Manual and automatic image analysis segmentation methods for blood flow studies in microchannels

In blood flow studies, image analysis plays an extremely important role to examine raw data obtained by high-speed video microscopy systems. This work shows different ways to process the images which contain various blood phenomena happening in microfluidic devices and in microcirculation. For this purpose, the current methods used for tracking red blood cells (RBCs) flowing through a glass capillary and techniques to measure the cell-free layer thickness in different kinds of microchannels will be presented. Most of the past blood flow experimental data have been collected and analyzed by means of manual methods, that can be extremely reliable, but they are highly time-consuming, user-intensive, repetitive, and the results can be subjective to user-induced errors. For this reason, it is crucial to develop image analysis methods able to obtain the data automatically. Concerning automatic image analysis methods for individual RBCs tracking and to measure the well known microfluidic phenomena cell-free layer, two developed methods are presented and discussed in order to demonstrate their feasibility to obtain accurate data acquisition in such studies. Additionally, a comparison analysis between manual and automatic methods was performed.This project has been funded by Portuguese national funds of FCT/MCTES (PIDDAC) through the base funding from the following research units: UIDB/00532/2020 (Transport Phenomena Research Center—CEFT), UIDB/04077/2020 (Mechanical Engineering and Resource Sustainability Center—MEtRICs), UIDB/00690/2020 (CIMO). The authors are also grateful for the partial funding of FCT through the projects, NORTE-01-0145-FEDER-029394 (PTDC/EMD-EMD/29394/2017) and NORTE-01-0145-FEDER-030171 (PTDC/EMD-EMD/30171/2017) funded by COMPETE2020, NORTE2020, PORTUGAL2020 and FEDER. D. Bento acknowledges the PhD scholarship SFRH/BD/ 91192/2012 granted by FCT

Computer aided diagnosis algorithms for digital microscopy

Automatic analysis and information extraction from an image is still a highly chal- lenging research problem in the computer vision area, attempting to describe the image content with computational and mathematical techniques. Moreover the in- formation extracted from the image should be meaningful and as most discrimi- natory as possible, since it will be used to categorize its content according to the analysed problem. In the Medical Imaging domain this issue is even more felt because many important decisions that affect the patient care, depend on the use- fulness of the information extracted from the image. Manage medical image is even more complicated not only due to the importance of the problem, but also because it needs a fair amount of prior medical knowledge to be able to represent with data the visual information to which pathologist refer. Today medical decisions that impact patient care rely on the results of laboratory tests to a greater extent than ever before, due to the marked expansion in the number and complexity of offered tests. These developments promise to improve the care of patients, but the more increase the number and complexity of the tests, the more increases the possibility to misapply and misinterpret the test themselves, leading to inappropriate diagnosis and therapies. Moreover, with the increased number of tests also the amount of data to be analysed increases, forcing pathologists to devote much time to the analysis of the tests themselves rather than to patient care and the prescription of the right therapy, especially considering that most of the tests performed are just check up tests and most of the analysed samples come from healthy patients. Then, a quantitative evaluation of medical images is really essential to overcome uncertainty and subjectivity, but also to greatly reduce the amount of data and the timing for the analysis. In the last few years, many computer assisted diagno- sis systems have been developed, attempting to mimic pathologists by extracting features from the images. Image analysis involves complex algorithms to identify and characterize cells or tissues using image pattern recognition technology. This thesis addresses the main problems associated to the digital microscopy analysis in histology and haematology diagnosis, with the development of algorithms for the extraction of useful information from different digital images, but able to distinguish different biological structures in the images themselves. The proposed methods not only aim to improve the degree of accuracy of the analysis, and reducing time, if used as the only means of diagnoses, but also they can be used as intermediate tools for skimming the number of samples to be analysed directly from the pathologist, or as double check systems to verify the correct results of the automated facilities used today

Discovery of new biomarkers of mild cognitive impairment and Alzheimer\u27s disease risk in buccal cells using laser scanning cytometry

