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Chaganti, Shikha. "Image Analysis of Glioblastoma Histopathology." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406820611.
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DI, CATALDO SANTA. "Image Processing Techniques for Histopathology." Doctoral thesis, Politecnico di Torino, 2011. http://hdl.handle.net/11583/2586367.
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In the last few years biologists and pathologists are relying more and more on image analysis, and immunohistochemistry (IHC) is nowadays one of the most popular imaging techniques to analyze the presence and activity of target antigens in the tissues, with important applications in the diagnosis and assessment of tumors as well as for several research purposes. However, immunohistochemistry has been traditionally affected by lack of reproducibility due to technological variabilities as well as to the inherent subjectivity of the visual observation, thus the analysis has been limited to qualitative evaluation of the presence of the target stains within the tissues. The rapid evolution of the technique as a valid diagnostic and prognostic tool for tumor marker identification and cancer assessment has ultimately shifted the aim from qualitative to quantitative, stressing the demand for the standardization of the overall IHC assay and for the extraction of objective and repeatable measures of protein activity from the IHC images. Computer-aided image analysis has been universally acknowledged for having a fundamental role in solving the IHC standardization issue; in particular, tissue and cell segmentation techniques are precious instruments to identify the regions of interest of the target antigens in the specimens, allowing fully-automated and repeatable measurements at cellular and sub-cellular level; this is required by modern pathology and not feasible with simple visual evaluation. To this date, literature does not provide effective solutions for these challenging tasks, which gives motivation to our thesis work.
This thesis addresses the problems of tissue compartmentalization and cell segmentation in IHC images, proposing fully-automated techniques to i) recognize the cancerous areas of the samples disregarding non-interesting tissues such as stroma and blood vessels; ii) detect and delineate the sub-cellular regions of the cancerous cells, i.e. nuclei, cellular membranes and cytoplasm. The proposed methods, based on color and morphological processing, were validated on large datasets of IHC cancer images from several anatomical locations and compared experimentally with state of the art segmentation approaches, such as Support Vector Machines and active contours. Our extensive experimental results demonstrate the quantitative accuracy and reproducibility of the segmentations provided by our techniques, that can be used to obtain localized measure of protein activity as well as for any other applications requiring tissue and cell exploration in pathological tissues. We conclude our thesis with final remarks about IHC quantification methods, proposing a set of requirements to obtain reliable quantifications applying computer-aided image analysis.
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Sertel, Olcay. "Image Analysis for Computer-aided Histopathology." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1276791696.
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Haddad, Jane Wurster 1965. "Evaluation of diagnostic clues in histopathology through image processing techniques." Thesis, The University of Arizona, 1990. http://hdl.handle.net/10150/277296.
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The primary method for the diagnostic interpretation of histopathologic sections is visual analysis. However, in a small, but significant percentage of cases, histopathologists do not come to a consensus. Therefore, due to the importance of early and accurate detection of tissue changes indicative of pathology, quantitative image analysis techniques have been applied to this problem. The accurate segmentation of image structures such as cells and glands in histopathological sections, as with all "natural scenes", proves challenging. This has led to the development of an additional segmentation technique, the heuristic gradient search. Following the successful segmentation and labeling of scene objects, algorithms evaluating diagnostic clues as to the shape, size and distribution of image components were developed in order to form an overall diagnosis. A description of these diagnostic clues and the image processing techniques residing in the computer vision system used to evaluate them are presented.
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Traore, Lamine. "Semantic modeling of an histopathology image exploration and analysis tool." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066621/document.
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La formalisation des données cliniques est réalisée et adoptée dans plusieurs domaines de la santé comme la prévention des erreurs médicales, la standardisation, les guides de bonnes pratiques et de recommandations. Cependant, la communauté n'arrive pas encore à tirer pleinement profit de la valeur de ces données. Le problème majeur reste la difficulté à intégrer ces données et des services sémantiques associés au profit de la qualité de soins. Objectif L'objectif méthodologique de ce travail consiste à formaliser, traiter et intégrer les connaissances d'histopathologie et d'imagerie basées sur des protocoles standardisés, des référentiels et en utilisant les langages du web sémantique. L'objectif applicatif est de valoriser ces connaissances dans une plateforme pour faciliter l'exploration des lames virtuelles (LV), améliorer la collaboration entre pathologistes et fiabiliser les systèmes d'aide à la décision dans le cadre spécifique du diagnostic du cancer du sein. Il est important de préciser que notre but n'est pas de remplacer le clinicien, mais plutôt de l'accompagner et de faciliter ses lourdes tâches quotidiennes : le dernier mot reste aux pathologistes. Approche Nous avons adopté une approche transversale pour la représentation formelle des connaissances d'histopathologie et d'imagerie dans le processus de gradation du cancer. Cette formalisation s'appuie sur les technologies du web sémantiqueSemantic modelling of a histopathology image exploration and analysis tool. Recently, anatomic pathology (AP) has seen the introduction of several tools such as high-resolution histopathological slide scanners, efficient software viewers for large-scale histopathological images and virtual slide technologies. These initiatives created the conditions for a broader adoption of computer-aided diagnosis based on whole slide images (WSI) with the hope of a possible contribution to decreasing inter-observer variability. Beside this, automatic image analysis algorithms represent a very promising solution to support pathologist’s laborious tasks during the diagnosis process. Similarly, in order to reduce inter-observer variability between AP reports of malignant tumours, the College of American Pathologists edited 67 organ-specific Cancer Checklists and associated Protocols (CAP-CC&P). Each checklist includes a set of AP observations that are relevant in the context of a given organ-specific cancer and have to be reported by the pathologist. The associated protocol includes interpretation guidelines for most of the required observations. All these changes and initiatives bring up a number of scientific challenges such as the sustainable management of the available semantic resources associated to the diagnostic interpretation of AP images by both humans and computers. In this context, reference vocabularies and formalization of the associated knowledge are especially needed to annotate histopathology images with labels complying with semantic standards. In this research work, we present our contribution in this direction. We propose a sustainable way to bridge the content, features, performance and usability gaps between histopathology and WSI analysis
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Hossain, Md Shamim. "An automated deep learning based approach for nuclei segmentation of renal digital histopathology image analysis." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2611.
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Renal clear cell carcinoma affects the kidneys by abnormal cell division which spreads to other organs through the bloodstream and lymphatic system. The number of renal cancer cases grows whilst rapid and accurate diagnoses are required for early intervention. Biopsies are critical for cancer diagnosis. Pathologists look beyond manual evaluation to include computer-based analysis to develop accurate cancer diagnostics. Pathologists render diagnostic reports to assist with treatment whilst expert analysis is time consuming and restricts early diagnosis.
The process of manual expert pathology reporting is prohibitive and poor and repetitive concentration can lead to misdiagnosis. The probability of observational error increases along with the increased workload of the average pathologist and the demand for histopathology image analysis. Advances in computational and computer-assisted applications can provide accurate and timely analysis of histopathology images. Manual annotation now looks to machine learning algorithms. The nuclei segmentation technique is one possible approach. It uses deep learning-based nuclei segmentation approaches to train networks. This assists expert pathology which is expensive and time-consuming. Overlapping nuclei segmentation is a challenging issue for automated histopathology image analysis. Deep observation is required in digital images to identify the overlapping nuclei and variations in segmentation errors can mislead expert pathologists.
The aim of this research study was to perform a literature review of existing nuclei segmentation techniques including overlapping splitting algorithms; identify the limitations and knowledge gaps; and propose computerised deep learning based individual nuclei segmentation and analysis of histopathology images. A mixed method research study was performed with sequential research experiments in data collection; image pre-processing; synthetic image generation; segmentation of nuclei regions; overlapping nuclei; and the validation of a proposed framework. A series of experiments were executed to find the most viable approach.
An improved approach was designed for synthetic image generation using a cycle-consistent GAN network. The network created synthetic backgrounds and allowed for a CNN filtering method to separate the initial synthetic backgrounds. Nuclei shapes were collected to create transformed shapes. These transformed shapes were placed on the refined synthetic backgrounds to generate complete synthetic images. The similarity of original and synthetic images established and viable, valid pathway. A nuclei mask of synthetic images was collected to train a modified U-net segmentation network for better segmentation accuracy. These synthetic images performed better than original images. Accurately delineating the individual nucleus boundary helped to generatean automated system divide the nuclei clumps into individual nuclei in histopathology images. Using the nuclei ground truth of original images, it was possible to validate an application that informed manual expert pathology and to process multiple images and minimise histopathology image analysis.
The novelty of this research is the creation of an automated deep learning based individual nuclei segmentation system for renal histopathology images. The synthetic images and corresponding nuclei masks were trained with a modified U-net nuclei segmentation network. The trained network provides better nuclei segmentation performance in original images. The research developed a robust application which allows for the analysis of multiple histopathology images.
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Kårsnäs, Andreas. "Image Analysis Methods and Tools for Digital Histopathology Applications Relevant to Breast Cancer Diagnosis." Doctoral thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-219306.
