4D CMR datasets from a publicly available database and on a large database from an ongoing multi-center clinical trial with 318 4D datasets. In the technical validation, the framework showed competitive results against the state-of-the-art methods, presenting leading results in both accuracy and average computational time in the common database used for comparative purposes. Moreover, the results in the large scale clinical validation confirmed the high feasibility and robustness of the proposed framework for accurate LV morphology and global function assessment. In combination with the low computational burden of the method, the present methodology seems promising to be used in daily clinical practice.As doenças cardiovasculares (DCVs) são a principal causa de morte no mundo, representando 30% destas a nível global. Na prática clínica, uma técnica empregue no diagnóstico de pacientes com DCVs é a avaliação da morfologia e da função global do ventrículo esquerdo (VE), através de técnicas de imagiologia não-invasivas. Atualmente, a ressonância magnética cardíaca (RMC) é a modalidade de referência na quantificação dos volumes, massa e fração de ejeção do VE, exigindo a delimitação dos contornos do endocárdio e epicárdio a partir de imagens dinâmicas de RMC. Na prática clínica diária, o método preferencial é a segmentação manual. No entanto, esta é uma tarefa demorada, sujeita a erro humano e pouco prática. Apesar de até à data diversos métodos (semi)-automáticos terem sido apresentados para a segmentação do VE em imagens de RMC, ainda não existe um método capaz de avaliar idealmente os contornos de uma forma automática, rápida e precisa, levando a que geralmente o médico necessite de corrigir manualmente os contornos. No presente trabalho é proposta uma nova framework para a segmentação automática do VE em imagens 3D+tempo de RMC. O algoritmo apresenta três blocos principais: 1) uma inicialização e segmentação automática 2D num corte medial do ventrículo; 2) uma inicialização e segmentação tridimensional no volume correspondente ao final da diástole; e 3) um algoritmo de tracking para obter os contornos ao longo de todo o ciclo cardíaco. Neste sentido, são propostos procedimentos de inicialização automática com elevada robustez. Mais ainda, é proposta uma adaptação da recente framework “B-spline Explicit Active Surfaces” (BEAS) com a integração de uma energia específica para as imagens de RMC e utilizando uma formulação cilíndrica para tirar partido da topologia destas imagens. Por último, são apresentados e comparados dois algoritmos de tracking para a obtenção dos contornos ao longo do tempo. A framework proposta foi validada em 45 datasets de RMC provenientes de uma base de dados disponível ao público, bem como numa extensa base de dados com 318 datasets para uma validação clínica. Na avaliação técnica, a framework proposta obteve resultados competitivos quando comparada com outros métodos do estado da arte, tendo alcançado resultados de precisão e tempo computacional superiores a estes. Na validação clínica em larga escala, a framework provou apresentar elevada viabilidade e robustez na avaliação da morfologia e função global do VE. Em combinação com o baixo custo computacional do algoritmo, a presente metodologia apresenta uma perspetiva promissora para a sua aplicação na prática clínica diária

Rapid Segmentation Techniques for Cardiac and Neuroimage Analysis

Recent technological advances in medical imaging have allowed for the quick acquisition of highly resolved data to aid in diagnosis and characterization of diseases or to guide interventions. In order to to be integrated into a clinical work flow, accurate and robust methods of analysis must be developed which manage this increase in data. Recent improvements in in- expensive commercially available graphics hardware and General-Purpose Programming on Graphics Processing Units (GPGPU) have allowed for many large scale data analysis problems to be addressed in meaningful time and will continue to as parallel computing technology improves. In this thesis we propose methods to tackle two clinically relevant image segmentation problems: a user-guided segmentation of myocardial scar from Late-Enhancement Magnetic Resonance Images (LE-MRI) and a multi-atlas segmentation pipeline to automatically segment and partition brain tissue from multi-channel MRI. Both methods are based on recent advances in computer vision, in particular max-flow optimization that aims at solving the segmentation problem in continuous space. This allows for (approximately) globally optimal solvers to be employed in multi-region segmentation problems, without the particular drawbacks of their discrete counterparts, graph cuts, which typically present with metrication artefacts. Max-flow solvers are generally able to produce robust results, but are known for being computationally expensive, especially with large datasets, such as volume images. Additionally, we propose two new deformable registration methods based on Gauss-Newton optimization and smooth the resulting deformation fields via total-variation regularization to guarantee the problem is mathematically well-posed. We compare the performance of these two methods against four highly ranked and well-known deformable registration methods on four publicly available databases and are able to demonstrate a highly accurate performance with low run times. The best performing variant is subsequently used in a multi-atlas segmentation pipeline for the segmentation of brain tissue and facilitates fast run times for this computationally expensive approach. All proposed methods are implemented using GPGPU for a substantial increase in computational performance and so facilitate deployment into clinical work flows. We evaluate all proposed algorithms in terms of run times, accuracy, repeatability and errors arising from user interactions and we demonstrate that these methods are able to outperform established methods. The presented approaches demonstrate high performance in comparison with established methods in terms of accuracy and repeatability while largely reducing run times due to the employment of GPU hardware