Previous studies have shown that mild cognitive impairment (MCI) may reflect the early stages of more pronounced neurodegenerative disorders such as Alzheimer’s disease (AD). In clinical practice, patients with AD are not usually identified until the disease has progressed to a stage when primary prevention is no longer possible. Therefore there is a need for a minimally invasive and inexpensive diagnostic to identify those who exhibit cellular pathology indicative of MCI and AD risk so that they can be prioritised for primary prevention. Human buccal cells are accessible in a minimally invasive manner, and exhibit cytological and nuclear morphologies that may be indicative of accelerated ageing or neurodegenerative disorders such as AD. The hypothesis that a minimally invasive approach using isolated buccal mucosa cells can be used to identify individuals diagnosed with MCI or AD was therefore tested using laser scanning cytometry (LSC). LSC combines the principles of flow cytometry, quantitative imaging and immunohistochemistry with high-content, multi-color fluorescence analysis, and can be used to identify specific cells in a heterogeneous population as well as scoring unique molecular events within them. This study aimed at investigating buccal cell types (buccal cell cytome) by the use of high-content LSC analysis and to detect potential biomarkers of MCI and AD risk i.e. buccal cell types, nuclear DNA content, intracellular neutral lipids, Tau protein and amyloid-β (Aβ) protein. Buccal cells were sampled from the South Australian Alzheimer’s Nutrition & DNA Damage study (SAND) or the The Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL), fixed and stained with labelled fluorescent antibodies (for detection of Aβ and Tau) and/or DAPI, Fast Green and Oil Red O dyes. In an initial study an LSC protocol was developed to identify and measure differences in buccal cell types and nuclear DNA content as well as a significant increase in micronuclei measured in AD (n=10) and Down’s syndrome (n=10) compared to their respective controls (n=20). Another LSC protocol measured a significant increase in DNA content and hyperdiploidy (as measured by DAPI fluorescence) as well as a significant decrease in neutral lipid content (measured by Oil Red O staining) in buccal cells of MCI (n=22) and AD (n=15) compared to controls (n=37) from the SAND study. Using another novel LSC protocol a significant increase in Aβ was measured in buccal cells from AD (n=20) compared to controls (n=20) from the AIBL study. Immunocytochemistry and ELISA experiments showed no significant differences in putative buccal cell Tau protein. The diagnostic value of parameters examined in these studies, individually or in combination was assessed and reported as specificity and sensitivity scores. In these studies, LSC has proven to be an efficient and useful technology for high-content analysis of buccal cells. Moreover, the changes in the buccal cell cytome observed using LSC may reflect alterations in the metabolism, cellular kinetics, gene expression, genome stability or structural profile of the buccal mucosa, and may prove useful as potential biomarkers in identifying individuals with a high risk of developing MCI and eventually AD

Methodology for automatic classification of atypical lymphoid cells from peripheral blood cell images

