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

Data-driven Representation Learning from Histopathology Image Databases to Support Digital Pathology Analysis

Cancer research is a major public health priority in the world due to its high incidence, diversity and mortality. Despite great advances in this area during recent decades, the high incidence and lack of specialists have proven that one of the major challenges is to achieve early diagnosis. Improved early diagnosis, especially in developing countries, plays a crucial role in timely treatment and patient survival. Recent advances in scanner technology for the digitization of pathology slides and the growth of global initiatives to build databases for cancer research have enabled the emergence of digital pathology as a new approach to support pathology workflows. This has led to the development of many computational methods for automatic histopathology image analysis, which in turn has raised new computational challenges due to the high visual variability of histopathology slides, the difficulty in assessing the effectiveness of methods (considering the lack of annotated data from different pathologists and institutions), and the need of interpretable, efficient and feasible methods for practical use. On the other hand, machine learning techniques have focused on exploiting large databases to automatically extract and induce information and knowledge, in the form of patterns and rules, that allow to connect low-level content with its high-level meaning. Several approaches have emerged as opposed to traditional schemes based on handcrafted features for data representation, which nowadays are known as representation learning. The objective of this thesis is the exploration, development and validation of precise, interpretable and efficient computational machine learning methods for automatic representation learning from histopathology image databases to support diagnosis tasks of different types of cancer. The validation of the proposed methods during the thesis development allowed to corroborate their capability in several histopathology image analysis tasks of different types of cancer. These methods achieve good results in terms of accuracy, robustness, reproducibility, interpretability and feasibility suggesting their potential practical application towards translational and personalized medicine.Resumen. La investigación en cáncer es una de las principales prioridades de salud pública en el mundo debido a su alta incidencia, diversidad y mortalidad. A pesar de los grandes avances en el área en las últimas décadas, la alta incidencia y la falta de especialistas ha llevado a que una de las principales problemáticas sea lograr su detección temprana, en especial en países en vías de desarrollo, como quiera a que de ello depende las posibilidades de un tratamiento oportuno y las oportunidades de supervivencia de los pacientes. Los recientes avances en tecnología de escáneres para digitalización de láminas de patología y el crecimiento de iniciativas mundiales para la construcción de bases de datos para la investigación en cáncer, han permitido el surgimiento de la patología digital como un nuevo enfoque para soportar los flujos de trabajo en patología. Esto ha llevado al desarrollo de una gran variedad de métodos computacionales para el análisis automático de imágenes de histopatología, lo cual ha planteado nuevos desafíos computacionales debido a la alta variabilidad visual de las láminas de histopatología; la dificultad para evaluar la efectividad de los métodos por la falta de datos de diferentes instituciones que cuenten con anotaciones por parte de los patólogos, y la necesidad de métodos interpretables, eficientes y factibles para su uso práctico. Por otro lado, el aprendizaje de máquina se ha enfocado en explotar las grandes bases de datos para extraer e inducir de manera automática información y conocimiento, en forma de patrones y reglas, que permita conectar el contenido de bajo nivel con su significado. Diferentes técnicas han surgido en contraposición a los esquemas tradicionales basados en diseño manual de la representación de los datos, en lo que se conoce como aprendizaje de la representación. El propósito de esta tesis fue la exploración, desarrollo y validación de métodos computacionales de aprendizaje de máquina precisos, interpretables y eficientes a partir de bases de datos de imágenes de histopatología para el aprendizaje automático de la representación en tareas de apoyo al diagnóstico de distintos tipos de cáncer. La validación de los distintos métodos propuestos durante el desarrollo de la tesis permitieron corroborar la capacidad de cada uno de ellos en distintivas tareas de análisis de imágenes de histopatología, en diferentes tipos de cáncer, con buenos resultados en términos de exactitud, robustez, reproducibilidad, interpretabilidad y factibilidad, lo cual sugiere su potencial aplicación práctica hacia la medicina traslacional y personalizada.Doctorad

Anwendung Massenspektrometrie basierter Technologie zur Entdeckung räumlicher Peptidsignaturen in der Krebsforschung

