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

Patch-based segmentation with spatial context for medical image analysis

Accurate segmentations in medical imaging form a crucial role in many applications from pa- tient diagnosis to population studies. As the amount of data generated from medical images increases, the ability to perform this task without human intervention becomes ever more de- sirable. One approach, known broadly as atlas-based segmentation, is to propagate labels from images which have already been manually labelled by clinical experts. Methods using this ap- proach have been shown to be e ective in many applications, demonstrating great potential for automatic labelling of large datasets. However, these methods usually require the use of image registration and are dependent on the outcome of the registration. Any registrations errors that occur are also propagated to the segmentation process and are likely to have an adverse e ect on segmentation accuracy. Recently, patch-based methods have been shown to allow a relaxation of the required image alignment, whilst achieving similar results. In general, these methods label each voxel of a target image by comparing the image patch centred on the voxel with neighbouring patches from an atlas library and assigning the most likely label according to the closest matches. The main contributions of this thesis focuses around this approach in providing accurate segmentation results whilst minimising the dependency on registration quality. In particular, this thesis proposes a novel kNN patch-based segmentation framework, which utilises both intensity and spatial information, and explore the use of spatial context in a diverse range of applications. The proposed methods extend the potential for patch-based segmentation to tolerate registration errors by rede ning the \locality" for patch selection and comparison, whilst also allowing similar looking patches from di erent anatomical structures to be di erentiated. The methods are evaluated on a wide variety of image datasets, ranging from the brain to the knees, demonstrating its potential with results which are competitive to state-of-the-art techniques.Open Acces

Anatomical Image Series Analysis in the Computational Anatomy Random Orbit Model

Serially acquired medical imagery plays an important role in the computational study of human anatomy. In this work, we describe the development of novel algorithms set in the large deformation diffeomorphic metric mapping framework for analyzing serially acquired imagery of two general types: spatial image series and temporal image series. In the former case, a critical step in the analysis of neural connectivity from serially-sectioned brain histology data is the reconstruction of spatially distorted image volumes and registration into a common coordinate space. In the latter case, computational methods are required for building low dimensional representations of the infinite dimensional shape space standard to computational anatomy. Here, we review the vast body of work related to volume reconstruction and atlas-mapping of serially-sectioned data as well as diffeomorphic methods for longitudinal data and we position our work relative to these in the context of the computational anatomy random orbit model. We show how these two problems are embedded as extensions to the classic random orbit model and use it to both enforce diffeomorphic conditions and analyze the distance metric associated to diffeomorphisms. We apply our new algorithms to histology and MRI datasets to study the structure, connectivity, and pathological degeneration of the brain

Automated Morphometric Characterization of the Cerebral Cortex for the Developing and Ageing Brain

Morphometric characterisation of the cerebral cortex can provide information about patterns of brain development and ageing and may be relevant for diagnosis and estimation of the progression of diseases such as Alzheimer's, Huntington's, and schizophrenia. Therefore, understanding and describing the differences between populations in terms of structural volume, shape and thickness is of critical importance. Methodologically, due to data quality, presence of noise, PV effects, limited resolution and pathological variability, the automated, robust and time-consistent estimation of morphometric features is still an unsolved problem. This thesis focuses on the development of tools for robust cross-sectional and longitudinal morphometric characterisation of the human cerebral cortex. It describes techniques for tissue segmentation, structural and morphometric characterisation, cross-sectional and longitudinally cortical thickness estimation from serial MRI images in both adults and neonates. Two new probabilistic brain tissue segmentation techniques are introduced in order to accurately and robustly segment the brain of elderly and neonatal subjects, even in the presence of marked pathology. Two other algorithms based on the concept of multi-atlas segmentation propagation and fusion are also introduced in order to parcelate the brain into its multiple composing structures with the highest possible segmentation accuracy. Finally, we explore the use of the Khalimsky cubic complex framework for the extraction of topologically correct thickness measurements from probabilistic segmentations without explicit parametrisation of the edge. A longitudinal extension of this method is also proposed. The work presented in this thesis has been extensively validated on elderly and neonatal data from several scanners, sequences and protocols. The proposed algorithms have also been successfully applied to breast and heart MRI, neck and colon CT and also to small animal imaging. All the algorithms presented in this thesis are available as part of the open-source package NiftySeg

