Vision-based estimation of volume status in ultrasound

This thesis provides a proof-of-concept approach to the analysis of ultrasound imagery using machine learning and computer vision for the purposes of tracking relative changes in apparent circulating blood volume. Data for the models was collected from a simulation which involved having healthy subjects recline at angles between 0 and 90 degrees to induce changes in the size of the internal jugular vein (IJV) resulting from gravity. Ultrasound video clips were then captured of the IJV. The clips were segmented, followed by feature generation, feature selection and training of predictive models to determine the angle of inclination. This research provides insight into the feasibility of using automated analysis techniques to enhance portable ultrasound as a monitoring tool. In a dataset of 34 subjects the angle was predicted within 11 degrees. An accuracy of 89% was achieved for high/low classification

Computer-based estimation of circulating blood volume from ultrasound imagery

Detection of relative changes in circulating blood volume is important to guide resuscitation and manage a variety of medical conditions including sepsis, trauma, dialysis and congestive heart failure. In recent years, ultrasound images of inferior vena cava (IVC) and internal jugular vein (IJV) have been used to assess volume status and guide fluid administration. This approach has limitations in that a skilled operator must perform repeated measurements over time. In this dissertation, we develop semi-automatic image processing algorithms for estimation and tracking of the IVC anterior-posterior (AP)-diameter and IJV crosssectional area in ultrasound videos. The proposed algorithms are based on active contours (ACs), where either the IVC AP-diameter or IJV CSA is estimated by minimization of an energy functional. More specifically, in chapter 2, we propose a novel energy functional based on the third centralized moment and show that it outperforms the functionals that are traditionally used with active contours (ACs). We combine the proposed functional with the polar contour representation and use it for segmentation of the IVC. In chapters 3 and 4, we propose active shape models based on ellipse; circle; and rectangles fitted inside the IVC as efficient, consistent and novel approaches to tracking and approximating the anterior-posterior (AP)-diameter even in the context of poor quality images. The proposed algorithms are based on a novel heuristic evolution functional that works very well with ultrasound images. In chapter 3, we show that the proposed active circle algorithm accurately, estimates the IVC AP-diameter. Although the estimated AP-diameter is very close to its actual value, the clinicians define the IVC AP-diameter as the largest vertical diameter of the IVC contour which deviates from its actual definition. To solve this problem and estimate the AP-diameter in the same way as its clinical definition, in chapter 4, we propose the active rectangle algorithm, where clinically measured AP-diameter is modeled as the height of a vertical thin rectangle. The results show that the AP-diameter estimated by the active rectangle algorithm is closer to its clinically measurement than the active circle and active ellipse algorithms. In chapter 5, we propose a novel adaptive polar active contour (Ad-PAC) algorithm for the segmentation and tracking of the IJV in ultrasound videos. In the proposed algorithm, the parameters of the Ad-PAC algorithm are adapted based on the results of segmentation in previous frames. The Ad-PAC algorithm has been applied to 65 ultrasound videos and shown to be a significant improvement over existing segmentation algorithms. So far, all proposed algorithms are semi-automatic as they need an operator to either locate the vessel in the first frame, or manually segment the first first and work automatically for the next frames. In chapter 6, we proposed a novel algorithm to automatically locate the vessel in ultrasound videos. The proposed algorithm is based on convolutional neural networks (CNNs) and is trained and applied for IJV videos. In this chapter we show that although the proposed algorithm is trained for data acquired from healthy subjects, it works efficiently for the data collected from coronary heart failure (CHF) patients without additional training. Finally, conclusions are drawn and possible extensions are discussed in chapter 7

Biomedical Image Processing and Classification

Biomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture

Echocardiography

The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography

Interactive Segmentation of 3D Medical Images with Implicit Surfaces

To cope with a variety of clinical applications, research in medical image processing has led to a large spectrum of segmentation techniques that extract anatomical structures from volumetric data acquired with 3D imaging modalities. Despite continuing advances in mathematical models for automatic segmentation, many medical practitioners still rely on 2D manual delineation, due to the lack of intuitive semi-automatic tools in 3D. In this thesis, we propose a methodology and associated numerical schemes enabling the development of 3D image segmentation tools that are reliable, fast and interactive. These properties are key factors for clinical acceptance. Our approach derives from the framework of variational methods: segmentation is obtained by solving an optimization problem that translates the expected properties of target objects in mathematical terms. Such variational methods involve three essential components that constitute our main research axes: an objective criterion, a shape representation and an optional set of constraints. As objective criterion, we propose a unified formulation that extends existing homogeneity measures in order to model the spatial variations of statistical properties that are frequently encountered in medical images, without compromising efficiency. Within this formulation, we explore several shape representations based on implicit surfaces with the objective to cover a broad range of typical anatomical structures. Firstly, to model tubular shapes in vascular imaging, we introduce convolution surfaces in the variational context of image segmentation. Secondly, compact shapes such as lesions are described with a new representation that generalizes Radial Basis Functions with non-Euclidean distances, which enables the design of basis functions that naturally align with salient image features. Finally, we estimate geometric non-rigid deformations of prior templates to recover structures that have a predictable shape such as whole organs. Interactivity is ensured by restricting admissible solutions with additional constraints. Translating user input into constraints on the sign of the implicit representation at prescribed points in the image leads us to consider inequality-constrained optimization