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In 2012, more than 1.6 million new cases of breast cancer were diagnosed and about half a million women died of breast cancer. The incidence has increased in the developing world. The mortality, however, has decreased. This is thought to partly be the result of advances in diagnosis and treatment. Studying tissue samples from biopsies through a microscope is an important part of diagnosing breast cancer. Recent techniques include camera-equipped microscopes and whole slide scanning systems that allow for digital high-throughput scanning of tissue samples. The introduction of digital pathology has simplified parts of the analysis, but manual interpretation of tissue slides is still labor intensive and costly, and involves the risk for human errors and inconsistency. Digital image analysis has been proposed as an alternative approach that can assist the pathologist in making an accurate diagnosis by providing additional automatic, fast and reproducible analyses. This thesis addresses the automation of conventional analyses of tissue, stained for biomarkers specific for the diagnosis of breast cancer, with the purpose of complementing the role of the pathologist. In order to quantify biomarker expression, extraction and classification of sub-cellular structures are needed. This thesis presents a method that allows for robust and fast segmentation of cell nuclei meeting the need for methods that are accurate despite large biological variations and variations in staining. The method is inspired by sparse coding and is based on dictionaries of local image patches. It is implemented in a tool for quantifying biomarker expression of various sub-cellular structures in whole slide images. Also presented are two methods for classifying the sub-cellular localization of staining patterns, in an attempt to automate the validation of antibody specificity, an important task within the process of antibody generation. In addition, this thesis explores methods for evaluation of multimodal data. Algorithms for registering consecutive tissue sections stained for different biomarkers are evaluated, both in terms of registration accuracy and deformation of local structures. A novel region-growing segmentation method for multimodal data is also presented. In conclusion, this thesis presents computerized image analysis methods and tools of potential value for digital pathology applications.
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Fanchon, Louise. "Autoradiographie quantitative d'échantillons prélevés par biopsie guidée par TEP/TDM : méthode et applications cliniques." Thesis, Brest, 2016. http://www.theses.fr/2016BRES0018.
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Au cours des dix dernières années, l’utilisation de l’imagerie par tomographie par émission de positrons (TEP) s’est rapidement développée en oncologie. Certaines tumeurs non visibles en imagerie anatomique conventionnelle sont détectables en mesurant l'activité métabolique dans le corps humain par TEP. L’imagerie TEP est utilisée pour guider la délivrance de traitements locaux tels que par rayonnement ionisants ou ablation thermique. Pour la délivrance de ces traitements, segmenter la zone tumorale avec précision est primordial. Cependant, la faible résolution spatiale des images TEP rend la segmentation difficile. Plusieurs études ont démontré que la segmentation manuelle est sujette à une grande variabilité inter- et intra- individuelle et est fastidieuse. Pour ces raisons, de nombreux algorithmes de segmentation automatiques ont été développés. Cependant, peu de données fiables, avec des résultats histopathologiques existent pour valider ces algorithmes car il est expérimentalement difficile de les produire. Le travail méthodologique mis en place durant cette thèse a eu pour but de développer une méthode permettant de comparer les données histopathologiques aux données obtenue par TEP pour tester et valider des algorithmes de segmentation automatiques. Cette méthode consiste à réaliser des autoradiographies quantitatives de spécimens prélevés lors de biopsies guidées par TEP/tomodensitométrie (TDM); l’autoradiographie permettant d’imager la distribution du radiotraceur dans les échantillons avec une haute résolution spatiale. Les échantillons de tissus sont ensuite finement tranchés pour pouvoir être étudiés à l’aide d’un microscope. L’autoradiographie et les photomicrographes de l’échantillon de tissus sont ensuite recalés à l’image TEP, premièrement en les alignant avec l’aiguille à biopsie visible sur l’image TDM, puis en les transférant sur l’image TEP. Nous avons ensuite cherché à utiliser ces données pour tester deux algorithmes de segmentation automatique d'images TEP, le Fuzzy Locally Adaptive Bayesian (FLAB) développé au Laboratoire de Traitement de l'Information Médicale (LaTIM) à Brest, ainsi qu’une méthode de segmentation par seuillage. Cependant, la qualité de ces données repose sur la précision du recalage des images TEP, autoradiographiques et des micrographes. La principale source d’erreur dans le recalage de ces images venant de la fusion des images TEP/TDM, une méthode a été développée afin de quantifier la précision du recalage. Les résultats obtenus pour les patients inclus dans cette étude montrent que la précision de la fusion varie de 1.1 à 10.9 mm. En se basant sur ces résultats, les données ont été triées, pour finalement sélectionner les données acquises sur 4 patients jugées satisfaisantes pour tester les algorithmes de segmentation. Les résultats montrent qu’au point de la biopsie, les contours obtenus avec FLAB concordent davantage avec le bord de la lésion observé sur les micrographes. Cependant les deux méthodes de segmentation donnent des contours similaires, les lésions étant peu hétérogènesDuring the last decade, positron emission tomography (PET) has been finding broader application in oncology. Some tumors that are non-visible in standard anatomic imaging like computerized tomography (CT) or ultrasounds, can be detected by measuring in 3D the metabolic activity of the body, using PET imaging. PET images can also be used to deliver localized therapy like radiation therapy or ablation. In order to deliver localized therapy, the tumor border has to be delineated with very high accuracy. However, the poor spatial resolution of PET images makes the segmentation challenging. Studies have shown that manual segmentation introduces a large inter- and intra- variability, and is very time consuming. For these reasons, many automatic segmentation algorithms have been developed. However, few datasets with histopathological information are available to test and validate these algorithms since it is experimentally difficult to produce them. The aim of the method developed was to evaluate PET segmentation algorithms against the underlying histopathology. This method consists in acquiring quantitative autoradiography of biopsy specimen extracted under PET/CT guidance. The autoradiography allows imaging the radiotracer distribution in the biopsy specimen with a very high spatial accuracy. Histopathological sections of the specimen can then obtained and observed under the microscope. The autoradiography and the micrograph of the histological sections can then be registered with the PET image, by aligning them first with the biopsy needle seen on the CT image and then transferring them onto the PET image. The next step was to use this dataset to test two PET automatic segmentation algorithms: the Fuzzy Locally Adaptive Bayesian (FLAB) developed at the Laboratory of Medical Information Processing (LaTIM) in Brest, France, as well as a fix threshold segmentation method. However, the reliability of the dataset produced depends on the accuracy of the registration of the PET, autoradiography and micrograph images. The main source of uncertainty in the registration of these images comes from the registration between the CT and the PET. In order to evaluate the accuracy of the registration, a method was developed. The results obtained with this method showed that the registration error ranges from 1.1 to 10.9mm. Based on those results, the dataset obtained from 4 patients was judged satisfying to test the segmentation algorithms. The comparison of the contours obtained with FLAB and with the fixed threshold method shows that at the point of biopsy, the FLAB contour is closer than that to the histopathology contour. However, the two segmentation methods give similar contours, because the lesions were homogeneous
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Hrabovszki, Dávid. "Classification of brain tumors in weakly annotated histopathology images with deep learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177271.
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Brain and nervous system tumors were responsible for around 250,000 deaths in 2020 worldwide. Correctly identifying different tumors is very important, because treatment options largely depend on the diagnosis. This is an expert task, but recently machine learning, and especially deep learning models have shown huge potential in tumor classification problems, and can provide fast and reliable support for pathologists in the decision making process. This thesis investigates classification of two brain tumors, glioblastoma multiforme and lower grade glioma in high-resolution H&E-stained histology images using deep learning. The dataset is publicly available from TCGA, and 220 whole slide images were used in this study. Ground truth labels were only available on whole slide level, but due to their large size, they could not be processed by convolutional neural networks. Therefore, patches were extracted from the whole slide images in two sizes and fed into separate networks for training. Preprocessing steps ensured that irrelevant information about the background was excluded, and that the images were stain normalized. The patch-level predictions were then combined to slide level, and the classification performance was measured on a test set. Experiments were conducted about the usefulness of pre-trained CNN models and data augmentation techniques, and the best method was selected after statistical comparisons. Following the patch-level training, five slide aggregation approaches were studied, and compared to build a whole slide classifier model. Best performance was achieved when using small patches (336 x 336 pixels), pre-trained CNN model without frozen layers, and mirroring data augmentation. The majority voting slide aggregation method resulted in the best whole slide classifier with 91.7% test accuracy and 100% sensitivity. In many comparisons, however, statistical significance could not be shown because of the relatively small size of the test set.
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Azar, Jimmy. "Automated Tissue Image Analysis Using Pattern Recognition." Doctoral thesis, Uppsala universitet, Bildanalys och människa-datorinteraktion, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-231039.
