Extraction automatisée de l'angle de pennation du muscle gastrocnémien par analyse d'images -- Computer science

Dans ce travail, nous proposons trois différentes méthodes pour l’extraction de l’angle de pennation du muscle gastrocnémien d’un flux d’images échographiques. Les trois méthodes utilisent l’algorithme de Lucas-Kanade pour extraire l’orientation des aponévroses, mais, elles diffèrent dans leurs manières d’extraire l’orientation des fibres musculaires. La première méthode tente d’extraire cet angle en détectant les fibres et en les filtrant à partir de différentes propriétés. La méthode suivante se base sur la transformée de Radon pour l’extraction. La dernière méthode utilise une valeur d’initialisation pour l’angle, fournie par l’utilisateur, et l’algorithme de Lucas-Kanade, pour suivre lemouvement de la fibre musculaire.Les méthodes sont ensuite comparées en termes de facilité de mise en oeuvre, précision et performances. Dans la plupart des cas, c’est la méthode utilisant l’algorithme Lucas-Kanade qui semble offrir les meilleurs résultats avec une erreur moyenne inférieure au degré.info:eu-repo/semantics/nonPublishe

Image processing in digital pathology: an opportunity to improve the characterization of IHC staining through normalization, compartmentalization and colocalization

With the increasing amount of information needed for diagnosis and therapeutic decision-making, and new trends such as “personalized medicine”, pathologists are expressing an increasing demand for automated tools that perform their most recurrent tasks in their daily practice, as well as an increase in the complexity of the analyses requested in their research activities. With current advances in histopathology, oncology, and biology, the current questions require the analysis of protein expression - evidenced using immunohistochemical (IHC) staining - within specific histological structures or tissue components, or the analysis of the co-expression of several proteins in a large number of tissue samples. In this Ph.D. thesis, we developed innovative solutions to make these analyses available for pathologists. To achieve this objective, we have used recent “machine learning” and, in particular, “deep learning” methodologies. We addressed different problems such as image normalization, to solve the important problem of inter-batch variability of IHC staining, and the automatic segmentation of histological structures, to compartmentalize protein expression quantification. Finally, we adapted image registration techniques to Tissue MicroArray (TMA) slide images to enable large-scale analyses of IHC staining colocalization. While imagenormalization will improve study reproducibility, the tools developed for automated segmentation will drastically reduce time and expert resources required for some studies as well as errors and imprecision due to the human factor. Finally, the work on image registration can provide answers to complex questions that require studying the potential interaction between several proteins on numerous histological samples.Avec la quantité croissante d’informations nécessaires au diagnostic et à la prise de décision thérapeutique, et le développement de la “médecine personnalisée”, les pathologistes ont un besoin croissant d’outils automatisés pour exécuter leurs tâches les plus récurrentes. Ces outils se doivent également de réaliser des tâches de plus en plus complexes. En effet, avec les progrès récents en histopathologie, oncologie et biologie, les questions actuelles demandent, par exemple, l’analyse de l’expression de protéines révélées par marquages immunohistochimiques (IHC) au sein de structures ou compartiments histologiques spécifiques, ou encore l’analyse de la co-expression de plusieurs protéines dans un grand nombre d’échantillons. Dans cette thèse de doctorat, nous avons développé des solutions innovantes pour mettre ce type d’analyse à la disposition des pathologistes. Pour atteindre cet objectif, nous avons notamment fait appel à des méthodologies récentes de “machine learning” et, particulièrement, de “deep learning”. Nous avons ainsi abordé différentes questions telles que la normalisation d’images, pour résoudre l’important problème de la variabilité des marquages IHC, et la segmentation automatique de structures histologiques, pour permettre une quantification compartimentée de l’expression de protéines. Enfin, nous avons adapté des techniques dites de “recalage” aux images de lames de Tissue MicroArrays (TMA) pour permettre des analyses de colocalisation de marquages IHC à grande échelle. Alors que la normalisation des images améliore la reproductibilité des évaluations de marquages IHC, les outils développés pour la segmentation automatisée permettent de réduire significativement le temps et les ressources expertes nécessaires, ainsi que les erreurs et imprécisions dues au facteur humain. Enfin, les travaux sur le recalages d’images permettent d’apporter des éléments de réponse à des questions complexes qui nécessitent d’étudier l’interaction potentielle entre plusieurs protéines sur de nombreux échantillons histologiques.Doctorat en Sciences de l'ingénieur et technologieinfo:eu-repo/semantics/nonPublishe

Strategies to Reduce the Expert Supervision Required for Deep Learning-Based Segmentation of Histopathological Images

The emergence of computational pathology comes with a demand to extract more and more information from each tissue sample. Such information extraction often requires the segmentation of numerous histological objects (e.g. cell nuclei, glands, etc.) in histological slide images, a task for which deep learning algorithms have demonstrated their effectiveness. However, these algorithms require many training examples to be efficient and robust. For this purpose, pathologists must manually segment hundreds or even thousands of objects in histological images, i.e. a long, tedious and potentially biased task. The present paper aims to review strategies that could help provide the very large number of annotated images needed to automate the segmentation of histological images using deep learning. This review identifies and describes four different approaches: the use of immunohistochemical markers as labels, realistic data augmentation, Generative Adversarial Networks (GAN), and transfer learning. In addition, we describe alternative learning strategies that can use imperfect annotations. Adding real data with high-quality annotations to the training set is a safe way to improve the performance of a well configured deep neural network. However, the present review provides new perspectives through the use of artificially generated data and/or imperfect annotations, in addition to transfer learning opportunities.SCOPUS: re.jinfo:eu-repo/semantics/publishe

