ESTUDO COMPARATIVO DE MODELOS DE DETECÇÃO DE OBJETOS PARA DETECÇÃO DE GLOMÉRULOS

Uma doença renal pode afetar a capacidade do corpo humano de manter o equilíbrio dos componentes presentes do sangue, o que a circulação de impurezas, além de afetar os componentes funcionais do sangue, como glóbulos vermelhos, glóbulos brancos e proteínas. Os glomérulos são uma microestrutura de grande importância para o rim. Essa microestrutura é formada por capilares que formam uma espécie de novelo de lã encontrada no néfron e é a unidade funcional do rim [1]

Faster R-CNN-Based Glomerular Detection in Multistained Human Whole Slide Images

The detection of objects of interest in high-resolution digital pathological images is a key part of diagnosis and is a labor-intensive task for pathologists. In this paper, we describe a Faster R-CNN-based approach for the detection of glomeruli in multistained whole slide images (WSIs) of human renal tissue sections. Faster R-CNN is a state-of-the-art general object detection method based on a convolutional neural network, which simultaneously proposes object bounds and objectness scores at each point in an image. The method takes an image obtained from a WSI with a sliding window and classifies and localizes every glomerulus in the image by drawing the bounding boxes. We configured Faster R-CNN with a pretrained Inception-ResNet model and retrained it to be adapted to our task, then evaluated it based on a large dataset consisting of more than 33,000 annotated glomeruli obtained from 800 WSIs. The results showed the approach produces comparable or higher than average F-measures with different stains compared to other recently published approaches. This approach could have practical application in hospitals and laboratories for the quantitative analysis of glomeruli in WSIs and, potentially, lead to a better understanding of chronic glomerulonephritis

Faster R-CNN-Based Glomerular Detection in Multistained Human Whole Slide Images

The detection of objects of interest in high-resolution digital pathological images is a key part of diagnosis and is a labor-intensive task for pathologists. In this paper, we describe a Faster R-CNN-based approach for the detection of glomeruli in multistained whole slide images (WSIs) of human renal tissue sections. Faster R-CNN is a state-of-the-art general object detection method based on a convolutional neural network, which simultaneously proposes object bounds and objectness scores at each point in an image. The method takes an image obtained from a WSI with a sliding window and classifies and localizes every glomerulus in the image by drawing the bounding boxes. We configured Faster R-CNN with a pretrained Inception-ResNet model and retrained it to be adapted to our task, then evaluated it based on a large dataset consisting of more than 33,000 annotated glomeruli obtained from 800 WSIs. The results showed the approach produces comparable or higher than average F-measures with different stains compared to other recently published approaches. This approach could have practical application in hospitals and laboratories for the quantitative analysis of glomeruli in WSIs and, potentially, lead to a better understanding of chronic glomerulonephritis