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Automated tissue image analysis aims to develop algorithms for a variety of histological applications. This has important implications in the diagnostic grading of cancer such as in breast and prostate tissue, as well as in the quantification of prognostic and predictive biomarkers that may help assess the risk of recurrence and the responsiveness of tumors to endocrine therapy. In this thesis, we use pattern recognition and image analysis techniques to solve several problems relating to histopathology and immunohistochemistry applications. In particular, we present a new method for the detection and localization of tissue microarray cores in an automated manner and compare it against conventional approaches. We also present an unsupervised method for color decomposition based on modeling the image formation process while taking into account acquisition noise. The method is unsupervised and is able to overcome the limitation of specifying absorption spectra for the stains that require separation. This is done by estimating reference colors through fitting a Gaussian mixture model trained using expectation-maximization. Another important factor in histopathology is the choice of stain, though it often goes unnoticed. Stain color combinations determine the extent of overlap between chromaticity clusters in color space, and this intrinsic overlap sets a main limitation on the performance of classification methods, regardless of their nature or complexity. In this thesis, we present a framework for optimizing the selection of histological stains in a manner that is aligned with the final objective of automation, rather than visual analysis. Immunohistochemistry can facilitate the quantification of biomarkers such as estrogen, progesterone, and the human epidermal growth factor 2 receptors, in addition to Ki-67 proteins that are associated with cell growth and proliferation. As an application, we propose a method for the identification of paired antibodies based on correlating probability maps of immunostaining patterns across adjacent tissue sections. Finally, we present a new feature descriptor for characterizing glandular structure and tissue architecture, which form an important component of Gleason and tubule-based Elston grading. The method is based on defining shape-preserving, neighborhood annuli around lumen regions and gathering quantitative and spatial data concerning the various tissue-types.
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Vandenberghe, Michel. "3D whole-brain quantitative histopathology : methodology and applications in mouse models of Alzheimer's disease." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066411/document.
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L’histologie est la méthode de choix pour l’étude ex vivo de la distribution spatiale des molécules qui composent les organes. En particulier, l’histologie permet de mettre en évidence les marqueurs neuropathologiques de la maladie d’Alzheimer ce qui en fait un outil incontournable pour étudier la physiopathologie de la maladie et pour évaluer l’efficacité de candidats médicaments. Classiquement, l’analyse de données histologiques implique de lourdes interventions manuelles, et de ce fait, est souvent limitée à l’analyse d’un nombre restreint de coupe histologiques et à quelques régions d’intérêts. Dans ce travail de thèse, nous proposons une méthode automatique pour l’analyse quantitative de marqueurs histopathologiques en trois dimensions dans le cerveau entier de rongeurs. Les images histologiques deux-dimensionnelles sont d’abord reconstruites en trois dimensions en utilisant l’imagerie photographique de bloc comme référence géométrique et les marqueurs d’intérêts sont segmentés par apprentissage automatique. Deux approches sont proposées pour détecter des différences entre groupes d’animaux: la première est basée sur l’utilisation d’une ontologie anatomique de cerveau qui permet détecter des différences à l’échelle de structures entières et la deuxième approche est basée sur la comparaison voxel-à-voxel afin de détecter des différences locales sans a priori spatial. Cette méthode a été appliquée dans plusieurs études chez des souris modèles de déposition amyloïde afin d’en démontrer l’utilisabilitéHistology is the gold standard to study the spatial distribution of the molecular building blocks of organs. In humans and in animal models of disease, histology is widely used to highlight neuropathological markers on brain tissue sections. This makes it particularly useful to investigate the pathophysiology of neurodegenerative diseases such as Alzheimer’s disease and to evaluate drug candidates. However, due to tedious manual interventions, quantification of histopathological markers is classically performed on a few tissue sections, thus restricting measurements to limited portions of the brain. Quantitative methods are lacking for whole-brain analysis of cellular and pathological markers. In this work, we propose an automated and scalable method to thoroughly quantify and analyze histopathological markers in 3D in rodent whole brains. Histology images are reconstructed in 3D using block-face photography as a spatial reference and the markers of interest are segmented via supervised machine learning. Two complimentary approaches are proposed to detect differences in histopathological marker load between groups of animals: an ontology-based approach is used to infer difference at the level of brain regions and a voxel-wise approach is used to detect local differences without spatial a priori. Several applications in mouse models of A-beta deposition are described to illustrate 3D histopathology usability to characterize animal models of brain diseases, to evaluate the effect of experimental interventions, to anatomically correlate cellular and pathological markers throughout the entire brain and to validate in vivo imaging techniques
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Bug, Daniel [Verfasser], Dorit [Akademischer Betreuer] Merhof, and Horst K. [Akademischer Betreuer] Hahn. "Digital histopathology : Image processing for histological analyses and immune response quantification / Daniel Bug ; Dorit Merhof, Horst K. Hahn." Aachen : Universitätsbibliothek der RWTH Aachen, 2020. http://d-nb.info/1240689543/34.
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Sazzad, TM Shahriar. "An automated approach to identify nongrowing follicles using digitized images of type P63 histopathology ovarian slides." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2017. https://ro.ecu.edu.au/theses/2032.
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Many developing countries are still facing challenges with limited access to fertility health services. Women face problems in conceiving due to many factors such as increasing age. In vitro fertilization (IVF) treatments can assist these women but are considered too expensive. Medical pathology laboratories are searching for novel technologies that can improve microscopic slide testing of female ovarian reproductive tissues. Current electronic methods for the assessment of human ovaries are not suitable for analysis of ovarian reproductive tissues. Ultrasound method cannot be used to identify small ovarian Non-Growing Follicles (NGFs) that are responsible for reproduction. A computer assisted approach to overcome the problems associated with manual microscopic analysis of ovarian reproductive tissues could be beneficial in increasing the accuracy and speed of the analysis. Few studies have reported on the use of images and other artificial intelligence techniques for ovarian tissue samples and have mostly concentrated on the analysis of cancer cells or ovarian animal tissues which are different from human ovarian reproductive tissues. Other studies using human ovarian reproductive tissues have been limited in terms of accuracy. This research examines the possibility of developing an automated computer approach which will improve the practices of these pathology laboratories to analyse female ovarian reproductive tissues and assist medical practitioners to provide necessary fertility treatment. The objective of the research was to study existing computerized methods used in various tissue assessments; identify the gaps and limitations; and to propose a novel method on digitized colour images acquired from ovarian reproductive tissue slides. The following major research question has been addressed by the research study: “How to develop an automated approach to assist pathology experts to identify ovarian reproductive NGFs (Non-growing follicles) or simply ovarian reproductive tissues using digital images acquired from type P63 (counter and non-counter stained) histopathology ovarian biopsy slides?” In order to answer this question, the research was carried out in a number of phases to examine existing computerized techniques for impact assessment of the ovarian reproductive tissue analysis. The research used a mixed method approach based on a case study using experimental and engineering methodologies. The study also employed quantitative and statistical data analysis methods. The research was carried out as a series of research activities including data collection, image processing, development of proposed approach, assessment factors (different magnification and different stains), validation of results with manual microscopic analysis results and development of the framework. A series of 7 different approaches were examined which started with basic image analysis technique. Modification and further medications were carried out to find the best possible approach which maintains the “gold standard” criteria in comparison to manual microscopic analysis results. A novel proposed approach was developed which used two phases: (1) phase one include pre-processing (intensity correction, filter operation, colour image segmentation, intensity clustering, feature extraction approach to find out the most suitable features); and (2) phase two for identifications of potential ovarian NGFs using shape, size and colour features that were extracted in phase one. It was found that the accuracy rate was above 90% for all magnifications and stains used in this research study which maintains the “gold standard” criteria in comparison to manual microscopic analysis results. To increase the accuracy rate and to diminish the false error rate classification approach was incorporated. The proposed approach established the most effect techniques in comparison to existing available approaches. A novel intensity correction was proposed and incorporated at the beginning of pre-processing, fast reliable novel filter operation was developed and incorporated for filter operation, colour image segmentation was considered to use the colour features for identification of region of interest (ROIs) from other tissues, extraction of features to capture NGFs’ characteristics, and incorporation of domain knowledge to identify NGFs. Validation was carried out with experts’ manual microscopic analysis results and similar regions were analysed to minimize the experts’ observation variability issues to improve the accuracy rate. A prototype software tool was developed in MATLAB platform, which enables a non-expert to easily use and analyse the ovarian reproductive tissues without changing any processing parameter automatically by giving the image magnification and image type as input parameter. The proposed approach was found to reduce the time and effort required for the analysis without any human intervention. The novelty of the research is that the approach was fully automated; non-experts will be able to use this approach for analysis; and no change of processing parameter is essential for new image batch/batches. The approach was also accurate, reliable and provided repeatable results in comparison to manual microscopic analysis results. Further work could explore the modification of tissue parameters that could be used for other tissue analysis.
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Sharma, Harshita [Verfasser], Olaf [Akademischer Betreuer] Hellwich, Olaf [Gutachter] Hellwich, Niels [Gutachter] Grabe, and Peter [Gutachter] Hufnagl. "Medical image analysis of gastric cancer in digital histopathology: methods, applications and challenges / Harshita Sharma ; Gutachter: Olaf Hellwich, Niels Grabe, Peter Hufnagl ; Betreuer: Olaf Hellwich." Berlin : Technische Universität Berlin, 2017. http://d-nb.info/1156180163/34.