Automatic segmentation of glandular epithelium in colorectal tissue images using Deep Learning in order to compartmentalize IHC biomarker quantification

Immunohistochemistry (IHC) is commonly employed for diagnostic and prognostic purposes in histopathology as well as for biomarker validation in clinical research. Whole slide scanning and image analysis tools now enable objective and quantitative evaluation of IHC biomarkers in a whole tissue slide or a specific region of interest delineated by a pathologist. Compartmentalizing the quantitative evaluation of IHC biomarkers in a specific histological structure is often required to provide more relevant and informative measurements for clinical research. For this purpose, pathologists have to annotate thousands of structures present in histological slide series, a long, tedious and potentially biased task that would greatly benefit from automation.We developed an algorithm for automatically annotating glandular epithelium in slide images from colorectal tissue samples submitted to different staining techniques, including haematoxylin-eosin (H&E) as well as IHC. Our approach combines Deep Learning and a new method of data augmentation. The algorithm implements a convolutional neural network, which was first trained and evaluated with regard to the state-of-the-art on H&E images provided by the international GLaS (Gland Segmentation in Colon Histology Images) challenge contest. To apply our method in the context of IHC staining, we created a second dataset by using tissue microarray slides submitted to IHC to evidence the expression of different antigens on colorectal tumour samples. An expert manually annotated the images to delineate the glandular epithelium. We then quantified the IHC staining in and/or out of the glandular epithelium delineated on the basis of the manual or automatic annotations.Our method achieves state-of-the-art performances in epithelium segmentation on the H&E images and provides accurate segmentation on the IHC images, whatever the targeted antigen. Compartmentalized IHC quantification showed high concordance between measurements carried out using either manual or automatic segmentation. In addition to be efficient in terms of epithelium segmentation, our algorithm is very fast and thus relevant for quantitative IHC analysis performed on large series of whole (tissue or TMA) slides, as generally required in clinical research.info:eu-repo/semantics/publishe

Image processing in digital pathology: an opportunity to solve inter-batch variability of immunohistochemical staining

Immunohistochemistry (IHC) is a widely used technique in pathology to evidence protein expression in tissue samples. However, this staining technique is known for presenting inter-batch variations. Whole slide imaging in digital pathology offers a possibility to overcome this problem by means of image normalisation techniques. In the present paper we propose a methodology to objectively evaluate the need of image normalisation and to identify the best way to perform it. This methodology uses tissue microarray (TMA) materials and statistical analyses to evidence the possible variations occurring at colour and intensity levels as well as to evaluate the efficiency of image normalisation methods in correcting them. We applied our methodology to test different methods of image normalisation based on blind colour deconvolution that we adapted for IHC staining. These tests were carried out for different IHC experiments on different tissue types and targeting different proteins with different subcellular localisations. Our methodology enabled us to establish and to validate inter-batch normalization transforms which correct the non-relevant IHC staining variations. The normalised image series were then processed to extract coherent quantitative features characterising the IHC staining patterns.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Automated Tissue Microarray Image Processing in Digital Pathology.

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Image normalization for quantitative immunohistochemistry in digital pathology

We propose to adapt to immunohistochemistry (IHC) some methods proposed to normalize images from histological slices stained with hematoxylin-eosin (H&E). Our final aim is to provide a coherent quantitative characterization of IHC biomarkers across different IHC batches with possible staining variations. In contrast to H&E, IHC staining strongly varies with the tissue analyzed and the protein targeted, making image normalization challenging. To solve this problem, we added in each IHC batch a slice from a reference tissue microarray (TMA) and then digitalized it to establish an inter-batch normalization transform. A comparison of two methods adapted to the specificity of IHC-stained slides evidences some normalization requirements to make valid IHC biomarker quantification across different staining batches.SCOPUS: cp.pinfo:eu-repo/semantics/publishe

High-Throughput Analysis of Tissue-Based Biomarkers in Digital Pathology

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Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: a deep learning approach

In this paper, we propose a method for automatically annotating slide images from colorectal tissue samples. Our objective is to segment glandular epithelium in histological images from tissue slides submitted to different staining techniques, including usual haematoxylin-eosin (H&E) as well as immunohistochemistry (IHC). The proposed method makes use of Deep Learning and is based on a new convolutional network architecture. Our method achieves better performances than the state of the art on the H&E images of the GlaS challenge contest, whereas it uses only the haematoxylin colour channel extracted by colour deconvolution from the RGB images in order to extend its applicability to IHC. The network only needs to be fine-tuned on a small number of additional examples to be accurate on a new IHC dataset. Our approach also includes a new method of data augmentation to achieve good generalisation when working with different experimental conditions and different IHC markers. We show that our methodology enables to automate the compartmentalisation of the IHC biomarker analysis, results concurring highly with manual annotations.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Vascular Insulin-Like Growth Factor Receptor Type 2 (IGF2R) Expression is Upregulated in Malignant Tumours

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