Morphological analysis is the starting point for the diagnostic approach of more than 80% of the hematological diseases. However, the morphological differentiation among different types of abnormal lymphoid cells in peripheral blood is a difficult task, which requires high experience and skill. Objective values do not exist to define cytological variables, which sometimes results in doubts on the correct cell classification in the daily hospital routine. Automated systems exist which are able to get an automatic preclassification of the normal blood cells, but fail in the automatic recognition of the abnormal lymphoid cells. The general objective of this thesis is to develop a complete methodology to automatically recognize images of normal and reactive lymphocytes, and several types of neoplastic lymphoid cells circulating in peripheral blood in some mature B-cell neoplasms using digital image processing methods. This objective follows two directions: (1) with engineering and mathematical background, transversal methodologies and software tools are developed; and (2) with a view towards the clinical laboratory diagnosis, a system prototype is built and validated, whose input is a set of pathological cell images from individual patients, and whose output is the automatic classification in one of the groups of the different pathologies included in the system. This thesis is the evolution of various works, starting with a discrimination between normal lymphocytes and two types of neoplastic lymphoid cells, and ending with the design of a system for the automatic recognition of normal lymphocytes and five types of neoplastic lymphoid cells. All this work involves the development of a robust segmentation methodology using color clustering, which is able to separate three regions of interest: cell, nucleus and peripheral zone around the cell. A complete lymphoid cell description is developed by extracting features related to size, shape, texture and color. To reduce the complexity of the process, a feature selection is performed using information theory. Then, several classifiers are implemented to automatically recognize different types of lymphoid cells. The best classification results are achieved using support vector machines with radial basis function kernel. The methodology developed, which combines medical, engineering and mathematical backgrounds, is the first step to design a practical hematological diagnosis support tool in the near future.Los análisis morfológicos son el punto de partida para la orientación diagnóstica en más del 80% de las enfermedades hematológicas. Sin embargo, la clasificación morfológica entre diferentes tipos de células linfoides anormales en la sangre es una tarea difícil que requiere gran experiencia y habilidad. No existen valores objetivos para definir variables citológicas, lo que en ocasiones genera dudas en la correcta clasificación de las células en la práctica diaria en un laboratorio clínico. Existen sistemas automáticos que realizan una preclasificación automática de las células sanguíneas, pero no son capaces de diferenciar automáticamente las células linfoides anormales. El objetivo general de esta tesis es el desarrollo de una metodología completa para el reconocimiento automático de imágenes de linfocitos normales y reactivos, y de varios tipos de células linfoides neoplásicas circulantes en sangre periférica en algunos tipos de neoplasias linfoides B maduras, usando métodos de procesamiento digital de imágenes. Este objetivo sigue dos direcciones: (1) con una orientación propia de la ingeniería y la matemática de soporte, se desarrollan las metodologías transversales y las herramientas de software para su implementación; y (2) con un enfoque orientado al diagnóstico desde el laboratorio clínico, se construye y se valida un prototipo de un sistema cuya entrada es un conjunto de imágenes de células patológicas de pacientes analizados de forma individual, obtenidas mediante microscopía y cámara digital, y cuya salida es la clasificación automática en uno de los grupos de las distintas patologías incluidas en el sistema. Esta tesis es el resultado de la evolución de varios trabajos, comenzando con una discriminación entre linfocitos normales y dos tipos de células linfoides neoplásicas, y terminando con el diseño de un sistema para el reconocimiento automático de linfocitos normales y reactivos, y cinco tipos de células linfoides neoplásicas. Todo este trabajo involucra el desarrollo de una metodología de segmentación robusta usando agrupamiento por color, la cual es capaz de separar tres regiones de interés: la célula, el núcleo y la zona externa alrededor de la célula. Se desarrolla una descripción completa de la célula linfoide mediante la extracción de descriptores relacionados con el tamaño, la forma, la textura y el color. Para reducir la complejidad del proceso, se realiza una selección de descriptores usando teoría de la información. Posteriormente, se implementan varios clasificadores para reconocer automáticamente diferentes tipos de células linfoides. Los mejores resultados de clasificación se logran utilizando máquinas de soporte vectorial con núcleo de base radial. La metodología desarrollada, que combina conocimientos médicos, matemáticos y de ingeniería, es el primer paso para el diseño de una herramienta práctica de soporte al diagnóstico hematológico en un futuro cercano

COMPUTATIONAL APPROACHES TO UNDERSTAND PHENOTYPIC STRUCTURE AND CONSTITUTIVE MECHANICS RELATIONSHIPS OF SINGLE CELLS

The goal of this work is to better understand the relationship between the structure and function of biological cells by simulating their nonlinear mechanical behavior under static and dynamic loading using image structure-based finite element modeling (FEM). Vascular smooth muscle cells (VSMCs) are chosen for this study due to the strong correlation of the geometric arrangement of their structural components on their mechanical behavior and the implications of that behavior on diseases such as atherosclerosis. VSMCs are modeled here using a linear elastic material model together with truss elements, which simulate the cytoskeletal fiber network that provides the cells with much of their internal structural support. Geometric characterization of single VSMCs of two physiologically relevant phenotypes in 2D cell culture is achieved using confocal microscopy in conjunction with novel image processing techniques. These computer vision techniques use image segmentation, 2D frequency analysis, and linear programming approaches to create representative 3D model structures consisting of the cell nucleus, cytoplasm, and actin stress fiber network of each cell. These structures are then imported into MSC Patran for structural analysis with Marc. Mechanical characterization is achieved using atomic force microscopy (AFM) indentation. Material properties for each VSMC model are input based on values individually obtained through experimentation, and the results of each model are compared against those experimental values. This study is believed to be a significant step towards the viability of finite element models in the field of cellular mechanics because the geometries of the cells in the model are based on confocal microscopy images of actual cells and thus, the results of the model can be compared against experimental data for those same cells