Cuantificación de glándulas en imágenes histopatológicas de cáncer gástrico

Automatic detection and quantification of glands in gastric cancer may contribute to objectively measure the lesion severity, to develop strategies for early diagnosis, and most importantly to improve the patient categorization; however, gland quantification is a highly subjective task, prone to error due to the high biopsy traffic and the experience of each expert. The present master’s dissertation is composed by three chapters that carry to an objective identification of glands. In the first chapter of this document we present a new approach for segmentation of glandular nuclei based on nuclear local and contextual (neighborhood) information “NLCI”. A Gradient-BoostedRegression-Tree classifier is trained to distinguish between glandular nuclei and non glandular nuclei. Validation was carried out using 45.702 annotated nuclei from 90 fields of view (patches) extracted from whole slide images of patients diagnosed with gastric cancer. NLCI achieved an accuracy of 0.977 and an F-measure of 0.955, while R-CNN yielded corresponding accuracy and F-measures of 0.923 and 0.719, respectively. In second chapter we presents an entire framework for automatic detection of glands in gastric cancer images. By selecting gland candidates from a binarized version of the hematoxylin channel. Next, the gland’s shape and nuclei are characterized using local features which feed a Random-Cross-validation method classifier trained previously with images manually annotated by an expert. Validation was carried out using a data-set with 1.330 from seven fields of view extracted from patients diagnosed with gastric cancer whole slide images. Results showed an accuracy of 93 % using a linear classifier. Finally, in the third chapter analyzing gland and their glandular nuclei most relevant features, since predict if a patient will survive more than a year after being diagnosed with gastric cancer. A feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy “mRMR” approach selects those features that correlated better with patient survival. A data set with 668 Fields of View (FoV), 2.076 glandular structures from 14 whole slide images were extracted from patient diagnosed with gastric cancer. Results showed an accuracy of 78.57 % using a QDA Linear & Quadratic Discriminant Analysis was training with Leave-one-out e.g training with thirteen cases and leaving a separate case to validate.La detección y cuantificación automática de las glándulas en el cáncer gástrico puede contribuir a medir objetivamente la gravedad de la lesión, desarrollar estrategias para el diagnóstico precoz y lo que es más importante, mejorar la categorización del paciente; sin embargo, su cuantificación es una tarea altamente subjetiva, propensa a errores debido al alto tráfico de biopsias y a la experiencia de cada experto. La presente disertación de maestría está compuesta por tres capítulos los cuales llevan a la cuantificación objetiva de glándulas. En el primer capítulo del documento se presenta un nuevo enfoque para la segmentación de los núcleos glandulares en base a la información nuclear local y contextual (vecindario). Se entrenó un Gradient-Boosted-Regression-Tree para distinguir entre núcleos glandulares y núcleos no glandulares. La validación se llevó con 45.702 núcleos anotados manualmente de 90 campos de visión (parches) extraídos de imágenes de biopsias completas de pacientes diagnosticados con cáncer gástrico. NLCI logró una precisión de 0.977% y un F-Score de 0.955%, mientras que fast R-CNN arrojó una precisión de 0.923% y un F-Score y 0.719%. En el segundo capítulo se presenta un marco completo para detección automática de glándulas en imágenes de cáncer gástrico. Las glándulas candidatas se seleccionan de una versión binarizada del canal de hematoxilina. A continuación, la forma y los núcleos de las glándulas se caracterizan y se alimenta un clasificador Random Cross Validation, entrenado previamente con imágenes anotadas manualmente por un experto. La validación se realizó en un conjunto de datos con 1.330 parches extraídos de siete biopsias de pacientes diagnosticados con cáncer gástrico. Los resultados mostraron una precisión del 93% utilizando un clasificador lineal. Finalmente, el tercer capítulo analiza las características más relevantes de las glándulas y sus núcleos glandulares, para predecir la sobrevida a un año de un paciente diagnosticado con cáncer gástrico. Una selección de características basada en información mutua: criterios de dependencia máxima, máxima relevancia y mínima redundancia (mRMR) escogen las características correlacionadas con la supervivencia del paciente. Se extrajo un conjunto de datos con 668 campos de visión (FoV), 2.076 estructuras glandulares de 14 imágenes completas de pacientes diagnosticados con cáncer gástrico. Los resultados mostraron una precisión del 76.3% usando un Análisis Discriminante Lineal y Cuadrático (QDA) y un esquema de evaluación entrenando con trece casos y dejando un caso aparte para validar.Magíster en Ingeniería Biomédica. Línea de investigación: Procesamiento de señale