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Ngwa, Victor Ngu. "Evolution of liver fibrosis during long-term experimental Schistosoma japonicum infection in pigs /." Uppsala : Dept. of Biomedicine and Veterinary Public Health, Swedish University of Agricultural Sciences, 2006. http://epsilon.slu.se/10425083.pdf.
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Gavrilovic, Milan. "Spectral Image Processing with Applications in Biotechnology and Pathology." Doctoral thesis, Uppsala universitet, Centrum för bildanalys, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-160574.
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Color theory was first formalized in the seventeenth century by Isaac Newton just a couple of decades after the first microscope was built. But it was not until the twentieth century that technological advances led to the integration of color theory, optical spectroscopy and light microscopy through spectral image processing. However, while the focus of image processing often concerns modeling of how images are perceived by humans, the goal of image processing in natural sciences and medicine is the objective analysis. This thesis is focused on color theory that promotes quantitative analysis rather than modeling how images are perceived by humans. Color and fluorescent dyes are routinely added to biological specimens visualizing features of interest. By applying spectral image processing to histopathology, subjectivity in diagnosis can be minimized, leading to a more objective basis for a course of treatment planning. Also, mathematical models for spectral image processing can be used in biotechnology research increasing accuracy and throughput, and decreasing bias. This thesis presents a model for spectral image formation that applies to both fluorescence and transmission light microscopy. The inverse model provides estimates of the relative concentration of each individual component in the observed mixture of dyes. Parameter estimation for the model is based on decoupling light intensity and spectral information. This novel spectral decomposition method consists of three steps: (1) photon and semiconductor noise modeling providing smoothing parameters, (2) image data transformation to a chromaticity plane removing intensity variation while maintaining chromaticity differences, and (3) a piecewise linear decomposition combining advantages of spectral angle mapping and linear decomposition yielding relative dye concentrations. The methods described herein were used for evaluation of molecular biology techniques as well as for quantification and interpretation of image-based measurements. Examples of successful applications comprise quantification of colocalization, autofluorescence removal, classification of multicolor rolling circle products, and color decomposition of histological images.
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Akbar, Shazia. "Tumour localisation in histopathology images." Thesis, University of Dundee, 2015. https://discovery.dundee.ac.uk/en/studentTheses/c282ab9c-5776-400f-8440-f5ac9cf2f4ba.
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Immunohistochemical (IHC) assessment in cancer research is important for understanding the distribution and localisation of biomarkers at the cellular level. However currently IHC analyses are predominantly performed manually, increasing workloads and introducing inter- and intra-observer variability. Automation shows great potential in clinical research to reduce pathologists' workloads and speed up cancer research in large clinical studies. Whilst recent advancements in digital pathology have enabled IHC measurements to be performed automatically, the acquisition of manual annotations of tumours in scanned digital slides is still a limiting factor. In this thesis, an automated solution to tumour localisation is explored with the aim of replacing manual annotations. As an exemplar, human breast tissue microarrays stained with estrogen receptor are considered. Methods for automated tumour localisation are described with a focus on capturing structural information in tissue by adopting superpixel properties in a rotation invariant manner, suitable for histopathology images. To incorporate essential contextual information, methods which utilise posterior tumour probabilities in an iterative manner are proposed. Results showed pixel-level agreements between automated and manual tumour segmentation masks (κ=0.811) approach inter-rater agreement between expert pathologists (κ=0.908). A large proportion of disagreements between automated and manual segmentations were shown to correlate to minor discrepancies, inconsequential for IHC assessment. IHC scores extracted from automated and manual tumour segmentation masks showed strong agreements (Allred: κˆ=0.911; Quickscore: κˆ=0.922), demonstrating the potential of automation in clinical practice across large clinical trials.
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Samsi, Siddharth Sadanand. "Computer Aided Analysis of IHC and H&E Stained Histopathological Images in Lymphoma and Lupus." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1333560691.
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Jorge, Ana Elisa Serafim. "Terapia fotodinâmica em pele fotoenvelhecida de camundongo hairless: avaliação por técnicas óptica e histopatológica." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/82/82131/tde-07072014-142307/.
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A exposição crônica à radiação ultravioleta (UV) resulta no fotoenvelhecimento da pele humana. A fim de tratá-lo, a terapia fotodinâmica (TFD) - técnica que utiliza luz, um fotossensibilizador (FS) e oxigênio molecular - tem sido utilizada em ambientes clínicos, no entanto são escassas as investigações que revelam os achados histopatológicos desse tratamento na pele fotoenvelhecida. Com isso, o objetivo deste trabalho foi analisar experimentalmente os efeitos da TFD na pele fotoenvelhecida de camundongos por meio de técnicas ópticas e histológicas. Portanto, foram utilizados camundongos hairless (sem pelo) distribuídos aleatoriamente em diferentes grupos, tais como: Controle, animais de pele sadia envelhecida intrinsecamente, não irradiados com luz UV e não tratados; UV, animais irradiados com luz UV e não tratados; UV/Luz, animais irradiados com luz UV e tratados com fototerapia; UV/TFD, animais fotoenvelhecidos (irradiados com luz UV) e tratados com a TFD; e Controle/TFD, animais de pele sadia envelhecida intrinsecamente e tratados com TFD. A indução do fotoenvelhecimento foi realizada por diferentes fontes de luz contendo, principalmente, a banda espectral UV; para a TFD, foram utilizadas fontes de luz com comprimento de onda de 415, 630 e 635 nm (azul e vermelho) juntamente com o ácido 5-aminolevulínico (ALA), precursor do FS endógeno protoporfirina IX (PpIX). Para fazer o seguimento da TFD por técnicas ópticas, foram realizadas espectroscopia de fluorescência, imagem de fluorescência de campo amplo e tomografia por coerência óptica (OCT, do inglês optical coherence tomography). A avaliação histopatológica pós-TFD foi realizada com os corantes HE, Tricrômio de Masson (TM) e Verhoeff a fim de analisar a espessura da epiderme, o infiltrado inflamatório, o conteúdo de colágeno na derme e sua espessura e a qualidade das fibras elásticas. Como resultado, observouse aumento significativo da espessura da epiderme pela regeneração dos queratinócitos e deposição de novas fibras colágenas dérmicas apenas nos animais tratados com ALA-TFD, tanto irradiados quanto não-irradiados com luz UV (grupos UV/TFD e Controle/TFD, respectivamente). Assim, torna-se evidente que a TFD trata a pele fotoenvelhecida do camundongo hairless pelos achados histopatológicos e através das imagens de OCT. Com isso, este trabalho agrega informação às investigações clínicas a respeito da TFD no tratamento da pele fotoenvelhecida, atestando seu uso para o fotorrejuvenescimento cutâneo.The chronic exposure to ultraviolet radiation (UVR) leads to photoaging of the human skin. As a treatment, the photodynamic therapy (PDT) which brings together light, a photosensitizer, and molecular oxygen has been extensively used on clinical trials; however, there are few studies that highlight the histopathologic findings regarding this technique on photoaged skin. Thus, the aim of this study was to analyze experimentally the effects of PDT on photoaged skin of hairless mice by means of optical and histopathologic assessments. For this, hairless mice were randomly allocated in different groups, such as: Control, animals with healthy aged skin, neither exposed to an UV lamp nor treated; UV, animals exposed to an UV lamp with no treatment; UV/Light, animals exposed to an UV lamp and treated just with light; UV/PDT, animals exposed to an UV lamp and treated with PDT; and Control/PDT, animals with healthy aged skin treated with PDT. The photoaging process was induced by different light sources, which had mainly the UV spectrum; for the PDT, light sources of different wavelengths (415, 630, 635 nm) were used (blue and red light sources) and the 5-aminolevulinic acid (ALA), PS precursor of the endogenous protoporphyrin IX (PpIX). In order to follow the PDT protocol up by means of optical techniques, we used fluorescence spectroscopy, fluorescence images and optical coherence tomography. The histopathologic assessment after the PDT procedure was performed with H&E, Massons Trichrome and Verhoeff stains for epidermal thickness, inflammatory infiltrate, dermal collagen content, dermal thickness, and quality of elastic fibers. As a result, it has been observed a significant epidermal thickening due to keratinocytes regeneration and newly formed dermal collagen fibers only on the groups treated with PDT-ALA (UV/PDT and Control/PDT groups). Therefore, it is evident that PDT treats the photoaged skin of hairless mice since histopatologic findings and OCT images have shown those morphology changes. In conclusion, this work add information to the clinical trials regarding the PDT as a reliable technique to treat photoaged skin, proving its use for the skin photorejuvenation.
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Khan, Adnan M. "Algorithms for breast cancer grading in digital histopathology images." Thesis, University of Warwick, 2014. http://wrap.warwick.ac.uk/66024/.