Multidimensional image analysis of cardiac function in MRI

Cardiac morphology is a key indicator of cardiac health. Important metrics that are currently in clinical use are left-ventricle cardiac ejection fraction, cardiac muscle (myocardium) mass, myocardium thickness and myocardium thickening over the cardiac cycle. Advances in imaging technologies have led to an increase in temporal and spatial resolution. Such an increase in data presents a laborious task for medical practitioners to analyse. In this thesis, measurement of the cardiac left-ventricle function is achieved by developing novel methods for the automatic segmentation of the left-ventricle blood-pool and the left ventricle myocardium boundaries. A preliminary challenge faced in this task is the removal of noise from Magnetic Resonance Imaging (MRI) data, which is addressed by using advanced data filtering procedures. Two mechanisms for left-ventricle segmentation are employed. Firstly segmentation of the left ventricle blood-pool for the measurement of ejection fraction is undertaken in the signal intensity domain. Utilising the high discrimination between blood and tissue, a novel methodology based on a statistical partitioning method offers success in localising and segmenting the blood pool of the left ventricle. From this initialisation, the estimation of the outer wall (epi-cardium) of the left ventricle can be achieved using gradient information and prior knowledge. Secondly, a more involved method for extracting the myocardium of the leftventricle is developed, that can better perform segmentation in higher dimensions. Spatial information is incorporated in the segmentation by employing a gradient-based boundary evolution. A level-set scheme is implemented and a novel formulation for the extraction of the cardiac muscle is introduced. Two surfaces, representing the inner and the outer boundaries of the left-ventricle, are simultaneously evolved using a coupling function and supervised with a probabilistic model of expertly assisted manual segmentations