Object Detection in medical imaging

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Information and Decision SystemsArtificial Intelligence, assisted by deep learning, has emerged in various fields of our society. These systems allow the automation and the improvement of several tasks, even surpassing, in some cases, human capability. Object detection methods are used nowadays in several areas, including medical imaging analysis. However, these methods are susceptible to errors, and there is a lack of a universally accepted method that can be applied across all types of applications with the needed precision in the medical field. Additionally, the application of object detectors in medical imaging analysis has yet to be thoroughly analyzed to achieve a richer understanding of the state of the art. To tackle these shortcomings, we present three studies with distinct goals. First, a quantitative and qualitative analysis of academic research was conducted to gather a perception of which object detectors are employed, the modality of medical imaging used, and the particular body parts under investigation. Secondly, we propose an optimized version of a widely used algorithm to overcome limitations commonly addressed in medical imaging by fine-tuning several hyperparameters. Thirdly, we develop a novel stacking approach to augment the precision of detections on medical imaging analysis. The findings show that despite the late arrival of object detection in medical imaging analysis, the number of publications has increased in recent years, demonstrating the significant potential for growth. Additionally, we establish that it is possible to address some constraints on the data through an exhaustive optimization of the algorithm. Finally, our last study highlights that there is still room for improvement in these advanced techniques, using, as an example, stacking approaches. The contributions of this dissertation are several, as it puts forward a deeper overview of the state-of-the-art applications of object detection algorithms in the medical field and presents strategies for addressing typical constraints in this area.A Inteligência Artificial, auxiliada pelo deep learning, tem emergido em diversas áreas da nossa sociedade. Estes sistemas permitem a automatização e a melhoria de diversas tarefas, superando mesmo, em alguns casos, a capacidade humana. Os métodos de detecção de objetos são utilizados atualmente em diversas áreas, inclusive na análise de imagens médicas. No entanto, esses métodos são suscetíveis a erros e falta um método universalmente aceite que possa ser aplicado em todos os tipos de aplicações com a precisão necessária na área médica. Além disso, a aplicação de detectores de objetos na análise de imagens médicas ainda precisa ser analisada minuciosamente para alcançar uma compreensão mais rica do estado da arte. Para enfrentar essas limitações, apresentamos três estudos com objetivos distintos. Inicialmente, uma análise quantitativa e qualitativa da pesquisa acadêmica foi realizada para obter uma percepção de quais detectores de objetos são empregues, a modalidade de imagem médica usada e as partes específicas do corpo sob investigação. Num segundo estudo, propomos uma versão otimizada de um algoritmo amplamente utilizado para superar limitações comumente abordadas em imagens médicas por meio do ajuste fino de vários hiperparâmetros. Em terceiro lugar, desenvolvemos uma nova abordagem de stacking para aumentar a precisão das detecções na análise de imagens médicas. Os resultados demostram que, apesar da chegada tardia da detecção de objetos na análise de imagens médicas, o número de publicações aumentou nos últimos anos, evidenciando o significativo potencial de crescimento. Adicionalmente, estabelecemos que é possível resolver algumas restrições nos dados por meio de uma otimização exaustiva do algoritmo. Finalmente, o nosso último estudo destaca que ainda há espaço para melhorias nessas técnicas avançadas, usando, como exemplo, abordagens de stacking. As contribuições desta dissertação são várias, apresentando uma visão geral em maior detalhe das aplicações de ponta dos algoritmos de detecção de objetos na área médica e apresenta estratégias para lidar com restrições típicas nesta área

Reconocimiento y clasificación automatizada de especies de polen alergénicas

El presente trabajo de tesis doctoral está centrado en la detección de granos de polen en imágenes palinológicas tomadas de muestras estándar utilizando técnicas de aprendizaje profundo. La localización y clasificación de granos de polen es una tarea manual, muy laboriosa, que llevan a cabo palinólogos experimentados para estimar las concentraciones de los tipos de polen atmosférico presentes en distintas áreas geográficas. Este proceso se realiza a partir de muestras obtenidas en captadores de partículas aerobiológicas que, tras un procesamiento, deben visualizarse con un microscopio óptico. La estimación de los distintos tipos de polen resulta de gran utilidad en varios campos de la ciencia como en alergología, agricultura, ciencias forenses o paleopalinología. Desde el año 2012 el campo de la inteligencia artificial ha experimentado un desarrollo muy importante en detección de objetos en imágenes, gracias al exitoso desarrollo de técnicas basadas en redes neuronales convolucionales. Parte del éxito logrado se ha debido a la aparición en el mercado de unidades de procesamiento gráfico con grandes capacidades de cálculo paralelo, pero también resultó importante la recopilación de grandes conjuntos de imágenes clasificadas. Esta tesis doctoral tiene por objetivo principal evaluar la idoneidad de un método basado en redes neuronales convolucionales, que permita realizar la localización y detección de granos de varios tipos de polen de forma robusta. Consideramos éste un primer paso para desarrollar un sistema que pudiese servir de ayuda en un laboratorio de palinología.This doctoral thesis is focused on the detection of pollen grains in palynological images from standard samples, using deep learning techniques. The localization and classification of pollen grains is a very laborious manual task, which is carried out by experienced palynologists to estimate the concentrations of the different types of atmospheric pollen present in given geographic areas. This process is performed on the basis of samples obtained in aerobiological particle collectors, which after processing, must be visualized with an optical microscope. The estimation of the different pollen types is very useful in several areas of science such as allergology, agriculture, forensic science or paleopalinology. Since 2012, the field of artificial intelligence has achieved a very important progress in image object detection, thanks to the successful development of techniques based on convolutional neural networks. Part of the success achieved has been due to the appearance on the market of graphics processing units with large parallel computing capabilities, but the collection of large sets of classified images was also important. The main objective of this doctoral thesis is to evaluate the suitability of a method based on convolutional neural networks for the localization and detection of grains of pollen of various types in a robust way. This method is considered a first step in the development of a system that could be helpful in a palinology laboratory