Artificial Intelligence for Digital and Computational Pathology

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology. This field holds tremendous potential to automate clinical diagnosis, predict patient prognosis and response to therapy, and discover new morphological biomarkers from tissue images. Some of these artificial intelligence-based systems are now getting approved to assist clinical diagnosis; however, technical barriers remain for their widespread clinical adoption and integration as a research tool. This Review consolidates recent methodological advances in computational pathology for predicting clinical end points in whole-slide images and highlights how these developments enable the automation of clinical practice and the discovery of new biomarkers. We then provide future perspectives as the field expands into a broader range of clinical and research tasks with increasingly diverse modalities of clinical data

The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis

Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. The variety of image analysis tasks in the context of deep learning includes classification (e.g., healthy vs. cancerous tissue), detection (e.g., lymphocytes and mitosis counting), and segmentation (e.g., nuclei and glands segmentation). The majority of recent machine learning methods in digital pathology have a pre- and/or post-processing stage which is integrated with a deep neural network. These stages, based on traditional image processing methods, are employed to make the subsequent classification, detection, or segmentation problem easier to solve. Several studies have shown how the integration of pre- and post-processing methods within a deep learning pipeline can further increase the model's performance when compared to the network by itself. The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input (pre-processing) or to improve the results of the network output (post-processing), focusing on digital pathology image analysis. Many of the techniques presented here, especially the post-processing methods, are not limited to digital pathology but can be extended to almost any image analysis field

The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis.

Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. The variety of image analysis tasks in the context of deep learning includes classification (e.g., healthy vs. cancerous tissue), detection (e.g., lymphocytes and mitosis counting), and segmentation (e.g., nuclei and glands segmentation). The majority of recent machine learning methods in digital pathology have a pre- and/or post-processing stage which is integrated with a deep neural network. These stages, based on traditional image processing methods, are employed to make the subsequent classification, detection, or segmentation problem easier to solve. Several studies have shown how the integration of pre- and post-processing methods within a deep learning pipeline can further increase the model's performance when compared to the network by itself. The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input (pre-processing) or to improve the results of the network output (post-processing), focusing on digital pathology image analysis. Many of the techniques presented here, especially the post-processing methods, are not limited to digital pathology but can be extended to almost any image analysis field

A survey on artificial intelligence in histopathology image analysis

The increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed the use of computer systems in histopathology analysis. Extensive research in Artificial Intelligence (AI) with a huge progress has been conducted resulting in efficient, effective, and robust algorithms for several applications including cancer diagnosis, prognosis, and treatment. These algorithms offer highly accurate predictions but lack transparency, understandability, and actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand the mechanism behind the decisions made by AI methods and increase user trust but also to broaden the use of AI algorithms in the clinical setting. From the survey of over 150 papers, we explore different AI algorithms that have been applied and contributed to the histopathology image analysis workflow. We first address the workflow of the histopathological process. We present an overview of various learning-based, XAI, and actionable techniques relevant to deep learning methods in histopathological imaging. We also address the evaluation of XAI methods and the need to ensure their reliability on the field

Computational Pathology: A Survey Review and The Way Forward

Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath (https://github.com/AtlasAnalyticsLab/CPath_Survey).Comment: Accepted in Elsevier Journal of Pathology Informatics (JPI) 202

Domain Generalization in Computational Pathology: Survey and Guidelines

Deep learning models have exhibited exceptional effectiveness in Computational Pathology (CPath) by tackling intricate tasks across an array of histology image analysis applications. Nevertheless, the presence of out-of-distribution data (stemming from a multitude of sources such as disparate imaging devices and diverse tissue preparation methods) can cause \emph{domain shift} (DS). DS decreases the generalization of trained models to unseen datasets with slightly different data distributions, prompting the need for innovative \emph{domain generalization} (DG) solutions. Recognizing the potential of DG methods to significantly influence diagnostic and prognostic models in cancer studies and clinical practice, we present this survey along with guidelines on achieving DG in CPath. We rigorously define various DS types, systematically review and categorize existing DG approaches and resources in CPath, and provide insights into their advantages, limitations, and applicability. We also conduct thorough benchmarking experiments with 28 cutting-edge DG algorithms to address a complex DG problem. Our findings suggest that careful experiment design and CPath-specific Stain Augmentation technique can be very effective. However, there is no one-size-fits-all solution for DG in CPath. Therefore, we establish clear guidelines for detecting and managing DS depending on different scenarios. While most of the concepts, guidelines, and recommendations are given for applications in CPath, we believe that they are applicable to most medical image analysis tasks as well.Comment: Extended Versio