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Histological analysis of tissue biopsies by an expert pathologist is considered gold standard for diagnosing many cancers, including breast cancer. Nottingham grading system, which is the most widely used criteria for histological grading of breast tissues, consists of three components: mitotic count, nuclear atypia and tubular formation. In routine histological analysis, pathologists perform grading of breast cancer tissues by manually examining each tissue specimen against the three components, which is a laborious and subjective process and thus can suffer from low inter-observer agreement. With the advent of digital whole-slide scanning platforms, automatic image analysis algorithms can be used as a partial solution for these issues. The main goal of this dissertation is to develop frameworks that can aid towards building an automated or semi-automated breast cancer grading system. We present novel frameworks for detection of mitotic cells and nuclear atypia scoring in breast cancer histopathology images. Both of these frameworks can play a fundamental role in developing a computer-assisted breast cancer grading system. Moreover, the proposed image analysis frameworks can be adapted to grading and analysis of cancers of several other tissues such as lung and ovarian cancers. In order to deal with one of the fundamental problems in histological image analysis applications, we first present a stain normalisation algorithm that minimises the staining inconsistency in histological images. The algorithm utilises a novel image-specific colour descriptor which summarises the colour contents of a histological image. Stain normalisation algorithm is used in the remainder of the thesis as a preprocessing step. We present a mitotic cell detection framework mimicking a pathologist’s approach, whereby we first perform tumour segmentation to restrict our search for mitotic cells to tumour regions only, followed by candidate detection and evaluation in a statistical machine learning framework. We also employ a discriminative dictionary learning paradigm to learn the visual appearance of mitotic cells, that models colour, texture, and shape in a composite manner. Finally, we present a nuclear atypia scoring framework based on a novel image descriptor which summarises the texture heterogeneity, inherent in histological images in a compact manner. Classification is performed using a geodesic k-nearest neighbour classifier which explicitly exploits the structure of Riemannian manifold of the descriptor and achieves significant performance boost as compared to Euclidean counterpart.
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Barros, George Oliveira. "PathoSpotter: um sistema para classifica??o de glomerulopatias a partir de imagens histol?gicas renais." Universidade Estadual de Feira de Santana, 2016. http://localhost:8080/tede/handle/tede/389.
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The realization of an accurate diagnosis from histological images requires pathologists with practical experience because the characteristics of these images lead to a subjective analysis, which often hamper the accuracy of diagnosis. Systems that help to achieve better diagnoses can minimize doubts and improve the quality of diagnosis, influencing on increasing the effectiveness of medical treatments. This paper describes the research and development of PathoSpotter, a computer system to aid in the identification of diseases from histological images. The PathoSpotter proposes to reduce the lack of support work to histopathological diagnosis of renal diseases since much has been done in the area of cancer, but there is few published material in relation to the Digital Pathology applied to nephrology and hepatology. Our goal in this study was to apply the PathoSpotter the classification of proliferative glomerulopathy, which is a family of primary diseases affecting the kidneys. The work was based on a data set consisting of 811 histological pictures glomeruli and classical techniques of processing digital images and histopathology were used. The PathoSpotter presented a performance of 88.4% accuracy, which was similar to other Digital Pathology jobs that can be found in the literature.
A realiza??o do diagn?stico preciso a partir de imagens histol?gicas requer m?dicos patologistas com vasta experi?ncia pr?tica, pois as caracter?sticas dessas imagens conduzem a uma an?lise subjetiva que muitas vezes dificultam a exatid?o do diagn?stico. Sistemas que auxiliam a obten??o de melhores diagn?sticos podem minimizar d?vidas e melhorar a qualidade dos diagn?sticos, influenciando no aumento da efic?cia dos tratamentos m?dicos. Este trabalho descreve a pesquisa e o desenvolvimento do PathoSpotter, um sistema computacional para aux?lio na identifica??o de patologias a partir de imagens histol?gicas. O PathoSpotter se prop?e a reduzir a car?ncia de trabalhos de apoio ao diagn?stico histopatol?gico das doen?as renais, j? que muito tem sido feito na ?rea de neoplasias, mas h? pouco material publicado em rela??o ? Patologia Digital aplicada ? nefrologia ou hepatologia. Nosso objetivo neste trabalho foi aplicar o PathoSpotter na classifica??o das glomerulopatias proliferativas, que ? uma fam?lia de doen?as prim?rias que afetam os rins. O trabalho se baseou em um conjunto de dados composto por 811 imagens histol?gicas de glom?rulos, e foram utilizadas t?cnicas cl?ssicas de processamento de imagens e histopatologia digital. O PathoSpotter apresentou um desempenho de 88,4% de acur?cia, resultado similar ao de outros trabalhos de Patologia Digital que podem ser encontrados na literatura especializada.
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Liu, Jingxin. "Stain separation, cell classification and histochemical score in digital histopathology images." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/52290/.
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This thesis focuses on developing new automatic techniques addressing three typical problems in digital histopathology image analysis, histochemical stain separation at pixel-level, cell classifications at region level, and histochemical score assessment at image level, with the aim of providing useful tools to help histopathologists in their decision making. First, we study a pixel-level problem, separating positive chemical stains. To realise the full potential of digital pathology, accurate and robust computer techniques for automatically detecting biomarkers play an important role. Traditional methods transform the colour histopathology images into a gray scale image and apply a single threshold to separate positively stained tissues from the background. In this thesis, we show that the colour distribution of the positive immunohistochemical stains varies with the level of luminance and that a single threshold will be impossible to separate positively stained tissues from other tissues, regardless how the colour pixels are transformed. Based on this observation, two novel luminance adaptive biomarker detection methods are proposed. The first, termed Luminance Adaptive Multi-Thresholding (LAMT) first separate the pixels according to their luminance levels and for each luminance level a separate threshold is found for detecting the positive stains. The second, termed Luminance Adaptive Random Forest (LARF) applies one of the most powerful machine learning models, random forest, as a base classifier to build an ensemble classifier for biomarker detection. Second, we study a cell-level problem, the cell classification task in pathology images. Two different classification models are proposed. The first model for HEp-2 cell pattern classification comes with a novel object-graph based feature, which decompose the binary image into primitive objects and represent them with a set of morphological feature. Work on cell classification is further extended using deep learning model termed Deep Autoencoding-Classification Network (DACN). The DACN model consists of an autoencoder and a conventional classification convolutional neural network (CNN) with the two sharing the same encoding pipeline. The DACN model is jointly optimized for the classification error and the image reconstruction error based on a multi-task learning procedure. We will present experiment results to show that the proposed DACN outperforms all known state-of-the-art on two public indirect immunofluorescence stained HEp-2 cell datasets and H\&E stained colorectal adenocarcinomas cell dataset. Third, we study an image-level problem, assessing the histochemical score of a histopathology image. To determine the molecular class of the tumour, pathologists will have to manually mark the nuclei activity biomarkers by assigning a histochemical score (H-Score) to each TMA core with a semi-quantitative assessment method. Manually marking positively stained nuclei is a time consuming, imprecise and subjective process which will lead to inter-observer and intra-observer discrepancies. In this thesis, we present an end-to-end deep learning system which directly predicts the H-Score automatically. Our system imitates the pathologists' decision process and uses one fully convolutional network (FCN) to extract all nuclei region, a second FCN to extract tumour nuclei region, and a multi-column convolutional neural network which takes the outputs of the first two FCNs and the stain intensity description image as input and acts as the decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a TMA image as input and directly outputs a clinical score. We will present experimental results which demonstrate that the H-Scores predicted by our model have very high and statistically significant correlation with experienced pathologists' scores and that the H-Score discrepancy between our algorithm and the pathologists is on par with the inter-subject discrepancy between the pathologists.
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Willemse, Feike. "A colored view on quantitative pathology aspects of true color image analysis in routine pathology /." [S.l. : [Groningen] : s.n.] ; [University Library Groningen] [Host], 1996. http://irs.ub.rug.nl/ppn/143919504.
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Tay, ChiangHau. "Algorithms for Tissue Image Analysis using Multifractal Techniques." Thesis, University of Canterbury. Computer Science and Software Engineering, 2012. http://hdl.handle.net/10092/7268.
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Histopathological classification and grading of biopsy specimens play an important role in early cancer detection and prognosis. Nottingham Grading System (NGS) is one of the standard grading procedures used in breast cancer assessment, where three parameters, Mitotic Count (MC), Nuclear Pleomorphism (NP), and Tubule Formation (TF) are used for prognostic information. The grading takes into account the deviations in cellular structures and appearance between tumour and normal cells, using measures such as density, size, colour, and regularity. Cell structures in tissue images are also known to exhibit multifractal characteristics.
This research focused on the multifractal properties of several graded biopsy specimens and analysed the dependency and variation of the fractal parameters with respect to the scores pre-assigned by pathologists. The effectiveness of using multifractal techniques on breast cancer grading was measured with a set of quantitative evaluations for MC, NP, and TF criteria. The developed method for MC scoring has obtained 82.87% true positive rate on detecting mitotic cells. Furthermore, the overall positive classification rates for NP and TF analysis were 67.38% and 71.82%, respectively, while obtaining 30.26% of false classification rate for NP analysis and 27.17% for TF analysis. The results have shown that multifractal formalism is a feasible and novel method that could be used for automatic grading of biopsy sections.
