Computational techniques in medical image analysis application for white blood cells classification.

White blood cells play important rule in the human body immunity and any change in their count may cause serious diseases. In this study, a system is introduced for white blood cells localization and classification. The dataset used in this study is formed by two components, the first is the annotation dataset that will be used in the localization (364 images), and the second is labeled classes that will be used in the classification (12,444 images). For the localization, two approaches will be discussed, a classical approach and a deep learning based approach. For the classification, 5 different deep learning architectures will be discussed, 3 pretrained architectures and 2 customized architectures will be presented. After discussing this models and test them on the dataset, the best selected model will be evaluated describing the obtained results. The localization module achieved average Intersection over Union (IoU) of 71%, while the classification module achieved 92 % classification accuracy. In addition to reporting the model performance, the model robustness was also checked by adding three different types of noise, Gaussian noise, salt and pepper noise, and speckle noise. This system outperforms other studies in the literature, where the accuracy was either less than the obtained from the system or the dataset was much smaller the used data in this study

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning

The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However, it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25–5.0 μg/mL) and/or carbendazim (0.8–1.6 μg/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the “DeepFlow” neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for ‘mononucleates’, ‘binucleates’, ‘mononucleates with MN’ and ‘binucleates with MN’, respectively. Successful classifications of ‘trinucleates’ (90%) and ‘tetranucleates’ (88%) in addition to ‘other or unscorable’ phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful (≤ 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent concentration regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning.

The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However, it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25-5.0 μg/mL) and/or carbendazim (0.8-1.6 μg/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the "DeepFlow" neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for 'mononucleates', 'binucleates', 'mononucleates with MN' and 'binucleates with MN', respectively. Successful classifications of 'trinucleates' (90%) and 'tetranucleates' (88%) in addition to 'other or unscorable' phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful (≤ 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent concentration regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks

An automated classification system for leukocyte morphology in acute myeloid Leukemia

