Rule-Based Ventral Cavity Multi-organ Automatic Segmentation in CT Scans

We describe a newmethod for the automatic segmentation of multiple organs of the ventral cavity in CT scans. The methodisbasedonasetofrulesthatdeterminetheorderin which theorgansare isolatedand segmented, from the sim- plestonetothemostdifficultone. First,thebodyisisolated from the background. Second, the trachea and the left and right lungsare segmentedbasedon theirair content.Third, thespleenandthekidneys–theorganswithhighbloodcon- tent–aresegmented. Finally,thekidneyissegmentedbased onthesurroundingorganssegmentation. Eachorganisindi- vidually segmentedwitha four-stepprocedure that consists of: 1) definition of an inclusive region of interest; 2) identi- fication of the largest axial cross-section slice; 3) Semoval of background structuresbymorphologicaloperations,and; 4) 3D region growing segmentation. Ourmethod is unique in that ituses the same generic segmentation approach for all organsand in that it relieson the segmentationdifficultyof organs toguide the segmentationprocess. Experimental re- sultson15CTscansoftheVISCERALAnatomy2Challenge trainingdatasetsyieldaDicevolumeoverlapsimilarityscore of 79.1 for the trachea, 97.4 and 97.6 for the left and right lungs, 89.2 for the spleen, and 92.8 for the left kidney. For the5CT scans testdatasets, theDice scoresare97.9,97.0, 85.6, 93.4 and 90.2, respectively. Our method achieved an overallDICE score of 92.8 andwas ranked first among the fivemethodsthatparticipated inthechallenge. Copyright c © by the paper’s authors. Copying permitted only for private and academic purposes

Cloud-Based Benchmarking of Medical Image Analysis

Medical imagin

Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

Detecting abnormalities in two-dimensional (2D) and three-dimensional (3D) medical structures is among the most interesting and challenging research areas in the medical imaging field. Obtaining the desired accurate automated quantification of abnormalities in medical structures is still very challenging. This is due to a large and constantly growing number of different objects of interest and associated abnormalities, large variations of their appearances and shapes in images, different medical imaging modalities, and associated changes of signal homogeneity and noise for each object. The main objective of this dissertation is to address these problems and to provide proper mathematical models and techniques that are capable of analyzing low and high resolution medical data and providing an accurate, automated analysis of the abnormalities in medical structures in terms of their area/volume, shape, and associated abnormal functionality. This dissertation presents different preliminary mathematical models and techniques that are applied in three case studies: (i) detecting abnormal tissue in the left ventricle (LV) wall of the heart from delayed contrast-enhanced cardiac magnetic resonance images (MRI), (ii) detecting local cardiac diseases based on estimating the functional strain metric from cardiac cine MRI, and (iii) identifying the abnormalities in the corpus callosum (CC) brain structure—the largest fiber bundle that connects the two hemispheres in the brain—for subjects that suffer from developmental brain disorders. For detecting the abnormal tissue in the heart, a graph-cut mathematical optimization model with a cost function that accounts for the object’s visual appearance and shape is used to segment the the inner cavity. The model is further integrated with a geometric model (i.e., a fast marching level set model) to segment the outer border of the myocardial wall (the LV). Then the abnormal tissue in the myocardium wall (also called dead tissue, pathological tissue, or infarct area) is identified based on a joint Markov-Gibbs random field (MGRF) model of the image and its region (segmentation) map that accounts for the pixel intensities and the spatial interactions between the pixels. Experiments with real in-vivo data and comparative results with ground truth (identified by a radiologist) and other approaches showed that the proposed framework can accurately detect the pathological tissue and can provide useful metrics for radiologists and clinicians. To estimate the strain from cardiac cine MRI, a novel method based on tracking the LV wall geometry is proposed. To achieve this goal, a partial differential equation (PDE) method is applied 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. The main advantage of the proposed tracking method over traditional texture-based methods is its ability to track the movement and rotation of the LV wall based on tracking the geometric features of the inner, mid-, and outer walls of the LV. This overcomes noise sources that come from scanner and heart motion. To identify the abnormalities in the CC from brain MRI, the CCs are aligned using a rigid registration model and are segmented using a shape-appearance model. Then, they are mapped to a simple unified space for analysis. This work introduces a novel cylindrical mapping model, which is conformal (i.e., one to one transformation and bijective), that enables accurate 3D shape analysis of the CC in the cylindrical domain. The framework can detect abnormalities in all divisions of the CC (i.e., splenium, rostrum, genu and body). In addition, it offers a whole 3D analysis of the CC abnormalities instead of only area-based analysis as done by previous groups. The initial classification results based on the centerline length and CC thickness suggest that the proposed CC shape analysis is a promising supplement to the current techniques for diagnosing dyslexia. The proposed techniques in this dissertation have been successfully tested on complex synthetic and MR images and can be used to advantage in many of today’s clinical applications of computer-assisted medical diagnostics and intervention

