4,452 research outputs found
Serial optical coherence microscopy for label-free volumetric histopathology
The observation of histopathology using optical microscope is an essential procedure for examination of tissue biopsies or surgically excised specimens in biological and clinical laboratories. However, slide-based microscopic pathology is not suitable for visualizing the large-scale tissue and native 3D organ structure due to its sampling limitation and shallow imaging depth. Here, we demonstrate serial optical coherence microscopy (SOCM) technique that offers label-free, high-throughput, and large-volume imaging of ex vivo mouse organs. A 3D histopathology of whole mouse brain and kidney including blood vessel structure is reconstructed by deep tissue optical imaging in serial sectioning techniques. Our results demonstrate that SOCM has unique advantages as it can visualize both native 3D structures and quantitative regional volume without introduction of any contrast agents
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Automation Process for Morphometric Analysis of Volumetric CT Data from Pulmonary Vasculature in Rats
With advances in medical imaging scanners, it has become commonplace to generate large multidimensional datasets. These datasets require tools for a rapid, thorough analysis. To address this need, we have developed an automated algorithm for morphometric analysis incorporating A Visualization Workshop computational and image processing libraries for three-dimensional segmentation, vascular tree generation and structural hierarchical ordering with a two-stage numeric optimization procedure for estimating vessel diameters. We combine this new technique with our mathematical models of pulmonary vascular morphology to quantify structural and functional attributes of lung arterial trees. Our physiological studies require repeated measurements of vascular structure to determine differences in vessel biomechanical properties between animal models of pulmonary disease. Automation provides many advantages including significantly improved speed and minimized operator interaction and biasing. The results are validated by comparison with previously published rat pulmonary arterial micro-CT data analysis techniques, in which vessels were manually mapped and measured using intense operator intervention
Optical Coherence Tomography as a Diagnostic Tool in Renal Transplant and Cancer Imaging
This dissertation aims to investigate optical coherence tomography (OCT) as a diagnostic technology in renal imaging through two main arenas: renal transplantation and renal cancer.
Part 1: Ischemia-reperfusion injury, which frequently occurs after kidney transplant, is a major contributing factor in delayed-graft function leading to varying degrees of early renal dysfunction. Real-time assessment of graft morphological and hemodynamic changes could help to evaluate graft condition and offer valuable information to predict the prognosis of graft injury for patient-specific management strategies. Previous studies have shown the ability of OCT to monitor structural changes associated with ischemia-reperfusion injury in vivo. Therefore, we investigated the ability of Doppler OCT (DOCT) to image microcirculatory changes in real time in the kidney glomerulus in vivo in an animal model and quantified observed changes in blood flow in 3D. Then, we translated OCT/DOCT technology into clinical testing for renal imaging during transplantation procedures and demonstrated the ability of OCT/DOCT to reveal renal tubular morphology and blood flow immediately following reperfusion.
Part 2: For solid renal masses, nephron sparing procedures have been developed as an alternative to radical nephrectomy. However, achieving a negative tumor margin is critical to ensuring the best oncological efficacy for precluding tumor recurrence. OCT is a high-resolution, real-time imaging technology that has shown the ability to distinguish cancerous tissue from normal in several systems of the body based on changes in tissue optical properties. Therefore, we investigated the capability of OCT to quantify differences in optical properties between tumor and normal renal tissue. However, we did not observe a significant difference in optical attenuation between tumor and normal tissue in ex vivo specimens. These results suggest that further studies or possible alternative metrics need to be investigated to determine if OCT is able to detect renal neoplasms
Computer Vision and Medical Image Processing: a brief survey of application areas
Every day is greater the number of images obtained to characterize the anatomy and functions of the human body, because of this the automation of the medical image processing has become a practice to improve the diagnosis and treatment of certain diseases. In this study the main areas of application of computer vision to the digital processing of medical images are reviewed. It begins with the selection of the three edges with more publications available in Springer, ScienceDirect, Wiley, and IEEE which are: segmentation of organs and lesions, feature extraction in optical images and labelling machine on x-ray images. Over them, latest algorithms, techniques and methods for medical imaging processing are analyzed exposing its main characteristics and ways of use.Sociedad Argentina de Informática e Investigación Operativa (SADIO
Computer Vision and Medical Image Processing: a brief survey of application areas
Every day is greater the number of images obtained to characterize the anatomy and functions of the human body, because of this the automation of the medical image processing has become a practice to improve the diagnosis and treatment of certain diseases. In this study the main areas of application of computer vision to the digital processing of medical images are reviewed. It begins with the selection of the three edges with more publications available in Springer, ScienceDirect, Wiley, and IEEE which are: segmentation of organs and lesions, feature extraction in optical images and labelling machine on x-ray images. Over them, latest algorithms, techniques and methods for medical imaging processing are analyzed exposing its main characteristics and ways of use.Sociedad Argentina de Informática e Investigación Operativa (SADIO
Clinical MR in the year 2010
This article tries to look into the future of magnetic resonance imaging. Deducing from six rules, it pinpoints the most likely developments of this imaging techniqu
Computational fluid dynamics indicators to improve cardiovascular pathologies
In recent years, the study of computational hemodynamics within anatomically complex vascular regions has generated great interest among clinicians.