Análise funcional do ventrículo esquerdo em angio-TC coronária

Doutoramento em Engenharia InformáticaCoronary CT angiography is widely used in clinical practice for the assessment of coronary artery disease. Several studies have shown that the same exam can also be used to assess left ventricle (LV) function. LV function is usually evaluated using just the data from end-systolic and end-diastolic phases even though coronary CT angiography (CTA) provides data concerning multiple cardiac phases, along the cardiac cycle. This unused wealth of data, mostly due to its complexity and the lack of proper tools, has still to be explored in order to assess if further insight is possible regarding regional LV functional analysis. Furthermore, different parameters can be computed to characterize LV function and while some are well known by clinicians others still need to be evaluated concerning their value in clinical scenarios. The work presented in this thesis covers two steps towards extended use of CTA data: LV segmentation and functional analysis. A new semi-automatic segmentation method is presented to obtain LV data for all cardiac phases available in a CTA exam and a 3D editing tool was designed to allow users to fine tune the segmentations. Regarding segmentation evaluation, a methodology is proposed in order to help choose the similarity metrics to be used to compare segmentations. This methodology allows the detection of redundant measures that can be discarded. The evaluation was performed with the help of three experienced radiographers yielding low intraand inter-observer variability. In order to allow exploring the segmented data, several parameters characterizing global and regional LV function are computed for the available cardiac phases. The data thus obtained is shown using a set of visualizations allowing synchronized visual exploration. The main purpose is to provide means for clinicians to explore the data and gather insight over their meaning, as well as their correlation with each other and with diagnosis outcomes. Finally, an interactive method is proposed to help clinicians assess myocardial perfusion by providing automatic assignment of lesions, detected by clinicians, to a myocardial segment. This new approach has obtained positive feedback from clinicians and is not only an improvement over their current assessment method but also an important first step towards systematic validation of automatic myocardial perfusion assessment measures.A angiografia coronária por TC (angio-TC) é prática clínica corrente para a avaliação de doença coronária. Alguns estudos mostram que é também possível utilizar o exame de angio-TC para avaliar a função do ventrículo esquerdo (VE). A função ventricular esquerda (FVE) é normalmente avaliada considerando as fases de fim de sístole e de fim de diástole, apesar de a angio-TC proporcionar dados relativos a diferentes fases distribuídas ao longo do ciclo cardíaco. Estes dados não considerados, devido à sua complexidade e à falta de ferramentas apropriadas para o efeito, têm ainda de ser explorados para que se perceba se possibilitam uma melhor compreensão da FVE. Para além disso, podem ser calculados diferentes parâmetros para caracterizar a FVE e, enquanto alguns são bem conhecidos dos médicos, outros requerem ainda uma avaliação do seu valor clínico. No âmbito de uma utilização alargada dos dados proporcionados pelos angio- TC, este trabalho apresenta contributos ao nível da segmentação do VE e da sua análise funcional. É proposto um método semi-automático para a segmentação do VE de forma a obter dados para as diferentes fases cardíacas presentes no exame de angio- TC. Foi também desenvolvida uma ferramenta de edição 3D que permite aos utilizadores a correcção das segmentações assim obtidas. Para a avaliação do método de segmentação apresentado foi proposta uma metodologia que permite a detecção de medidas de similaridade redundantes, a usar no âmbito da avaliação para comparação entre segmentações, para que tais medidas redundantes possam ser descartadas. A avaliação foi executada com a colaboração de três técnicos de radiologia experientes, tendo-se verificado uma baixa variabilidade intra- e inter-observador. De forma a permitir explorar os dados segmentados, foram calculados vários parâmetros para caracterização global e regional da FVE, para as diversas fases cardíacas disponíveis. Os resultados assim obtidos são apresentados usando um conjunto de visualizações que permitem uma exploração visual sincronizada dos mesmos. O principal objectivo é proporcionar ao médico a exploração dos resultados obtidos para os diferentes parâmetros, de modo a que este tenha uma compreensão acrescida sobre o seu significado clínico, assim como sobre a correlação existente entre diferentes parâmetros e entre estes e o diagnóstico. Finalmente, foi proposto um método interactivo para ajudar os médicos durante a avaliação da perfusão do miocárdio, que atribui automaticamente as lesões detectadas pelo médico ao respectivo segmento do miocárdio. Este novo método obteve uma boa receptividade e constitui não só uma melhoria em relação ao método tradicional mas é também um primeiro passo para a validação sistemática de medidas automáticas da perfusão do miocárdio

Coupled Shape Models for the Diagnosis of Organ Motion Restriction

Annähernd 30% der weltweiten Todesfälle sind auf Erkrankungen des Herzens und der Lunge zurückzuführen, wobei die meisten dieser Erkrankungen während ihres Verlaufs die Mobilität des betroffenen Organs verändern. Viele dieser To-desfälle könnten durch eine frühzeitige Erkennung und Behandlung der Erkran-kung vermieden werden. Deshalb wurden im Zuge dieser Arbeit Methoden ent-wickelt, um aus Segmentierungen von dynamischen Magnetresonanztomogra-phie-Daten quantitative Kennzahlen für die funktionale Analyse der Herz- und Lungenbewegung zu generieren. Ein automatisiertes Segmentierungsverfahren basierend auf gekoppelten Formmodellen wurde entwickelt, welches wechsel-seitige Informationen der Form und Geometrie mehrerer korrelierter Objekte mit einbezieht, und somit 40% bessere Ergebnisse im Vergleich zur Verwendung einzelner Modelle erzielte. Im Fall des Herzens wurde ein Volumenberechnungs-fehler von unter 13% erreicht, was in der Größenordnung der Interobserver-Variabilität liegt. Für die Lunge konnte ein Volumenfehler von unter 70ml gezeigt werden. Aus den Segmentierungsergebnissen wurden funktionale Parameter der lokalen Organdynamik abgeleitet und visualisiert, die gegen konventionelle Diag-nosemethoden evaluiert wurden und dabei gute Übereinstimmung zeigen, dar-über hinaus jedoch eine lokal und regionale Mobilitätscharakterisierung erlau-ben

Analysis of contrast-enhanced medical images.

Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection, (ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer. However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfusion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building new mathematical models and learning techniques that facilitate accurate analysis of CAs perfusion in living organs and include: (i) accurate mathematical models for the segmentation of the object-of-interest, which integrate object shape and appearance features in terms of pixel/voxel-wise image intensities and their spatial interactions; (ii) motion correction techniques that combine both global and local models, which exploit geometric features, rather than image intensities to avoid problems associated with nonlinear intensity variations of the CE images; (iii) fusion of multiple features using the genetic algorithm. The proposed techniques have been integrated into CAD systems that have been tested in, but not limited to, three clinical studies. First, a noninvasive CAD system is proposed for the early and accurate diagnosis of acute renal transplant rejection using dynamic contrast-enhanced MRI (DCE-MRI). Acute rejection–the immunological response of the human immune system to a foreign kidney–is the most sever cause of renal dysfunction among other diagnostic possibilities, including acute tubular necrosis and immune drug toxicity. In the U.S., approximately 17,736 renal transplants are performed annually, and given the limited number of donors, transplanted kidney salvage is an important medical concern. Thus far, biopsy remains the gold standard for the assessment of renal transplant dysfunction, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The diagnostic results of the proposed CAD system, based on the analysis of 50 independent in-vivo cases were 96% with a 95% confidence interval. These results clearly demonstrate the promise of the proposed image-based diagnostic CAD system as a supplement to the current technologies, such as nuclear imaging and ultrasonography, to determine the type of kidney dysfunction. Second, a comprehensive CAD system is developed for the characterization of myocardial perfusion and clinical status in heart failure and novel myoregeneration therapy using cardiac first-pass MRI (FP-MRI). Heart failure is considered the most important cause of morbidity and mortality in cardiovascular disease, which affects approximately 6 million U.S. patients annually. Ischemic heart disease is considered the most common underlying cause of heart failure. Therefore, the detection of the heart failure in its earliest forms is essential to prevent its relentless progression to premature death. While current medical studies focus on detecting pathological tissue and assessing contractile function of the diseased heart, this dissertation address the key issue of the effects of the myoregeneration therapy on the associated blood nutrient supply. Quantitative and qualitative assessment in a cohort of 24 perfusion data sets demonstrated the ability of the proposed framework to reveal regional perfusion improvements with therapy, and transmural perfusion differences across the myocardial wall; thus, it can aid in follow-up on treatment for patients undergoing the myoregeneration therapy. Finally, an image-based CAD system for early detection of prostate cancer using DCE-MRI is introduced. Prostate cancer is the most frequently diagnosed malignancy among men and remains the second leading cause of cancer-related death in the USA with more than 238,000 new cases and a mortality rate of about 30,000 in 2013. Therefore, early diagnosis of prostate cancer can improve the effectiveness of treatment and increase the patient’s chance of survival. Currently, needle biopsy is the gold standard for the diagnosis of prostate cancer. However, it is an invasive procedure with high costs and potential morbidity rates. Additionally, it has a higher possibility of producing false positive diagnosis due to relatively small needle biopsy samples. Application of the proposed CAD yield promising results in a cohort of 30 patients that would, in the near future, represent a supplement of the current technologies to determine prostate cancer type. The developed techniques have been compared to the state-of-the-art methods and demonstrated higher accuracy as shown in this dissertation. The proposed models (higher-order spatial interaction models, shape models, motion correction models, and perfusion analysis models) can be used in many of today’s CAD applications for early detection of a variety of diseases and medical conditions, and are expected to notably amplify the accuracy of CAD decisions based on the automated analysis of CE images

Specular reflection removal and bloodless vessel segmentation for 3-D heart model reconstruction from single view images