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Irshad, Humayun. "Automated Mitosis Detection in Color and Multi-spectral High-Content Images in Histopathology : Application to Breast Cancer Grading in Digital Pathology." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENM007/document.
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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 concoursDigital 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
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Quarrie, Karisha Claudia. "Correlation of post mortem LODOX digital radiological images with histopathological findings at autopsy : a prospective autopsy study at the Tygerberg Forensic Pathology Service Facility." Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/96682.
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Thesis (MMed)--Stellenbosch University, 2015.ENGLISH ABSTRACT: Background: The LODOX Statscan is a whole-body digital X-ray scanning device which was adapted for medical usage. The LODOX has an established role in the field of Forensic Pathology where it shows high sensitivity and specificity for the detection of skeletal pathology and foreign bodies. The role of the scanner in the detection of soft tissue pathology in the lungs of adults has not been reported and this study aims to review the radio-pathological correlation and the applicability of LODOX as a viable screening tool in the detection of lung pathology in post mortem cases. Methods: We prospectively reviewed cases which were referred for medico-legal autopsy between November 2012 and March 2013 to the Tygerberg Forensic Pathology Service mortuary, Cape Town, South Africa. All cases meeting the prescribed inclusion criteria underwent LODOX scanning as well as macroscopic and microscopic evaluation of the lungs as permitted by the Inquests Act 58 of 1959. The macroscopic and microscopic variables were considered the “gold standard” when compared with the results of the LODOX. The sensitivity, specificity, positive and negative predictive values were assessed. Results: One hundred and fifty nine cases (159) were included in the study. The most common radiographic patterns reported were the presence of ground glass opacities and consolidation. Overall, low to moderate sensitivity of these LODOX patterns in the prediction of pneumonic microscopic pathology (oedema, acute and chronic inflammation and features of diffuse alveolar damage) was noted. These values were lower than that reported for pneumonia using conventional X-rays. Additionally, these LODOX patterns have a high probability of representing oedema or autolytic/decomposition change. Pneumothorax was the most common pleural pathology detected on LODOX, but autopsy correlation could not be performed. Poor to no correlation was noted with the variables of cavity, malignant tumour, and bronchiectasis, but the prevalence of these conditions in our cohort was low. In general, LODOX predictions were better at excluding pathology which was not present rather than confirming pathology which was present. Conclusions: The LODOX offers excellent evidentiary value in the demonstration of a pneumothorax but currently has limited value as a “stand alone” test in the field of Forensic Pathology. However the continued use of the LODOX as an adjunct examination, as well as prospective study of its applicability, is advised.
AFRIKAANSE OPSOMMING: Agtergrond: Die LODOX Statscan is ‘n heel-liggaam digitale X-straal skandeer apparaat wat aangepas is vir mediese gebruik. Die LODOX het ‘n gevestigde rol in Geregtelike Patologie, waar dit ‘n hoë sensitiwiteit en spesifisiteit het in die opsporing van skeletale patologie en vreemde voorwerpe. Die rol van die skandeerder in die opspoor van sagte weefsel patologie in die longe van volwassenes is nog nie gerapporteer nie, en hierdie studie ondersoek die radio-patologiese korrelasie en toepaslikheid van LODOX as ‘n doeltreffende siftingsmeganisme om long patologie op te spoor in post-mortale gevalle. Metode: Gevalle wat verwys is na die Tygerberg Geregtelike Patologie Diens lykshuis in Kaapstad, Suid-Afrika vir medies-geregtelike outopsies tussen November 2012 en Maart 2013, is prospektief geëvalueer. Alle gevalle wat die voorgeskrewe insluitingskriteria nagekom het, het LODOX skandering asook makroskopiese en mikroskopiese ondersoek van die longe ondergaan, soos toegelaat deur die Wet op Geregtelike Doodsondersoeke Nr 58 van 1959. Die makroskopiese en mikroskopiese veranderlikes is beskou as die “goud standaard” in vergelyking met die resultate van die LODOX. Die sensitiwiteit, spesifisiteit, positiewe en negatiewe voorspellingswaardes is beoordeel. Resultate: Eenhonderd-nege-en-vyftig gevalle (159) is ingesluit in die studie. Die algemeenste radiografiese pattroon wat gerapporteer is, was die teenwoordigheid van gemaalde glas opasiteit en konsolidasie. In geheel is lae to matige sensitiwiteit van hierdie LODOX beelde waargeneem in die voorspelling van pneumoniese mikroskopiese patologie (edeem, akute en chroniese ontsteking, en eienskappe van diffuse alveolêre skade). Hierdie waardes was laer as die wat gerapporteer is vir pneumonie met konvensionele X-strale. Verder het hierdie LODOX beelde ‘n hoë waarskynlikheid om edeem en/of outolise/ontbinding uit te beeld. Pneumotoraks was die algemeenste pleurale patologie wat waargeneem is met die LODOX, maar outopsie korrelasie kon nie gedoen word nie. Swak tot geen korrelasie is gemerk vir die veranderlikes kaviteit, maligne tumor en brongi-ektase, maar die prevalensie van hierdie toestande in ons kohort was laag. Oor die algemeen was LODOX voorspellings beter om patologie wat nie teenwoordig is nie, uit te skakel, eerder as om patologie wat teenwoordig is, te bevestig. Gevolgtrekking: The LODOX is ‘n uitstekende bewysstuk in die aantoon van ‘n pneumotoraks, maar huidiglik het dit beperkte waarde as onafhanklike toets in die veld van Geregtelike Patologie. Desnieteenstaande word die verdere gebruik van LODOX as bydraende ondersoek, sowel as die prospektiewe studie van sy toepaslikheid aanbeveel.
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Signolle, Nicolas. "Approches multiéchelles pour la segmentation de très grandes images : application à la quantification de biomarqueurs en histopathologie cancérologique." Phd thesis, Université de Caen, 2009. http://tel.archives-ouvertes.fr/tel-01073319.
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Visualiser et analyser automatiquement des coupes fines de tumeurs cancéreuses sont des enjeux majeurs pour progresser dans la compréhension des mécanismes de la cancérisation et mettre en évidence de nouveaux indicateurs de réponse au traitement. Les nouveaux scanners microscopiques apportent une aide essentielle en fournissant des images couleur haute résolution de la totalité des lames histologiques. Ceci permet de s'affranchir de l'hétérogénéité de distribution des marqueurs à quantifier. La taille de ces images, appelées lames virtuelles, peut atteindre plusieurs GigaOctets. L'objectif de cette thèse est de concevoir et d'implémenter une méthode de segmentation permettant de séparer les différents types de compartiments stromaux présents sur une lame virtuelle de carcinome ovarien. Les deux principales difficultés à surmonter sont la taille des images, qui empêche de les traiter en une seule fois, et le choix de critères permettant de différencier les compartiments stromaux. Pour répondre à ces problèmes, nous avons développé une méthode générique de segmentation multiéchelle qui associe un découpage judicieux de l'image à une caractérisation de chaque compartiment stromal, considéré comme une texture. Cette caractérisation repose sur une modélisation multiéchelle des textures par un modèle d'arbre de Markov caché, appliqué sur les coefficients de la décomposition en ondelettes. Plutôt que de considérer tous les types de compartiments stromaux simultanément, nous avons choisi de transformer le problème multiclasse en un ensemble de problèmes binaires. Nous avons également analysé l'influence d'hyperparamètres (représentation couleur, type d'ondelettes et nombre de niveaux de résolutions intégrés à l'analyse) sur la segmentation, ce qui nous a permis de sélectionner les classifieurs les mieux adaptés. Différentes méthodes de combinaison des décisions des meilleurs classifieurs ont ensuite été étudiées. La méthode a été testée sur une vingtaine de lames virtuelles. Afin d'évaluer les résultats de la segmentation, nous avons mis en œuvre un protocole de tests fondé sur une approche stéréologique. Les résultats sur différents jeux de tests (images synthétiques, images de petite taille, images réelles) sont présentés et commentés. Les résultats obtenus sur les lames virtuelles sont prometteurs, compte tenu de la variabilité des échantillons et de la difficulté, pour un expert, à identifier parfois très précisément un compartiment : environ 60% des points sont correctement classés (entre 35% et 80% selon les lames).
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Morel, Sophie. "Imagerie grand champ en anatomopathologie." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAY075/document.