Role of deep learning techniques in non-invasive diagnosis of human diseases.

Machine learning, a sub-discipline in the domain of artificial intelligence, concentrates on algorithms able to learn and/or adapt their structure (e.g., parameters) based on a set of observed data. The adaptation is performed by optimizing over a cost function. Machine learning obtained a great attention in the biomedical community because it offers a promise for improving sensitivity and/or specificity of detection and diagnosis of diseases. It also can increase objectivity of the decision making, decrease the time and effort on health care professionals during the process of disease detection and diagnosis. The potential impact of machine learning is greater than ever due to the increase in medical data being acquired, the presence of novel modalities being developed and the complexity of medical data. In all of these scenarios, machine learning can come up with new tools for interpreting the complex datasets that confront clinicians. Much of the excitement for the application of machine learning to biomedical research comes from the development of deep learning which is modeled after computation in the brain. Deep learning can help in attaining insights that would be impossible to obtain through manual analysis. Deep learning algorithms and in particular convolutional neural networks are different from traditional machine learning approaches. Deep learning algorithms are known by their ability to learn complex representations to enhance pattern recognition from raw data. On the other hand, traditional machine learning requires human engineering and domain expertise to design feature extractors and structure data. With increasing demands upon current radiologists, there are growing needs for automating the diagnosis. This is a concern that deep learning is able to address. In this dissertation, we present four different successful applications of deep learning for diseases diagnosis. All the work presented in the dissertation utilizes medical images. In the first application, we introduce a deep-learning based computer-aided diagnostic system for the early detection of acute renal transplant rejection. The system is based on the fusion of both imaging markers (apparent diffusion coefficients derived from diffusion-weighted magnetic resonance imaging) and clinical biomarkers (creatinine clearance and serum plasma creatinine). The fused data is then used as an input to train and test a convolutional neural network based classifier. The proposed system is tested on scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. In the second application, we propose a novel deep learning approach for the automated segmentation and quantification of the LV from cardiac cine MR images. We aimed at achieving lower errors for the estimated heart parameters compared to the previous studies by proposing a novel deep learning segmentation method. Using fully convolutional neural networks, we proposed novel methods for the extraction of a region of interest that contains the left ventricle, and the segmentation of the left ventricle. Following myocardial segmentation, functional and mass parameters of the left ventricle are estimated. Automated Cardiac Diagnosis Challenge dataset was used to validate our framework, which gave better segmentation, accurate estimation of cardiac parameters, and produced less error compared to other methods applied on the same dataset. Furthermore, we showed that our segmentation approach generalizes well across different datasets by testing its performance on a locally acquired dataset. In the third application, we propose a novel deep learning approach for automated quantification of strain from cardiac cine MR images of mice. For strain analysis, we developed a Laplace-based approach to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. Following tracking, the strain estimation is performed using the Lagrangian-based approach. This new automated system for strain analysis was validated by comparing the outcome of these analysis with the tagged MR images from the same mice. There were no significant differences between the strain data obtained from our algorithm using cine compared to tagged MR imaging. In the fourth application, we demonstrate how a deep learning approach can be utilized for the automated classification of kidney histopathological images. Our approach can classify four classes: the fat, the parenchyma, the clear cell renal cell carcinoma, and the unusual cancer which has been discovered recently, called clear cell papillary renal cell carcinoma. Our framework consists of three convolutional neural networks and the whole-slide kidney images were divided into patches with three different sizes to be inputted to the networks. Our approach can provide patch-wise and pixel-wise classification. Our approach classified the four classes accurately and surpassed other state-of-the-art methods such as ResNet (pixel accuracy: 0.89 Resnet18, 0.93 proposed). In conclusion, the results of our proposed systems demonstrate the potential of deep learning for the efficient, reproducible, fast, and affordable disease diagnosis

Segmentation and Deformable Modelling Techniques for a Virtual Reality Surgical Simulator in Hepatic Oncology