The progress in computational fluid dynamics, image processing and high-performance computing haveallowed us to identify the candidate vascular regions for the appearance of cardiovascular diseases and to predict how this disease may evolve.
Medicine currently uses a paradigm called diagnosis. In this thesis we attempt to introduce into medicine the predictive paradigm that has been used in engineering for many years. The objective of this thesis is therefore to develop predictive models based on diagnostic indicators for cardiovascular pathologies.
We try to predict the evolution of aortic abdominal aneurysm, aortic coarctation and coronary artery disease in a personalized way for each patient. To understand how the cardiovascular pathology will evolve and when it will become a health risk, it is necessary to develop new technologies by merging medical imaging and computational science. We propose diagnostic indicators that can improve the diagnosis and predict the evolution of the disease more efficiently than the methods used until now. In particular, a new methodology for computing diagnostic indicators based on computational hemodynamics and medical imaging is proposed. We have worked with data of anonymous patients to create real predictive technology that will allow us to continue advancing in personalized medicine and generate more sustainable health systems. However, our final aim is to achieve an impact at a clinical level. Several groups have tried to create predictive models for cardiovascular pathologies, but they have not yet begun to use them in clinical practice. Our objective is to go further and obtain predictive variables to be used practically in the clinical field.
It is to be hoped that in the future extremely precise databases of all of our anatomy and physiology will be available to doctors. These data can be used for predictive models to improve diagnosis or to improve therapies or personalized treatments.En els últims anys, l'estudi de l'hemodinàmica computacional en regions vasculars anatòmicament complexes ha generat un gran interès entre els clínics. El progrés obtingut en la dinàmica de fluids computacional, en el processament d'imatges i en la computació d'alt rendiment ha permès identificar regions vasculars on poden aparèixer malalties cardiovasculars, així com predir-ne l'evolució. Actualment, la medicina utilitza un paradigma anomenat diagnòstic. En aquesta tesi s'intenta introduir en la medicina el paradigma predictiu utilitzat des de fa molts anys en l'enginyeria. Per tant, aquesta tesi té com a objectiu desenvolupar models predictius basats en indicadors de diagnòstic de patologies cardiovasculars. Tractem de predir l'evolució de l'aneurisma d'aorta abdominal, la coartació aòrtica i la malaltia coronària de forma personalitzada per a cada pacient. Per entendre com la patologia cardiovascular evolucionarà i quan suposarà un risc per a la salut, cal desenvolupar noves tecnologies mitjançant la combinació de les imatges mèdiques i la ciència computacional. Proposem uns indicadors que poden millorar el diagnòstic i predir l'evolució de la malaltia de manera més eficient que els mètodes utilitzats fins ara. En particular, es proposa una nova metodologia per al càlcul dels indicadors de diagnòstic basada en l'hemodinàmica computacional i les imatges mèdiques. Hem treballat amb dades de pacients anònims per crear una tecnologia predictiva real que ens permetrà seguir avançant en la medicina personalitzada i generar sistemes de salut més sostenibles. Però el nostre objectiu final és aconseguir un impacte en l¿àmbit clínic. Diversos grups han tractat de crear models predictius per a les patologies cardiovasculars, però encara no han començat a utilitzar-les en la pràctica clínica. El nostre objectiu és anar més enllà i obtenir variables predictives que es puguin utilitzar de forma pràctica en el camp clínic. Es pot preveure que en el futur tots els metges disposaran de bases de dades molt precises de tota la nostra anatomia i fisiologia. Aquestes dades es poden utilitzar en els models predictius per millorar el diagnòstic o per millorar teràpies o tractaments personalitzats.Postprint (published version
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