Representación de imágenes de histopatología utilizada en tareas de análisis automático: estado del arte

This paper presents a review of the state-of-the-art in histopathology image representation used in automatic image analysis tasks. Automatic analysis of histopathology images is important for building computer-assisted diagnosis tools, automatic image enhancing systems and virtual microscopy systems, among other applications. Histopathology images have a rich mix of visual patterns with particularities that make them difficult to analyze. The paper discusses these particularities, the acquisition process and the challenges found when doing automatic analysis. Second an overview of recent works and methods addressed to deal with visual content representation in different automatic image analysis tasks is presented. Third an overview of applications of image representation methods in several medical domains and tasks is presented. Finally, the paper concludes with current trends of automatic analysis of histopathology images like digital pathology

Local object patterns for tissue image representation and cancer classification

Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent Univ., 2013.Thesis (Master's) -- Bilkent University, 2013.Includes bibliographical refences.Histopathological examination of a tissue is the routine practice for diagnosis and grading of cancer. However, this examination is subjective since it requires visual interpretation of a pathologist, which mainly depends on his/her experience and expertise. In order to minimize the subjectivity level, it has been proposed to use automated cancer diagnosis and grading systems that represent a tissue image with quantitative features and use these features for classifying and grading the tissue. In this thesis, we present a new approach for effective representation and classification of histopathological tissue images. In this approach, we propose to decompose a tissue image into its histological components and introduce a set of new texture descriptors, which we call local object patterns, on these components to model their composition within a tissue. We define these descriptors using the idea of local binary patterns. However, we define our local object pattern descriptors at the component-level to quantify a component, as opposed to pixel-level local binary patterns, which quantify a pixel by constructing a binary string based on relative intensities of its neighbors. To this end, we specify neighborhoods with different locality ranges and encode spatial arrangements of the components within the specified local neighborhoods by generating strings. We then extract our texture descriptors from these strings to characterize histological components and construct the bag-of-words representation of an image from the characterized components. In this thesis, we use two approaches for the selection of the components: The first approach uses all components to construct a bag-ofwords representation whereas the second one uses graph walking to select multiple subsets of the components and constructs multiple bag-of-words representations from these subsets. Working with microscopic images of histopathological colon tissues, our experiments show that the proposed component-level texture descriptors lead to higher classification accuracies than the previous textural approaches.Olgun, GüldenM.S

Resampling-based Markovian modeling for automated cancer diagnosis

Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2011.Thesis (Master's) -- Bilkent University, 2011.Includes bibliographical references leaves 62-69.Correct diagnosis and grading of cancer is very crucial for planning an effective treatment. However, cancer diagnosis on biopsy images involves visual interpretation of a pathologist, which is highly subjective. This subjectivity may, however, lead to selecting suboptimal treatment plans. In order to circumvent this problem, it has been proposed to use automatic diagnosis and grading systems that help decrease the subjectivity levels by providing quantitative measures. However, one major challenge for designing these systems is the existence of high variance observed in the biopsy images due to the nature of biopsies. Thus, for successful classifications of unseen images, these systems should be trained with a large number of labeled images. However, most of the training sets in this domain have limited size of labeled data since it is quite difficult to collect and label histopathological images. In this thesis, we successfully address this issue by presenting a new resampling framework. This framework relies on increasing the generalization capacity of a classifier by augmenting the size and variation in the training set. To this end, we generate multiple sequences from an image, each of which corresponds to a perturbed sample of the image. Each perturbed sample characterizes different parts of the image, and hence, they are slightly different from each other. The use of these perturbed samples for representing the image increases the size and variability of the training set. These samples are modeled with Markov processes which are used to classify unseen image. Working with histopathological tissue images, our experiments demonstrate that the proposed framework is more effective for both larger and smaller training sets compared against other approaches. Additionally, they show that the use of perturbed samples is effective in a voting scheme which boosts the performance of the classifier.Özdemir, ErdemM.S

Multilevel cluster ensembling for histopathological image segmentation

Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2011.Thesis (Master's) -- Bilkent University, 2011.Includes bibliographical references leaves 58-67.In cancer diagnosis and grading, histopathological examination of tissues by pathologists is accepted as the gold standard. However, this procedure has observer variability and leads to subjectivity in diagnosis. In order to overcome such problems, computational methods which use quantitative measures are proposed. These methods extract mathematical features from tissue images assuming they are composed of homogeneous regions and classify images. This assumption is not always true and segmentation of images before classification is necessary. There are methods to segment images but most of them are proposed for generic images and work on the pixel-level. Recently few algorithms incorporated medical background knowledge into segmentation. Their high level feature definitions are very promising. However, in the segmentation step, they use region growing approaches which are not very stable and may lead to local optima. In this thesis, we present an efficient and stable method for the segmentation of histopathological images which produces high quality results. We use existing high level feature definitions to segment tissue images. Our segmentation method significantly improves the segmentation accuracy and stability, compared to existing methods which use the same feature definition. We tackle image segmentation problem as a clustering problem. To improve the quality and the stability of the clustering results, we combine different clustering solutions. This approach is also known as cluster ensembles. We formulate the clustering problem as a graph partitioning problem. In order to obtain diverse and high quality clustering results quickly, we made modifications and improvements on the well-known multilevel graph partitioning scheme. Our method clusters medically meaningful components in tissue images into regions and obtains the final segmentation. Experiments showed that our multilevel cluster ensembling approach performed significantly better than existing segmentation algorithms used for generic and tissue images. Although most of the images used in experiments, contain noise and artifacts, the proposed algorithm produced high quality results.Şimşek, Ahmet ÇağrıM.S