Cancer is one of the leading causes of death worldwide, within the molecular and structure complexity of tumors are causal factors for disease progression and treatment standards. With the development of molecular biological techniques, physicians could use genetic variation or protein and metabolic expression profile besides histo-morphologicial evaluation to classify more accurate risk assessment and to guide treatment decisions. The biomarker-driven personalized therapies might improve clinical care, avoid unnecessary treatments and reduce the duration and costs for hospital stay. Therefore, there is a strong demand for more reliable molecular biomarker profiles. In this dissertation, a novel technique called imaging mass spectrometry (MADLI-MSI) is used to investigate the potential of spatially resolved peptide signatures (directly from tumor tissue; in situ) for (i) discrimination of subtypes of serous ovarian cancer (HGSOC) and (ii) risk assessment of neuroblastoma. Univariate and multivariate static methods were used to determine associated peptide signatures. Using complementary methods, liquid chromatography-based mass spectrometry the corresponding proteins to the peptides were identified and verified by immunohistology. Consequently, peptide signatures were identified to predict disease recurrence in early-stage HGSOC patients and to distinguish high-risk neuroblastoma patients from other risk groups. These results suggest that the MALDI-MSI technique is a promising analytical method that facilitates diagnosis and treatment decision-making. It has also provided new biological insights into tumor heterogeneity, that could benefit the development of molecular biomarker profiles. The data of this dissertation have been really published in Journal “Cancers (MDPI)” 2020 and 2021.Onkologische Erkrankungen (Krebs) sind weltweit eine der häufigsten Todesursachen. Die molekulare und strukturelle Komplexität von Tumoren sind ursächlich für die Krankheitsprogression und Therapieanspruch. Mit der Entwicklung von neuen molekularbiologischen Verfahren könnten Ärzte neben der histo-morphologischen Bewertung auch genetische Variationen oder Protein- und Metabolit-Expressionsprofile nutzen, um eine genauere Risikobewertung vorzunehmen und die Behandlungsentscheidung zu treffen. Die personalisierten Therapien können die klinische Versorgung verbessern durch Vermeidung unnötiger Behandlungen und verringerte Dauer und Kosten des Krankenhausaufenthalts. Daher besteht ein starker Bedarf an zuverlässigeren molekularen Biomarker Profilen. In dieser Dissertation wird ein neuartiges Verfahren, die sogenannten bildgebenden Massenspektrometrie (MADLI-MSI) eingesetzte um das Potential von räumlich aufgelösten Peptide-Signaturen (direkt aus dem Tumorgewebe; in situ) für (i) die Diskriminierung von Subtypen des serösen Ovarialkarzinom (HGSOC) zu untersuchen und (ii) die Risikoabschätzung des Neuroblastomes. Dabei wurden univariate und multivariate statischer Verfahren eingesetzt, um assoziierten Peptide- Signaturen zu bestimmen. Mittels komplementärer Verfahren, Flüssigkeitschromatographie basierte Massenspektrometrie wurden die korrespondierenden Proteine zu den Peptiden identifiziert und Immunhistologisch verifiziert. Folglich wurden Peptidsignaturen zur Vorhersage des Wiederauftretens der Krankheit bei HGSOC-Patienten im Frühstadium und zur Unterscheidung von Hochrisiko-Neuroblastom Patienten von anderen Risikogruppen identifiziert. Diese Ergebnisse deuten darauf hin, dass die MALDI-MSI-Technik eine vielversprechende Analysemethode ist, die die Diagnose und die Entscheidung über die Behandlung erleichtert. Außerdem hat sie neue biologische Erkenntnisse über die Heterogenität des Tumors geliefert, die der Entwicklung von molekularen Biomarker-Profilen zu Gute kommen könnten. Die Daten dieser Dissertation wurden in der Zeitschrift „Cancers (MDPI)" 2020 und 2021 veröffentlicht

Added benefits of computer-assisted analysis of Hematoxylin-Eosin stained breast histopathological digital slides

This thesis aims at determining if computer-assisted analysis can be used to better understand pathologists’ perception of mitotic figures on Hematoxylin-Eosin (HE) stained breast histopathological digital slides. It also explores the feasibility of reproducible histologic nuclear atypia scoring by incorporating computer-assisted analysis to cytological scores given by a pathologist. In addition, this thesis investigates the possibility of computer-assisted diagnosis for categorizing HE breast images into different subtypes of cancer or benign masses. In the first study, a data set of 453 mitoses and 265 miscounted non-mitoses within breast cancer digital slides were considered. Different features were extracted from the objects in different channels of eight colour spaces. The findings from the first research study suggested that computer-aided image analysis can provide a better understanding of image-related features related to discrepancies among pathologists in recognition of mitoses. Two tasks done routinely by the pathologists are making diagnosis and grading the breast cancer. In the second study, a new tool for reproducible nuclear atypia scoring in breast cancer histological images was proposed. The third study proposed and tested MuDeRN (MUlti-category classification of breast histopathological image using DEep Residual Networks), which is a framework for classifying hematoxylin-eosin stained breast digital slides either as benign or cancer, and then categorizing cancer and benign cases into four different subtypes each. The studies indicated that computer-assisted analysis can aid in both nuclear grading (COMPASS) and breast cancer diagnosis (MuDeRN). The results could be used to improve current status of breast cancer prognosis estimation through reducing the inter-pathologist disagreement in counting mitotic figures and reproducible nuclear grading. It can also improve providing a second opinion to the pathologist for making a diagnosis

