<div><div><div><p>Determining the locations of nuclei in Zebrafish embryos is crucial for the study of the spatio-temporal behavior of these cells during the development process. With image seg- mentations, not only the location of the cell can be known, but also determine if each pixels is background or part of a nucleus. Traditional image processing techniques have been thor- oughly applied to this problem. These techniques suffer from bad generalization, many times relying on heuristic that apply to a specific type of image to reach a high accuracy when doing pixel by pixel segmentation. In previous work from our research lab, wavelet image segmentation was applied, but heuristics relied on expected nuclei size .</p><p>Machine learning techniques, and more specifically convolutional neural networks, have recently revolutionized image processing and computer vision in general. By relying on vast amounts of data and deep networks, problems in computer vision such as classification or semantic segmentation have reached new state of the art performance, and these techniques are continuously improving and pushing the boundaries of state of the art.</p><p>The lack of labeled data to as input to a machine learning model was the main bottleneck. To overcome this, this work utilized Amazon Turk platform. This platform allows users to create a task and give instructions to ‘Workers‘ , which agree to a price to complete each task. The data was preprocessed before being presented to the workers, and revised to make sure it was properly labeled.</p><p>Once labeled data was ready, the images and its corresponding segmented labels were used to train a U-Net model. In a nutshell, this models takes the input image, and at different scales, maps the image to a smaller vector. From this smaller vector, the model , again at different scales, constructs an image from this vector. During model training, the weights of the model are updated so that the image that is reconstructed minimizes the difference between the label image and the pixel segmentation.</p><p>We show that this method not only fits better the labeled ground truth image by the workers, but also generalizes well to other images of Zebrafish embryos. Once the model is trained, inference to obtain the segmented image is also orders of magnitude faster than previous techniques, including our previous wavelet segmentation method.</p></div></div></div>

Given the strong association between aberrant nuclear morphology and tumor progression, changes in nuclear structure have remained the gold standard for cancer diagnosis for over 150 years. Recently, the rapid development of imaging hardware and computation power creates the opportunity for automated computer-aided diagnosis (CAD). Developing a robust and reliable pattern recognition pipeline is a pressing need to mine and analyze tons of nuclei data being captured. Among the rich studies on pattern recognition problems in pathology, automated nuclei detection, segmentation and cancer detection are the recurring tasks due to the importance and challenges of nuclei analysis. In this thesis, we propose and investigate the state-of-art methods in the CAD modules for maximizing the overall amount of information from images for decision making. We focus on nuclei segmentation and patient cancer detection in the nuclei image analysis pipeline. As the first step in nuclei analysis, we develop an unsupervised nuclei detection and segmentation approach for pathology images. Different from many supervised segmentation methods whose performances rely on the quality and quantity of training samples, the proposed method is able to automatically search for the nucleus contour by solving the shortest path problem with little user effort. We consider the cancer detection task as a set classification problem and propose a highly discriminative predictive model in the sense that it not only optimizes the classifier decision boundary but also transfers discriminative information to set representation learning. The innovation of the model is the integration of set representation learning and classifier training into one objective function for boosting the cancer detection performance. Experimental results showed that the new model provides significant improvements compared with state-of-art methods in the diagnostic challenges. In addition, we showed that the predictive model enables visual interpretation of discriminative nuclear characteristics representing the whole nuclei set. We believe the proposed model is quite general and provide experimental validations in several extended pattern recognition problems.