Artificial Intelligence with Light Supervision: Application to Neuroimaging

Recent developments in artificial intelligence research have resulted in tremendous success in computer vision, natural language processing and medical imaging tasks, often reaching human or superhuman performance. In this thesis, I further developed artificial intelligence methods based on convolutional neural networks with a special focus on the automated analysis of brain magnetic resonance imaging scans (MRI). I showed that efficient artificial intelligence systems can be created using only minimal supervision, by reducing the quantity and quality of annotations used for training. I applied those methods to the automated assessment of the burden of enlarged perivascular spaces, brain structural changes that may be related to dementia, stroke, mult

Atlas-based segmentation of medical images

Atlas-Based Segmentation of medical images is an image analysis task which involves labelling a desired anatomy or set of anatomy from images generated by medical imaging modalities. The overall goal of atlas-based segmentation is to assist radiologists in the detection and diagnosis of diseases. By extracting the relevant anatomy from medical images and presenting it in an appropriate view, their work-flow can be optimised. This portfolio-style thesis discusses the research projects carried out in order to evaluate the applicability of atlas-based methods to a variety of medical imaging problems. The thesis describes how atlas-based methods have been applied to heart segmentation, to extract the heart for further cardiac analysis from cardiac CT images, to kidney segmentation, to prepare the kidney for automated perfusion measurements, and to coronary vessel tracking, in order to improve on the quality of tracking algorithms. This thesis demonstrates how state of the art atlas-based segmentation techniques can be applied successfully to a range of clinical problems in different imaging modalities. Each application has been tested using not only standard experimentation principles, but also by clinically-trained personnel to evaluate its efficacy. The success of these methods is such that some of the described applications have since been deployed in commercial products. While exploring these applications, several techniques based on published literature were explored and tailored to suit each individual application. This thesis describes in detail the methods used for each application in turn, recognising the state of the art, and outlines the author's contribution in every application

Quantifying structural changes in the ageing brain from magnetic resonance imaging

Understanding the ageing process is of increasing importance to an ageing society and one aspect of this is investigating what role the brain has in this process. Cognitive ability declines as we age and it is one of the most distressing aspects of getting older. Brain tissue deterioration is a significant contributor to lower cognitive ability in late life but the underlying biological mechanisms in the brain are not yet fully understood. One reason for this is the difficulty in obtaining accurate measures of potential ageing-related brain biomarkers. The chapters in this thesis explore the difficulties of quantifying brain changes in the ageing brain from Magnetic Resonance Imaging (MRI), and how the changes identified are related to cognition in later life. The data was acquired as part of the second wave of the longitudinal Lothian Birth Cohort 1936 study in which 866 people aged 73 years, returned for cognitive and medical assessment. At this stage of the study 702 underwent MR imaging resulting in 627 complete datasets across all testing. The entire data, a randomly chosen subset of 150 and 416 freely available data were used to investigate global and regional measurement methods in older brains and how the resultant measurements related to cognitive performance. Furthermore the presence of early life cognitive data in the form of a general intelligence test sat at age 11, served as an indicator of cognitive ability prior to the potential influence of the ageing process. The chapters concerning global measures at first establish, that a measure of intracranial volume (ICV) serves as both a way of correcting for individual differences in brain size between participants and as a proxy premorbid measure of brain size. The analysis, utilising freely available cross-sectional MRI data (http://www.oasis-brains.org) revealed that ICV differed very little between 18-28 year olds and 84-96 year olds where as total brain tissue volume (TBV) differed by 14.1% between the two groups, which was more than twice the standard deviation across the entire age range (18-96 years). Second a validated, reliable method for measuring ICV was investigated using 150 people randomly chosen from the LBC1936 study. Automated and semi-automated methods were validated against reference measurements the results of which showed that common ageing features make automated and semi-automated methods that do not have an additional manual editing step, ineffective at producing accurate ICV measurements. This analysis also highlighted the need to employ additional spatial overlap assessment to volumetric comparison of measurement methods to reduce the effect of false-positives and false-negatives skewing apparent discrepancies between methods. Using the information gained here ICV and TBV from the entire LBC1936 cohort were analysed in a structural equation model, alongside cognitive ability measures at both age 11 and age 73. We found that TBV was a stronger predictor of later life cognitive ability, after accounting for early life ability, but that a modest association remained between ICV and late life cognition. This suggests that early life factors pay a role in how well we age, though the relationship is complex. The regional measures chapters look at two brain regions commonly associated with ageing, the hippocampus and the frontal lobes. Measuring either of these brain regions in large samples of healthy older adults is challenging for many reasons. The hippocampus is small and as with all brain regions shows greater variation in older age, this makes employing automated methods that have the advantage of being fast and reproducible difficult. Following the results of our systematic review of automated methods for measuring the hippocampus, the two most commonly used and available automated methods were validated against reference standard measurements. The results indicated that although automated methods present an attractive alternative to laborious manual measurements they still require manual editing to produce accurate measurements in older adults. The modified strategy employed across the LBC1936 was to use an automated method and then manually edit the output; these segmentations were used to investigate the potential of multimodal image analysis in clarifying associations between the hippocampus and cognitive ability in old age. The analysis focused on associations between longitudinal relaxation time (T1), magnetization transfer ratio (MTR), fractional anisotropy (FA) and mean diffusivity (MD) in the hippocampus and general factors of fluid intelligence, cognitive processing speed and memory. The findings show that multi-modal MRI assessments were more sensitive than volumetric measurements at detecting associations with cognitive measures. The difficulty with producing a relevant frontal lobe measure was made apparent when the result of a large systematic review looking at the manual protocols used revealed 19 methods and 15 different landmarks had been employed. This resulted in an analysis that took the 5 most common boundaries reported and applied them to 10 randomly selected participants from the LBC1936. The results showed significant differences between the resultant volumes, with the smallest measurement when using the genu as the posterior marker representing only 35% of the measurement acquired using the central sulcus. The results from the studies presented in this thesis strongly highlight the need to develop age specific methods when using brain MRI to study ageing. Furthermore the implications of using unstandardised protocols, making assumptions about a methods performance based on validation in younger samples and the need to account for early life factors in this area of research have been made clearer. Studies building on these findings will be beneficial in elucidating the role of the brain in ageing