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L’objectif de cette thèse a été de développer une méthode simple et rapide (35 minutes) d'imagerie afin d’enregistrer des images grand champ (jusqu’à 2,5 cm x 2,5 cm) et multi-échelles (µm-cm) de lames de tissus colorés et non colorés en anatomopathologie.La solution proposée est basée sur l’imagerie sans lentille. C’est une méthode simple, bas coût, qui permet d’enregistrer des images grand champ (10-30 mm²) d’objets épars, comme des virus, des bactéries ou des cellules. Dans ces travaux, nous montrons qu’il est possible d'obtenir en imagerie sans lentille des images d'objets denses tels que des lames de tissus colorés ou non marqués. Pour ce faire, l’échantillon est illuminé sous différentes longueurs d’onde, et un nouvel algorithme de reconstruction holographique multi-longueurs d’onde permet de reconstruire le module et la phase d’objets denses. Chaque image est reconstruite en 1,1 seconde couvrant un champ de 10 mm². Une image totale de la lame de tissu, couvrant un champ de 6,25 cm², est obtenue en 35 minutes en scannant l’échantillon au-dessus du capteur. Les images reconstruites sont multi-échelles, permettant à l’utilisateur d’observer en une seule fois la structure générale du tissu, et de zoomer jusqu’à la cellule individuelle (3-4 µm). La méthode a été testée sur différents échantillons anatomopathologiques colorés et non colorés. Au-delà des lames de tissus, l’imagerie sans lentille multi-longueurs d’onde montre des résultats encourageants pour le diagnostic de la méningite, le suivi au cours du temps d’une population bactérienne pour l’identification et la réalisation d’antibiogrammes, et le suivi au cours du temps de cultures cellulairesThis PhD project aims to develop a simple, fast (35 minutes), wide-field (up to 2.5 cm x 2.5 cm) multiscale (µm-cm) imaging method for stained and unstained tissue slides for digital pathology application. We present a solution based on lensfree imaging. It is a simple, low-cost technique that enables wide field imaging (10-30 mm²) of sparse objects, like viruses, bacteria or cells. In this project, we adapted lensfree imaging for dense objects observation, like stained or unstained tissue slides. The sample is illuminated under multiple illumination wavelengths, and a new multiwavelength holographic reconstruction algorithm was developed in order to reconstruct the modulus and phase of dense objects. Each image covers 10 mm² field of view, and is reconstructed in 1.1 second. An image of the whole tissue slide covers 6.25 cm². It is recorded in 35 minutes by scanning the sample over the sensor. The reconstructed images are multiscale, allowing the user to observe the overall tissue structure and to zoom down to the single cell level (3-4 µm). The method was tested on various stained and unstained pathology samples. Besides tissue slides, multiwavelength lensfree imaging shows encouraging results for meningitis diagnosis, bacteria population monitoring for identification and antibiotic susceptibility testing, and cell culture monitoring
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Laifa, Oumeima. "A joint discriminative-generative approach for tumour angiogenesis assessment in computational pathology." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS230.
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L’angiogenèse est le processus par lequel de nouveaux vaisseaux sanguins se forment à partir du réseaux préexistant. Au cours de l’angiogenèse tumorale, les cellules tumorales sécrètent des facteurs de croissance qui activent la prolifération et la migration des cellules et stimulent la surproduction du facteur de croissance endothélial vasculaire (VEGF). Le rôle fondamental de l’approvisionnement vasculaire dans la croissance tumorale et le developement des thérapies anticancéreuses rend l’évaluation de l’angiogenèse tumorale, cruciale dans l’évaluation de l’effet des thérapies anti-angiogéniques, en tant que thérapie anticancéreuse prometteuse. Dans cette étude, nous établissons un panel quantitatif et qualitatif pour évaluer les structures des vaisseaux sanguins de la tumeur sur des images de fluorescence non invasives et des images histopathologique sur toute la surface tumorale afin d’identifier les caractéristiques architecturales et les mesures quantitatives souvent associées à la réponse thérapeutique ou prédictive de celle-ci. Nous développons un pipeline formé de Markov Random Field (MFR) et Watershed pour segmenter les vaisseaux sanguins et les composants du micro-environnement tumoral afin d’évaluer quantitativement l’effet du médicament anti-angiogénique Pazopanib sur le système vasculaire tumoral et l’interaction avec le micro-environnement de la tumeur. Le pazopanib, agent anti-angiogénèse, a montré un effet direct sur le système vasculaire du réseau tumoral via les cellules endothéliales. Nos résultats montrent une relation spécifique entre la néovascularisation apoptotique et la densité de noyau dans une tumeur murine traitée par Pazopanib. Une évaluation qualitative des vaisseaux sanguins de la tumeur est réalisée dans la suite de l’étude. Nous avons développé un modèle de réseau de neurone discriminant-générateur basé sur un modele d’apprentissage : réseau de neurones convolutionnels (CNN) et un modèle de connaissance basé sur des règles Marked Point Process (MPP) permettant de segmenter les vaisseaux sanguins sur des images très hétérogènes à l’aide de très peu de données annotées. Nous détaillons l’intuition et la conception du modèle discriminatif-génératif, sa similarité avec les Réseaux antagonistes génératifs (GAN) et nous évaluons ses performances sur des données histopathologiques et synthétiques. Les limites et les perspectives de la méthode sont présentées à la fin de notre étudeAngiogenesis is the process through which new blood vessels are formed from pre-existing ones. During angiogenesis, tumour cells secrete growth factors that activate the proliferation and migration of endothelial cells and stimulate over production of the vascular endothelial growth factor (VEGF). The fundamental role of vascular supply in tumour growth and anti-cancer therapies makes the evaluation of angiogenesis crucial in assessing the effect of anti-angiogenic therapies as a promising anti-cancer therapy. In this study, we establish a quantitative and qualitative panel to evaluate tumour blood vessels structures on non-invasive fluorescence images and histopathological slide across the full tumour to identify architectural features and quantitative measurements that are often associated with prediction of therapeutic response. We develop a Markov Random Field (MFRs) and Watershed framework to segment blood vessel structures and tumour micro-enviroment components to assess quantitatively the effect of the anti-angiogenic drug Pazopanib on the tumour vasculature and the tumour micro-enviroment interaction. The anti-angiogenesis agent Pazopanib was showing a direct effect on tumour network vasculature via the endothelial cells crossing the whole tumour. Our results show a specific relationship between apoptotic neovascularization and nucleus density in murine tumor treated by Pazopanib. Then, qualitative evaluation of tumour blood vessels structures is performed in whole slide images, known to be very heterogeneous. We develop a discriminative-generative neural network model based on both learning driven model convolutional neural network (CNN), and rule-based knowledge model Marked Point Process (MPP) to segment blood vessels in very heterogeneous images using very few annotated data comparing to the state of the art. We detail the intuition and the design behind the discriminative-generative model, and we analyze its similarity with Generative Adversarial Network (GAN). Finally, we evaluate the performance of the proposed model on histopathology slide and synthetic data. The limits of this promising framework as its perspectives are shown
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Filho, Clerio Francisco de Azevedo. "Avaliação da fibrose miocárdica pela ressonância magnética cardíaca na doença valvar aórtica grave: validação de um algoritmo de quantificação e comparação com a histopatologia." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/5/5131/tde-29042009-111724/.