<p>Microscopy image analysis can provide substantial information for clinical study and understanding of the biological structure. Two-photon microscopy is a type of fluorescence microscopy that can visualize deep into tissue with near-infrared excitation light. Large 3D image volumes of complex subcellular are often produced, which calls for automatic image analysis techniques. Automatic methods that can obtain nuclei quantity in microscopy image volumes are needed for biomedical research and clinical diagnosis. In general, several challenges exist for counting nuclei in 3D image volumes. These include “crowding” and touching of nuclei, overlapping of two or morenuclei, and shape and size variances of the nuclei. In this thesis, a 3D nuclei counterusing two different generative adversarial networks (GAN) is proposed and evaluated.Synthetic data that resembles real microscopy image is generated with a GAN. The synthetic data is used to train another 3D GAN network that counts the number o fnuclei. Our approach is evaluated with respect to the number of ground truth nuclei and compared with common ways of counting used in the biological research.Fluorescence microscopy 3D image volumes of rat kidneys are used to test our 3D nuclei counter. The evaluation of both networks shows that the proposed technique is successful for counting nuclei in 3D. Then, a 3D segmentation and classification method to segment and identify individual nuclei in fluorescence microscopy volumes without having ground truth volumes is introduced. Three dimensional synthetic data is generated using the Recycle-GAN with the Hausdorff distance loss introduced into preserve the shape of individual nuclei. Realistic microscopy image volumes with nuclei segmentation mask and nucleus boundary ground truth volumes are generated.A subsequent 3D CNN with a regularization term that discourage detection out of nucleus boundary is used to detect and segment nuclei. Nuclei boundary refinement is then performed to enhance nuclei segmentation. Experimental results on our rat kidney dataset show the proposed method is competitive with respect to several state-of-the-art methods. A Distributed and Networked Analysis of Volumetric Image Data(DINAVID) system is developed to enable remote analysis of microscopy images for biologists. There are two main functions integrated in the system, a 3D visualization tool and a remote computing tool for nuclei segmentation. The 3D visualization enables real-time rendering of large volumes of microscopy data. The segmentation tool provides fast inferencing of pre-trained deep learning models trained with 5 different types of microscopy data.<br></p>

Immunohistological (IHC) stained images occupy a fundamental role in the pathologist's diagnosis and monitoring of cancer development. The manual process of monitoring such images is a subjective, time consuming process that typically relies on the visual ability and experience level of the pathologist. A novel and comprehensive system for the automated quantification of antibody inside stained cell nuclei in immunohistochemistry images is proposed and demonstrated in this research. The system is based on a cellular level approach, where each nucleus is individually analyzed to observe the effects of protein antibodies inside the nuclei. The system provides three main quantitative descriptions of stained nuclei. The first quantitative measurement automatically generates the total number of cell nuclei in an image. The second measure classifies the positive and negative stained nuclei based on the nuclei colour, morphological and textural features. Such features are extracted directly from each nucleus to provide discriminative characteristics of different stained nuclei. The output generated from the first and second quantitative measures are used collectively to calculate the percentage of positive nuclei (PS). The third measure proposes a novel automated method for determining the staining intensity level of positive nuclei or what is known as the intensity score (IS). The minor intensity features are observed and used to classify low, intermediate and high stained positive nuclei. Statistical methods were applied throughout the research to validate the system results against the ground truth pathology data. Experimental results demonstrate the effectiveness of the proposed approach and provide high accuracy when compared to the ground truth pathology data.