Foetal echocardiographic segmentation

Congenital heart disease affects just under one percentage of all live births [1]. Those defects that manifest themselves as changes to the cardiac chamber volumes are the motivation for the research presented in this thesis. Blood volume measurements in vivo require delineation of the cardiac chambers and manual tracing of foetal cardiac chambers is very time consuming and operator dependent. This thesis presents a multi region based level set snake deformable model applied in both 2D and 3D which can automatically adapt to some extent towards ultrasound noise such as attenuation, speckle and partial occlusion artefacts. The algorithm presented is named Mumford Shah Sarti Collision Detection (MSSCD). The level set methods presented in this thesis have an optional shape prior term for constraining the segmentation by a template registered to the image in the presence of shadowing and heavy noise. When applied to real data in the absence of the template the MSSCD algorithm is initialised from seed primitives placed at the centre of each cardiac chamber. The voxel statistics inside the chamber is determined before evolution. The MSSCD stops at open boundaries between two chambers as the two approaching level set fronts meet. This has significance when determining volumes for all cardiac compartments since cardiac indices assume that each chamber is treated in isolation. Comparison of the segmentation results from the implemented snakes including a previous level set method in the foetal cardiac literature show that in both 2D and 3D on both real and synthetic data, the MSSCD formulation is better suited to these types of data. All the algorithms tested in this thesis are within 2mm error to manually traced segmentation of the foetal cardiac datasets. This corresponds to less than 10% of the length of a foetal heart. In addition to comparison with manual tracings all the amorphous deformable model segmentations in this thesis are validated using a physical phantom. The volume estimation of the phantom by the MSSCD segmentation is to within 13% of the physically determined volume

Medical Image Segmentation Review: The success of U-Net

Automatic medical image segmentation is a crucial topic in the medical domain and successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most widespread image segmentation architecture due to its flexibility, optimized modular design, and success in all medical image modalities. Over the years, the U-Net model achieved tremendous attention from academic and industrial researchers. Several extensions of this network have been proposed to address the scale and complexity created by medical tasks. Addressing the deficiency of the naive U-Net model is the foremost step for vendors to utilize the proper U-Net variant model for their business. Having a compendium of different variants in one place makes it easier for builders to identify the relevant research. Also, for ML researchers it will help them understand the challenges of the biological tasks that challenge the model. To address this, we discuss the practical aspects of the U-Net model and suggest a taxonomy to categorize each network variant. Moreover, to measure the performance of these strategies in a clinical application, we propose fair evaluations of some unique and famous designs on well-known datasets. We provide a comprehensive implementation library with trained models for future research. In addition, for ease of future studies, we created an online list of U-Net papers with their possible official implementation. All information is gathered in https://github.com/NITR098/Awesome-U-Net repository.Comment: Submitted to the IEEE Transactions on Pattern Analysis and Machine Intelligence Journa