Histopathological image classification using salient point patterns

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 69-79.Over the last decade, computer aided diagnosis (CAD) systems have gained great importance to help pathologists improve the interpretation of histopathological tissue images for cancer detection. These systems offer valuable opportunities to reduce and eliminate the inter- and intra-observer variations in diagnosis, which is very common in the current practice of histopathological examination. Many studies have been dedicated to develop such systems for cancer diagnosis and grading, especially based on textural and structural tissue image analysis. Although the recent textural and structural approaches yield promising results for different types of tissues, they are still unable to make use of the potential biological information carried by different tissue components. However, these tissue components help better represent a tissue, and hence, they help better quantify the tissue changes caused by cancer. This thesis introduces a new textural approach, called Salient Point Patterns (SPP), for the utilization of tissue components in order to represent colon biopsy images. This textural approach first defines a set of salient points that correspond to nuclear, stromal, and luminal components of a colon tissue. Then, it extracts some features around these salient points to quantify the images. Finally, it classifies the tissue samples by using the extracted features. Working with 3236 colon biopsy samples that are taken from 258 different patients, our experiments demonstrate that Salient Point Patterns approach improves the classification accuracy, compared to its counterparts, which do not make use of tissue components in defining their texture descriptors. These experiments also show that different set of features can be used within the SPP approach for better representation of a tissue image.Çığır, CelalM.S

Signal and image processing methods for imaging mass spectrometry data

Imaging mass spectrometry (IMS) has evolved as an analytical tool for many biomedical applications. This thesis focuses on algorithms for the analysis of IMS data produced by matrix assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometer. IMS provides mass spectra acquired at a grid of spatial points that can be represented as hyperspectral data or a so-called datacube. Analysis of this large and complex data requires efficient computational methods for matrix factorization and for spatial segmentation. In this thesis, state of the art processing methods are reviewed, compared and improved versions are proposed. Mathematical models for peak shapes are reviewed and evaluated. A simulation model for MALDI-TOF is studied, expanded and developed into a simulator for 2D or 3D MALDI-TOF-IMS data. The simulation approach paves way to statistical evaluation of algorithms for analysis of IMS data by providing a gold standard dataset. [...

Development of a complete advanced computational workflow for high-resolution LDI-MS metabolomics imaging data processing and visualization

La imatge per espectrometria de masses (MSI) mapeja la distribució espacial de les molècules en una mostra. Això permet extreure informació Metabolòmica espacialment corralada d'una secció de teixit. MSI no s'usa àmpliament en la metabolòmica espacial a causa de diverses limitacions relacionades amb les matrius MALDI, incloent la generació d'ions que interfereixen en el rang de masses més baix i la difusió lateral dels compostos. Hem desenvolupat un flux de treball que millora l'adquisició de metabòlits en un instrument MALDI utilitzant un "sputtering" per dipositar una nano-capa d'Au directament sobre el teixit. Això minimitza la interferència dels senyals del "background" alhora que permet resolucions espacials molt altes. S'ha desenvolupat un paquet R per a la visualització d'imatges i processament de les dades MSI, tot això mitjançant una implementació optimitzada per a la gestió de la memòria i la programació concurrent. A més, el programari desenvolupat inclou també un algoritme per a l'alineament de masses que millora la precisió de massa.La imagen por espectrometría de masas (MSI) mapea la distribución espacial de las moléculas en una muestra. Esto permite extraer información metabolòmica espacialmente corralada de una sección de tejido. MSI no se usa ampliamente en la metabolòmica espacial debido a varias limitaciones relacionadas con las matrices MALDI, incluyendo la generación de iones que interfieren en el rango de masas más bajo y la difusión lateral de los compuestos. Hemos desarrollado un flujo de trabajo que mejora la adquisición de metabolitos en un instrumento MALDI utilizando un “sputtering” para depositar una nano-capa de Au directamente sobre el tejido. Esto minimiza la interferencia de las señales del “background” a la vez que permite resoluciones espaciales muy altas. Se ha desarrollado un paquete R para la visualización de imágenes y procesado de los datos MSI, todo ello mediante una implementación optimizada para la gestión de la memoria y la programación concurrente. Además, el software desarrollado incluye también un algoritmo para el alineamiento de masas que mejora la precisión de masa.Mass spectrometry imaging (MSI) maps the spatial distributions of molecules in a sample. This allows extracting spatially-correlated metabolomics information from tissue sections. MSI is not widely used in spatial metabolomics due to several limitations related with MALDI matrices, including the generation of interfering ions and in the low mass range and the lateral compound delocalization. We developed a workflow to improve the acquisition of metabolites using a MALDI instrument. We sputter an Au nano-layer directly onto the tissue section enabling the acquisition of metabolites with minimal interference of background signals and ultra-high spatial resolution. We developed an R package for image visualization and MSI data processing, which is optimized to manage datasets larger than computer’s memory using a mutli-threaded implementation. Moreover, our software includes a label-free mass alignment algorithm for mass accuracy enhancement