La gradation de lames de biopsie fournit des informations pronostiques essentielles pour le diagnostic et le traitement. La détection et le comptage manuel des mitoses est un travail fastidieux, sujet à des variations inter-et intra- observateur considérables. L'objectif principal de cette thèse de doctorat est le développement d'un système capable de fournir une détection des mitoses sur des images provenant de différents types de scanners rapides automatiques, ainsi que d'un microscope multispectral. L'évaluation des différents systèmes proposés est effectuée dans le cadre du projet MICO (MIcroscopie COgnitive, projet ANR TecSan piloté par notre équipe). Dans ce contexte, les systèmes proposés ont été testés sur les données du benchmark MITOS. En ce qui concerne les images couleur, notre système s'est ainsi classé en deuxième position de ce concours international, selon la valeur du critère F-mesure. Par ailleurs, notre système de détection de mitoses sur images multispectrales surpasse largement les meilleurs résultats obtenus durant le concours. / Digital pathology represents one of the major and challenging evolutions in modernmedicine. Pathological exams constitute not only the gold standard in most of medicalprotocols, but also play a critical and legal role in the diagnosis process. Diagnosing adisease after manually analyzing numerous biopsy slides represents a labor-intensive workfor pathologists. Thanks to the recent advances in digital histopathology, the recognitionof histological tissue patterns in a high-content Whole Slide Image (WSI) has the potentialto provide valuable assistance to the pathologist in his daily practice. Histopathologicalclassification and grading of biopsy samples provide valuable prognostic information thatcould be used for diagnosis and treatment support. Nottingham grading system is thestandard for breast cancer grading. It combines three criteria, namely tubule formation(also referenced as glandular architecture), nuclear atypia and mitosis count. Manualdetection and counting of mitosis is tedious and subject to considerable inter- and intrareadervariations. The main goal of this dissertation is the development of a framework ableto provide detection of mitosis on different types of scanners and multispectral microscope.The main contributions of this work are eight fold. First, we present a comprehensivereview on state-of-the-art methodologies in nuclei detection, segmentation and classificationrestricted to two widely available types of image modalities: H&E (HematoxylinEosin) and IHC (Immunohistochemical). Second, we analyse the statistical and morphologicalinformation concerning mitotic cells on different color channels of various colormodels that improve the mitosis detection in color datasets (Aperio and Hamamatsu scanners).Third, we study oversampling methods to increase the number of instances of theminority class (mitosis) by interpolating between several minority class examples that lietogether, which make classification more robust. Fourth, we propose three different methodsfor spectral bands selection including relative spectral absorption of different tissuecomponents, spectral absorption of H&E stains and mRMR (minimum Redundancy MaximumRelevance) technique. Fifth, we compute multispectral spatial features containingpixel, texture and morphological information on selected spectral bands, which leveragediscriminant information for mitosis classification on multispectral dataset. Sixth, we performa comprehensive study on region and patch based features for mitosis classification.Seven, we perform an extensive investigation of classifiers and inference of the best one formitosis classification. Eight, we propose an efficient and generic strategy to explore largeimages like WSI by combining computational geometry tools with a local signal measureof relevance in a dynamic sampling framework.The evaluation of these frameworks is done in MICO (COgnitive MIcroscopy, ANRTecSan project) platform prototyping initiative. We thus tested our proposed frameworks on MITOS international contest dataset initiated by this project. For the color framework,we manage to rank second during the contest. Furthermore, our multispectral frameworkoutperforms significantly the top methods presented during the contest. Finally, ourframeworks allow us reaching the same level of accuracy in mitosis detection on brightlightas multispectral datasets, a promising result on the way to clinical evaluation and routine.

Immunohistological (IHC) stained images occupy a fundamental role in the pathologist¿s diagnosis and monitoring of cancer development. The manual process of monitoring such images is a subjective, time consuming process that typically relies on the visual ability and experience level of the pathologist. A novel and comprehensive system for the automated quantification of antibody inside stained cell nuclei in immunohistochemistry images is proposed and demonstrated in this research. The system is based on a cellular level approach, where each nucleus is individually analyzed to observe the effects of protein antibodies inside the nuclei. The system provides three main quantitative descriptions of stained nuclei. The first quantitative measurement automatically generates the total number of cell nuclei in an image. The second measure classifies the positive and negative stained nuclei based on the nuclei colour, morphological and textural features. Such features are extracted directly from each nucleus to provide discriminative characteristics of different stained nuclei. The output generated from the first and second quantitative measures are used collectively to calculate the percentage of positive nuclei (PS). The third measure proposes a novel automated method for determining the staining intensity level of positive nuclei or what is known as the intensity score (IS). The minor intensity features are observed and used to classify low, intermediate and high stained positive nuclei. Statistical methods were applied throughout the research to validate the system results against the ground truth pathology data. Experimental results demonstrate the effectiveness of the proposed approach and provide high accuracy when compared to the ground truth pathology data.