Multiphysical modelling of mechanical behaviour of soft tissue : application to prostate

The aim of this thesis is to propose computational methodologies to analyse how the morphological and microstructural changes in the soft tissues, caused by various pathological conditions, influence the mechanical properties of tissue. More importantly, how such understanding could provide more insights into the mechanical properties of tissue for the purpose of quantitative diagnosis. To achieve this objective, statistical analysis of tissue microstructure based on image processing of tissue histology has been carried out. The influence of such microstructural changes due to different pathological conditions has also been compared to the mechanical properties of the tissue by means of the homogenization approach. To understand better the influence of fluid movement in viscoelastic behaviour of tissue, an optimization based method using numerical homogenization that is integrated with fluid-structure interaction (FSI) modelling is presented. The microstructures of soft tissue are treated as bi-phasic materials, solid material representing the cells and extracellular materials and fluid phase for the interstitial fluid. Such proposed method would be beneficial for quantitative assessment of mechanical properties of soft tissue, as well as understanding the role of multiscale microstructural features of soft tissues in its functionality. It is envisaged that this work will pave the road towards more precise characterization of mechanical properties of soft tissue which can be implemented to non-invasive diagnostic techniques, in order to improve the effectiveness of a range of diagnostic methods such as palpation for primary prostate diagnosis and, more importantly, the life quality of patients

Topological analysis of the tumour microenvironment to study Neuroblastoma

Solid tumours and their tumour microenvironment (TME) can be considered as complex networks whose elements are in constant physical stress. All the elements of the TME, including tumour cells, stromal cells, immune and stem cells, blood/lymphatic vessels, nerve fibers and extracellular matrix components, belong to a highly balanced compressiontension molecular and cellular structure. Through mechanical signals, each element could affect its surroundings modulating tumour growth and migration. The analysis of these complex interactions and the understanding of the structural organization of a tumour requires the collaboration of different disciplines. In this thesis, we focus on a particular solid tumour: Neuroblastoma, a rare type of cancer, originated during the embryo development. We apply computational and mathematical tools to analyse the topology of vitronectin, a glycoprotein of the extracellular matrix, in neuroblastoma tumours. Vitronectin has a particular interest in tumour biology where it is associated with cell migration, angiogenesis, and matrix degradation. Still, its role in Neuroblastoma is not clear. Here, we study the organization of vitronectin within the TME considering Neuroblastoma patient prognosis and tumoral aggressiveness. Combing graph theory and image analysis, we characterize histopathological images taken, from a human sample, by analysing different topological features that capture the organizational cues of vitronectin. By means of statistical analyses, we find that two topological features (Euler number and branching), related to the organization of the existing vitronectin within and surrounding the cells (territorial), correlates with risk pre-stratification group and genetic instability criterion. We interpret that a large amount of recently synthesized VN would create tracks to aid malignant neuroblasts to invade other organs, pinpointed by both topological features, which in turn would change, dramatically, the constitution and mechanics of the extracellular matrix, increasing tumour aggressiveness and worsen patient outcomes. Further studies will be required to assess the true potential of vitronectin as a future therapeutic target of neuroblastoma.Los tumores sólidos y su microambiente tumoral (TME) pueden ser vistos como redes complejas cuyos elementos están en constante estrés físico. Todos los elementos del TME, incluidas células tumorales, células del estroma, células inmunes y células troncales, vasos sanguíneos o linfáticos, fibras nerviosas y componentes de la matriz extracelular, pertenecen a una maquinaria molecular y celular de tensión-compresión altamente equilibrada. A través de señales mecánicas, cada elemento podría afectar su entorno modulando el crecimiento tumoral y la migración. El análisis de estas interacciones complejas y la comprensión de la organización estructural de un tumor requiere la colaboración de diferentes disciplinas. En esta tesis, nos centramos en un tumor sólido particular: el neuroblastoma, un cáncer considerado como ‘raro’, que se origina durante el desarrollo del embrión. Aplicando herramientas computacionales y matemáticas, analizamos la topología de la vitronectina, una glicoproteína de la matriz extracelular, en tumores de neuroblastoma. La vitronectina tiene un interés particular en la biología tumoral, ya que está asociada con migración celular, angiogénesis y degradación de la propia matriz. Aún así, su papel en el neuroblastoma no está claro. En este trabajo, estudiamos la organización de la vitronectina dentro del microambiente tumoral, considerando el pronóstico del paciente con neuroblastoma y su agresividad tumoral. Combinando la teoría de gráficos y el análisis de imagen, caracterizamos las imágenes histopatológicas tomadas de una muestra humana, mediante el análisis de diferentes características topológicas que capturan la organización de la vitronectina. Mediante análisis estadísticos, encontramos que dos características topológicas (número de Euler y ‘ramificación’), relacionadas con la organización de la vitronectina existente dentro y alrededor de las células (territorial), se correlacionan con el grupo de pre-estratificación de riesgo y la inestabilidad genética del paciente. En consecuencia, interpretamos que una gran cantidad de VN, sintetizada recientemente, crearía una especia de ‘caminos’ para ayudar a los neuroblastos malignos a invadir otros órganos, que a su vez cambiarían dramáticamente la constitución y la mecánica de la matriz extracelular, aumentando la agresividad del tumor y empeorando el pronóstico del paciente. Futuros estudios serán requeridos para evaluar el verdadero potencial de la vitronectina como una diana terapéutica del neuroblastoma a largo plazo