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society

Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives

Deep learning has demonstrated remarkable performance across various tasks in medical imaging. However, these approaches primarily focus on supervised learning, assuming that the training and testing data are drawn from the same distribution. Unfortunately, this assumption may not always hold true in practice. To address these issues, unsupervised domain adaptation (UDA) techniques have been developed to transfer knowledge from a labeled domain to a related but unlabeled domain. In recent years, significant advancements have been made in UDA, resulting in a wide range of methodologies, including feature alignment, image translation, self-supervision, and disentangled representation methods, among others. In this paper, we provide a comprehensive literature review of recent deep UDA approaches in medical imaging from a technical perspective. Specifically, we categorize current UDA research in medical imaging into six groups and further divide them into finer subcategories based on the different tasks they perform. We also discuss the respective datasets used in the studies to assess the divergence between the different domains. Finally, we discuss emerging areas and provide insights and discussions on future research directions to conclude this survey.Comment: Under Revie

Advanced Applications of Rapid Prototyping Technology in Modern Engineering

Rapid prototyping (RP) technology has been widely known and appreciated due to its flexible and customized manufacturing capabilities. The widely studied RP techniques include stereolithography apparatus (SLA), selective laser sintering (SLS), three-dimensional printing (3DP), fused deposition modeling (FDM), 3D plotting, solid ground curing (SGC), multiphase jet solidification (MJS), laminated object manufacturing (LOM). Different techniques are associated with different materials and/or processing principles and thus are devoted to specific applications. RP technology has no longer been only for prototype building rather has been extended for real industrial manufacturing solutions. Today, the RP technology has contributed to almost all engineering areas that include mechanical, materials, industrial, aerospace, electrical and most recently biomedical engineering. This book aims to present the advanced development of RP technologies in various engineering areas as the solutions to the real world engineering problems

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

Machine learning approaches to model cardiac shape in large-scale imaging studies

Recent improvements in non-invasive imaging, together with the introduction of fully-automated segmentation algorithms and big data analytics, has paved the way for large-scale population-based imaging studies. These studies promise to increase our understanding of a large number of medical conditions, including cardiovascular diseases. However, analysis of cardiac shape in such studies is often limited to simple morphometric indices, ignoring large part of the information available in medical images. Discovery of new biomarkers by machine learning has recently gained traction, but often lacks interpretability. The research presented in this thesis aimed at developing novel explainable machine learning and computational methods capable of better summarizing shape variability, to better inform association and predictive clinical models in large-scale imaging studies. A powerful and flexible framework to model the relationship between three-dimensional (3D) cardiac atlases, encoding multiple phenotypic traits, and genetic variables is first presented. The proposed approach enables the detection of regional phenotype-genotype associations that would be otherwise neglected by conventional association analysis. Three learning-based systems based on deep generative models are then proposed. In the first model, I propose a classifier of cardiac shapes which exploits task-specific generative shape features, and it is designed to enable the visualisation of the anatomical effect these features encode in 3D, making the classification task transparent. The second approach models a database of anatomical shapes via a hierarchy of conditional latent variables and it is capable of detecting, quantifying and visualising onto a template shape the most discriminative anatomical features that characterize distinct clinical conditions. Finally, a preliminary analysis of a deep learning system capable of reconstructing 3D high-resolution cardiac segmentations from a sparse set of 2D views segmentations is reported. This thesis demonstrates that machine learning approaches can facilitate high-throughput analysis of normal and pathological anatomy and of its determinants without losing clinical interpretability.Open Acces

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