Three Dimensional (3D) human heart model is attracting attention for its role in medical images for education and clinical purposes. Analysing 2D images to obtain meaningful information requires a certain level of expertise. Moreover, it is time consuming and requires special devices to obtain aforementioned images. In contrary, a 3D model conveys much more information. 3D human heart model reconstruction from medical imaging devices requires several input images, while reconstruction from a single view image is challenging due to the colour property of the heart image, light reflections, and its featureless surface. Lights and illumination condition of the operating room cause specular reflections on the wet heart surface that result in noises forming of the reconstruction process. Image-based technique is used for the proposed human heart surface reconstruction. It is important the reflection is eliminated to allow for proper 3D reconstruction and avoid imperfect final output. Specular reflections detection and correction process examine the surface properties. This was implemented as a first step to detect reflections using the standard deviation of RGB colour channel and the maximum value of blue channel to establish colour, devoid of specularities. The result shows the accurate and efficient performance of the specularities removing process with 88.7% similarity with the ground truth. Realistic 3D heart model reconstruction was developed based on extraction of pixel information from digital images to allow novice surgeons to reduce the time for cardiac surgery training and enhancing their perception of the Operating Theatre (OT). Cardiac medical imaging devices such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) images, or Echocardiography provide cardiac information. However,these images from medical modalities are not adequate, to precisely simulate the real environment and to be used in the training simulator for cardiac surgery. The propose method exploits and develops techniques based on analysing real coloured images taken during cardiac surgery in order to obtain meaningful information of the heart anatomical structures. Another issue is the different human heart surface vessels. The most important vessel region is the bloodless, lack of blood, vessels. Surgeon faces some difficulties in locating the bloodless vessel region during surgery. The thesis suggests a technique of identifying the vessels’ Region of Interest (ROI) to avoid surgical injuries by examining an enhanced input image. The proposed method locates vessels’ ROI by using Decorrelation Stretch technique. This Decorrelation Stretch can clearly enhance the heart’s surface image. Through this enhancement, the surgeon become enables effectively identifying the vessels ROI to perform the surgery from textured and coloured surface images. In addition, after enhancement and segmentation of the vessels ROI, a 3D reconstruction of this ROI takes place and then visualize it over the 3D heart model. Experiments for each phase in the research framework were qualitatively and quantitatively evaluated. Two hundred and thirteen real human heart images are the dataset collected during cardiac surgery using a digital camera. The experimental results of the proposed methods were compared with manual hand-labelling ground truth data. The cost reduction of false positive and false negative of specular detection and correction processes of the proposed method was less than 24% compared to other methods. In addition, the efficient results of Root Mean Square Error (RMSE) to measure the correctness of the z-axis values to reconstruction of the 3D model accurately compared to other method. Finally, the 94.42% accuracy rate of the proposed vessels segmentation method using RGB colour space achieved is comparable to other colour spaces. Experimental results show that there is significant efficiency and robustness compared to existing state of the art methods

Basic Science to Clinical Research: Segmentation of Ultrasound and Modelling in Clinical Informatics

The world of basic science is a world of minutia; it boils down to improving even a fraction of a percent over the baseline standard. It is a domain of peer reviewed fractions of seconds and the world of squeezing every last ounce of efficiency from a processor, a storage medium, or an algorithm. The field of health data is based on extracting knowledge from segments of data that may improve some clinical process or practice guideline to improve the time and quality of care. Clinical informatics and knowledge translation provide this information in order to reveal insights to the world of improving patient treatments, regimens, and overall outcomes. In my world of minutia, or basic science, the movement of blood served an integral role. The novel detection of sound reverberations map out the landscape for my research. I have applied my algorithms to the various anatomical structures of the heart and artery system. This serves as a basis for segmentation, active contouring, and shape priors. The algorithms presented, leverage novel applications in segmentation by using anatomical features of the heart for shape priors and the integration of optical flow models to improve tracking. The presented techniques show improvements over traditional methods in the estimation of left ventricular size and function, along with plaque estimation in the carotid artery. In my clinical world of data understanding, I have endeavoured to decipher trends in Alzheimer’s disease, Sepsis of hospital patients, and the burden of Melanoma using mathematical modelling methods. The use of decision trees, Markov models, and various clustering techniques provide insights into data sets that are otherwise hidden. Finally, I demonstrate how efficient data capture from providers can achieve rapid results and actionable information on patient medical records. This culminated in generating studies on the burden of illness and their associated costs. A selection of published works from my research in the world of basic sciences to clinical informatics has been included in this thesis to detail my transition. This is my journey from one contented realm to a turbulent one