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Introdução: A doença valvar aórtica grave é caracterizada por um processo de acúmulo progressivo de fibrose intersticial no tecido miocárdico. No contexto da sobrecarga mecânica crônica do VE característica dessa condição, a quantidade de fibrose intersticial pode exercer um papel importante na indesejável transição entre hipertrofia ventricular esquerda compensada e insuficiência cardíaca congestiva clinicamente manifesta. Entretanto, a avaliação quantitativa da fibrose intersticial só tem sido possível através da análise histopatológica de fragmentos miocárdicos obtidos por biopsia endomiocárdica. Objetivos: Avaliar se a ressonância magnética (RM) cardíaca com técnica do realce tardio permite a quantificação não-invasiva da fibrose miocárdica quando comparada à análise histopatológica em pacientes portadores de doença valvar aórtica grave. Adicionalmente, avaliou-se a relação entre a quantidade de fibrose miocárdica e parâmetros prognósticos importantes, tais como mortalidade e recuperação funcional do VE após cirurgia de troca valvar aórtica. Métodos: Entre Maio de 2001 e Dezembro de 2003 foram incluídos 54 pacientes com indicação de cirurgia de troca valvar aórtica. Antes da cirurgia, todos os pacientes foram submetidos a RM cardíaca com técnicas de cine-RM e realce tardio miocárdico. A quantificação da fibrose miocárdica pela RM baseou-se na análise das imagens de realce tardio utilizando um novo algoritmo semi-automático. As regiões de fibrose miocárdica foram definidas como o somatório de todos os pixels do tecido miocárdico com intensidade de sinal acima de um limiar definido como: intensidade de sinal média do miocárdio + 2 desvios padrão da intensidade de sinal média da área remota + 2 desvios padrão da intensidade de sinal média do ar. Amostras de tecido miocárdico obtidas por miectomia durante o ato cirúrgico foram submetidas a coloração pelo picrosírius para quantificação da fibrose intersticial. Os pacientes foram submetidos a um segundo exame de RM cardíaca 6 meses após a cirurgia para se avaliar as alterações evolutivas dos parâmetros funcionais do VE e todos foram acompanhados por pelo menos 24 meses quanto à sobrevida após a cirurgia de troca valvar aórtica. Resultados: O percentual de fibrose miocárdica pela RM apresentou boa correlação com os valores obtidos pela histopatologia (r=0,69; y=3,10x+13,0; p<0,0001). A quantidade de fibrose miocárdica, tanto pela histopatologia como pela RM, apresentou correlação inversa significativa com a FE ventricular esquerda basal (r=-0,63 e -0,67 respectivamente; p<0,0001). Adicionalmente, o percentual de fibrose miocárdica apresentou correlação inversa significativa com o grau de recuperação funcional do VE após a cirurgia de troca valvar (r=- 0,42, p=0,04 para a histopatologia; r=-0,47, p=0,02 para a RM). Mais importante, a análise de Kaplan-Meier revelou que o acúmulo de fibrose miocárdica associou-se a menor sobrevida 52±17 meses após a cirurgia de troca valvar (teste log-rank: 2=6,32; p=0,01 para histopatologia; 2=5,85; p=0,02 para RM). Conclusões: A RM cardíaca permite quantificar as regiões de fibrose miocárdica com boa acurácia quando comparada à análise histopatológica nos pacientes portadores de doença valvar aórtica grave. A magnitude de acúmulo de fibrose miocárdica está associada a pior recuperação funcional do VE e a menor sobrevida após a cirurgia de troca valvar aórtica.Introduction: Severe aortic valve disease is characterized by a process of progressive accumulation of interstitial fibrosis in the myocardial tissue. It has been shown that the amount of interstitial myocardial fibrosis can play an important role in the transition from well-compensated hypertrophy to overt heart failure in the setting of chronic left ventricular mechanical overload typical of this condition. However, assessment of interstitial myocardial fibrosis has only been possible through histological analyses of myocardial fragments obtained from endomyocardial biopsies, which is a complex and invasive procedure and, therefore, with limited clinical applicability. Objectives: Determine whether delayedenhancement cardiac magnetic resonance imaging (MRI) allows for the non-invasive quantification of myocardial fibrosis when compared against histopathological analyses in patients with severe aortic valve disease. Additionally, we evaluated the relationship between the amount of myocardial fibrosis and important prognostic parameters, such as all-cause mortality and LV functional recovery after aortic valve replacement. Methods: Fifty-four patients scheduled to undergo aortic valve replacement surgery were enrolled between May 2001 and December 2003. Before surgery, all patients underwent cine and delayedenhancement MRI in a 1.5 Tesla scanner. Quantification of myocardial fibrosis by cardiac MRI was based on the assessment of the delayed-enhancement dataset using a novel semiautomatic algorithm. The regions of myocardial fibrosis were defined as the sum of pixels with signal intensity above a threshold value defined as: mean signal intensity of the myocardium + 2 standard deviations of mean signal intensity of a remote area + 2 standard deviations of mean signal intensity of air. During open-heart surgery, myectomy samples were acquired from the LV septum and later stained with picrosirius for interstitial myocardial fibrosis quantification. A second cardiac MRI study was performed 6 months after surgery to assess long-term changes in LV functional parameters, and all patients were followed for at least 24 months to evaluate survival after aortic valve replacement. Results: There was a good correlation between the values of myocardial fibrosis measured by MRI and those obtained by histopathological analyses (r=0.69; y=3.10x+13.0; p<0.0001). The amount of myocardial fibrosis, either by MRI or by histopathology, exhibited a significant inverse correlation with LV ejection fraction before surgery (r=-0.63 e -0.67 respectively; p<0.0001). Additionally, the amount of myocardial fibrosis displayed a significant inverse correlation with the degree of LV functional recovery after aortic valve replacement (r=-0.42, p=0.04 for histopathology; r=-0.47, p=0.02 for MRI). Most importantly, Kaplan-Meier and Cox regression analyses revealed that higher degrees of myocardial fibrosis accumulation were associated with worse survival 52±17 months after aortic valve replacement surgery (log-rank test: 2=6.32; p=0.01 for histopathology; 2=5.85; p=0.02 for MRI). Conclusions: Cardiac MRI allows for the non-invasive quantification of myocardial fibrosis with good accuracy when compared with histopathological analyses in patients with severe aortic valve disease. The degree of myocardial fibrosis accumulation is associated with impaired LV functional recovery and worse survival after aortic valve replacement surgery.
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Basavanhally, Ajay. "Automated image-based detection and grading of lymphocytic infiltration in breast cancer histopathology." 2010. http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000052094.
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Greenberg, Alexandra Rachel. "Longitudinal histopathological, immunohistochemical, and In Situ hybridization analysis of host and viral biomarkers in liver tissue sections of Ebola (EBOV) infected rhesus macaques." Thesis, 2019. https://hdl.handle.net/2144/36565.
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INTRODUCTION: Ebola virus (EBOV) is a highly infectious and often lethal filovirus that causes hemorrhagic fever, with a reported case fatality rate of 40-90%. There are currently no Food and Drug Administration (FDA) approved medical countermeasures (MCMs) for EBOV. Non-human primates (NHPs) remain the gold standard animal model for EBOV research as they most accurately recapitulate human disease.
OBJECTIVE: This study aimed to characterize the temporal viral pathogenesis of EBOV in the liver of infected rhesus macaques using routine histopathology, multiplex immunohistochemistry (mIHC) and multiplex fluorescent In Situ Hybridization (mFISH), refined by digital pathology (DP) and image analysis (DIA).
METHODS: 21 FFPE liver sections from EBOV-infected rhesus macaques were examined microscopically (Uninfected controls n=3; 3 DPE n=3; 4 DPE n=3; 5 DPE n=3; 6 DPE n=3; Terminal n=6). Tissues were stained with H&E and PTAH for histopathological scoring. Three serial sections were fluorescently immunolabeled or hybridized under three independent conditions (1.EBOV VP35, Tissue Factor, CD68; 2.EBOV VP35, Heppar, Myeloperoxidase (MPO); 3.EBOV VP35, IL-6, ISG-15). Slides were digitized by a Vectra PolarisTM fluorescent whole slide scanner and DIA was conducted using HaloTM image analysis software. Statistical analysis was conducted using GraphPad PrismTM 8.0.
RESULTS: Comparing peracute (3-4 DPE) to acute (5-6 DPE) and terminal (6-8 DPE) EBOV infection, there is a statistically significant (p < 0.05) increase in hepatic inflammation and fibrin thrombi, correlating with an absolute increase in macrophages (CD68), neutrophils (MPO), and total % of Tissue Factor in the liver. There is also a significant increase in the severity of necrosis, which correlates with a decrease in Heppar. While there was significant colocalization of VP35 and CD68 starting at 4 DPE, there was only rare colocalization of VP35 with Heppar, even in terminal animals. Similar to mIHC, progressive and statistically significant differences were observed in gene expression when comparing peracute to acute and terminal EBOV infection. IL-6 predominated within periportal fibrovascular compartments, but also colocalized within cells concurrently expressing EBOV VP35. EBOV VP35 expression was observed within histiocytes, endothelial cells, and less commonly hepatocytes. ISG-15 expression was observed in periportal regions and in proximity to cells expressing EBOV VP35, but colocalization within EBOV VP35 expressing cells was an extremely rare event.
CONCLUSION: Qualitative tools are well suited for confirming virulence and viral tissue tropism, but do little to build on our current understanding of disease. Using DIA in partnership with mIHC and mFISH, this study quantified statistically significant temporal changes in the immunoreactivity and hybridization of host and viral biomarkers that have previously been linked to the pathogenesis of EBOV. Taken together, these tools have enabled us to characterize minute changes that reflect magnitudes of biological variability simply not feasible to detect with the human eye. Furthermore, spatial context has refined our current understanding of differential gene expression of EBOV, which has the potential to aid in development of host-directed therapies. The establishment of these benchmarks will serve as a guide for the validation of cross-institutional EBOV animal models.
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SAADIZADEH, SAMAN. "SIGNIFICANTLY ACCURATE SYSTEM FOR BREAST CANCER MALIGNANCY OR BENIGN CLASSIFICATION." Thesis, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19429.
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Breast cancer happens to one out of eight females worldwide. It is the most elevated reason for
cancer malignancy deadliness among ladies. It is identified by finding the cancerous cells in
breast tissue. Novel techniques in medical image processing utilized histopathology dataset
images taken by an advanced microscope, and then disintegrate the images by applying various
algorithms and techniques. Artificial Intelligence methods are presently being applied for
processing pathological imagery and tools. Here in the project work, we concentrate on
building up the capability of computer-aided diagnosis (CAD) to anticipate the severity of
cancerous cells. Common cancerous cell detecting is a tedious process and involves the fault of
physicians, to this end we can use computer-aided detection (CAD) system to reduce the fault
and obtain the more acceptable outcome in comparison to a common pathological detection
system. Here we are comparing, our framework with the other three machine learning
frameworks in breast image segmentation and classification on a well-known dataset (BreakHis)
trial arrangement. Classification in deep neural network mainly utilize feature extraction by the
means of convolutional neural network and then by embedding a fully connected network, the
result would be an acceptable output. Deep learning has a vast amount of functionality in
medical image processing without any need for supervision of any professional person during
the process and the procedure can be done automatically. Here in our project we train a bunch
of histopathology images through a convolutional neural network and obtain accuracy in
prediction more than 92%.