Liver surgical resection is one of the most frequently used curative therapies. However, resectability is problematic. There is a need for a computer-assisted surgical planning and simulation system which can accurately and efficiently simulate the liver, vessels and tumours in actual patients. The present project describes the development of these core segmentation and deformable modelling techniques. For precise detection of irregularly shaped areas with indistinct boundaries, the segmentation incorporated active contours - gradient vector flow (GVF) snakes and level sets. To improve efficiency, a chessboard distance transform was used to replace part of the GVF effort. To automatically initialize the liver volume detection process, a rotating template was introduced to locate the starting slice. For shape maintenance during the segmentation process, a simplified object shape learning step was introduced to avoid occasional significant errors. Skeletonization with fuzzy connectedness was used for vessel segmentation. To achieve real-time interactivity, the deformation regime of this system was based on a single-organ mass-spring system (MSS), which introduced an on-the-fly local mesh refinement to raise the deformation accuracy and the mesh control quality. This method was now extended to a multiple soft-tissue constraint system, by supplementing it with an adaptive constraint mesh generation. A mesh quality measure was tailored based on a wide comparison of classic measures. Adjustable feature and parameter settings were thus provided, to make tissues of interest distinct from adjacent structures, keeping the mesh suitable for on-line topological transformation and deformation. More than 20 actual patient CT and 2 magnetic resonance imaging (MRI) liver datasets were tested to evaluate the performance of the segmentation method. Instrument manipulations of probing, grasping, and simple cutting were successfully simulated on deformable constraint liver tissue models. This project was implemented in conjunction with the Division of Surgery, Hammersmith Hospital, London; the preliminary reality effect was judged satisfactory by the consultant hepatic surgeon

Non-invasive hemodynamic monitoring by electrical impedance tomography

The monitoring of central hemodynamic parameters such as cardiac output (CO) and pulmonary artery pressure (PAP) is of paramount clinical importance to assess the health status of the cardiovascular system. However, their measurement requires the insertion of a pulmonary artery catheter, a highly invasive procedure associated with non-negligible morbidity and mortality rates. In this thesis, we investigated the clinical potential of electrical impedance tomography (EIT) - a radiation-free medical imaging technique - as a non-invasive alternative for the measurement of CO and PAP. In a first phase, we investigated the potential of EIT for the measurement of CO. This measurement is implicitly based on the hypothesis that the EIT heart signal (the ventricular component of the EIT signals) is induced by ventricular blood volume changes. This hypothesis has never been formally investigated, and the exact origins of the EIT heart signal remain subject to interpretation. Therefore, using a model, we investigated the genesis of this signal by identifying its various sources and their respective contributions. The results revealed that the EIT heart signal is dominated by cardioballistic effects (heart motion). However, although of prominently cardioballistic origin, the amplitude of the signal has shown to be strongly correlated to stroke volume (r = 0.996, p < 0.001; error of 0.57 +/- 2.19 mL). We explained these observations by the quasi-incompressibility of myocardial tissue and blood. We further identified several factors and conditions susceptible to affect the accuracy of the measurement. Finally, we investigated the influence of the EIT sensor belt position on the measured heart signal. We observed that small belt displacements - likely to occur in clinical settings during patient handling - can induce errors of up to 30 mL on stroke volume estimation. In a second phase, we investigated the feasibility of a novel method for the non-invasive measurement of PAP by EIT. The method is based on the physiological relation linking the PAP to the velocity of propagation of the pressure waves in the pulmonary arteries. We hypothesized that the variations of this velocity, and therefore of the PAP, could be measured by EIT. In a bioimpedance model of the human thorax, we demonstrated the feasibility of our method in various types of pulmonary hypertensive disorders. Our EIT-derived parameter has shown to be particularly well-suited for predicting early changes in pulmonary hemodynamics due to its physiological link with arterial compliance. Finally, we validated experimentally our method in 14 subjects undergoing hypoxia-induced PAP changes. Significant correlation coefficients (range: [0.70, 0.98], average: 0.89) and small standard errors of the estimate (range: [0.9, 6.3] mmHg, average: 2.4 mmHg) were found between our EIT-derived systolic PAP and reference systolic PAP values obtained by Doppler echocardiography. In conclusion, there is a promising outlook for EIT in non-invasive hemodynamic monitoring. Our observations provide novel insights for the interpretation and understanding of EIT heart signals, and detail the physiological and metrological requirements for an accurate measurement of CO by EIT. Our novel PAP monitoring method, validated in vivo, allows a reliable tracking of PAP changes, thereby paving the way towards the development of a new branch of non-invasive hemodynamic monitors based on the use of EIT