Diagnosis of hematological malignancies and of acute myeloid leukemia in particular have undergone wide-ranging advances in recent years, driven by an increasingly detailed knowledge of its underlying biological and genetic mechanisms. Nevertheless, cytomorphologic evaluation of samples of peripheral blood and bone marrow is still an integral part of the routine diagnostic workup. Microscopic analysis of these samples has so far defied automation and is still mainly performed by human cytologists manually classifying and counting relevant cell populations. Access to this diagnostic modality is therefore limited by the number and availability of educated cytologists. Furthermore, its results rest on judgments of examiners, which may vary according to their education and experience, rendering rigorous quantification and standardization of the method difficult. In this thesis, an approach to cytomorphologic classification is presented that aims to harness recent advances in computational image classification for leukocyte differentiation using Deep Learning techniques that derive from the domain of Artificial Intelligence. In a first stage of the project, peripheral blood smear samples from both AML patients and controls were scanned using techniques from digital pathology. Experienced cytologists from the Laboratory of Leukemia Diagnostics at the LMU Klinikum annotated the digitized samples according to a scheme of 15 morphological categories derived from standard routine diagnostics. The resulting set of over 18,000 annotated single-cell images is the largest public database of leukocyte morphologies in leukemia available today. In a second step, the compiled dataset was used to develop a neural network that is able to classify leukocytes into the standard morphological scheme. Evaluation of network predictions show that the network performs well at the classification task for most clinically relevant categories, with an error pattern similar to that of human examiners. The network can also be employed to answer two questions of immediate clinical relevance, namely if a given single-cell image shows a blast-like cell, or if it belongs to the set of atypical cells which are not present in peripheral blood smears under physiological conditions. At these questions, the network is found to show similar and slightly better performance compared to the human examiner. These results show the potential of Deep Learning techniques in the field of hematological diagnostics and suggest avenues for their further development as a helpful tool of leukemia diagnostics.In der Diagnostik hämatologischer Erkrankungen wie der akuten myeloischen Leukämie haben sich in den vergangenen Jahren bedeutende Fortschritte ergeben, die vor allem auf einem vertieften Verständnis ihrer biologischen und genetischen Ursachen beruhen. Trotzdem spielt die zytomorphologische Untersuchung von Blut- und Knochenmarkspräparaten nach wie vor eine zentrale Rolle in der diagnostischen Aufarbeitung. Die mikroskopische Begutachtung dieser Präparate konnte bisher nicht automatisiert werden und erfolgt nach wie vor durch menschliche Befunder, die eine manuelle Differentierung und Auszählung relevanter Zelltypen vornehmen. Daher ist der Zugang zu zytomorphologischen Untersuchungen durch die Zahl verfügbarer zytologischer Befunder begrenzt. Darüber hinaus beruht die Beurteilung der Präparate auf der individuellen Einschätzung der Befunder und ist somit von deren Ausbildung und Erfahrung abhängig, was eine standardisierte und quantitative Auswertung der Morphologie zusätzlich erschwert. Ziel der vorliegenden Arbeit ist es, ein computerbasiertes System zu entwickeln, die die morphologische Differenzierung von Leukozyten unterstützt. Zu diesem Zweck wird auf in den letzten Jahren entwickelte leistungsfähige Algorithmen aus dem Bereich der Künstlichen Intelligenz, insbesondere des sogenannten Tiefen Lernens zurückgegriffen. In einem ersten Schritt des Projekts wurden periphere Blutausstriche von AML-Patienten und Kontrollen mit Methoden der digitalen Pathologie erfasst. Erfahrene Befunder aus dem Labor für Leukämiediagnostik am LMU-Klinikum München annotierten die digitalisierten Präparate und differenzierten sie in ein 15-klassiges, aus der Routinediagnostik stammendes Standardschema. Auf diese Weise wurde mit über 18,000 morphologisch annotierten Leukozyten der aktuell größte öffentlich verfügbare Datensatz relevanter Einzelzellbilder zusammengestellt. In einer zweiten Phase des Projekts wurde dieser Datensatz verwendet, um Algorithmen vom Typ neuronaler Faltungsnetze zur Klassifikation von Einzelzellbilden zu trainieren. Eine Analyse ihrer Vorhersagen zeigt dass diese Netzwerke Einzelzellbilder der meisten Zellklassen sehr erfolgreich differenzieren können. Für falsch klassifizierte Bilder ähnelt ihr Fehlermuster dem menschlicher Befunder. Neben der Klassifikation einzelner Zellen erlauben die Netzwerke auch die Beantwortung gröberer, binärer Fragestellungen, etwa ob eine bestimmte Zelle blastären Charakter hat oder zu den morphologischen Klassen gehört die in einem peripheren Blutausstrich nicht unter physiologischen Bedingungen vorkommen. Bei diesen Fragen zeigen die Netzwerke eine ähnliche und leicht bessere Leistung als der menschliche Befunder. Die Ergebnisse dieser Arbeit illustrieren das Potential von Methoden der künstlichen Intelligenz auf dem Gebiet der Hämatologie und eröffnen Möglichkeiten zu ihrer Weiterentwicklung zu einem praktischen Hilfsmittel der Leukämiediagnostik

Cancer Outcome Prediction with Multiform Medical Data using Deep Learning

This thesis illustrated the work done for my PhD project, which aims to develop personalised cancer outcome prediction models using various types of medical data. A predictive modelling workflow that can analyse data with different forms and generate comprehensive outcome prediction was designed and implemented on a variety of datasets. The model development was accompanied by applying deep learning techniques for multivariate survival analysis, medical image analysis and sequential data processing. The modelling workflow was applied to three different tasks: 1. Deep learning models were developed for estimating the progression probability of patients with colorectal cancer after resection and after different lines of chemotherapy, which got significantly better predictive performance than the Cox regression models. Besides, CT-based models were developed for predicting the tumour local response after chemotherapy of patients with lung metastasis, which got an AUC of 0. 769 on disease progression detection and 0.794 on treatment response classification. 2. Deep learning models were developed for predicting the survival state of patients with non-small cell lung cancer after radiotherapy using CT scans, dose distribution and disease and treatment variables. The eventual model obtained by ensemble voting got an AUC of 0.678, which is significantly higher than the score achieved by the radiomics model (0.633). 3. Deep-learning-aided approaches were used for estimating the progression risk for patients with solitary fibrous tumours using digital pathology slides. The deep learning architecture was able to optimise the WHO risk assessment model using automatically identified levels of mitotic activity. Compared to manual counting given by pathologists, deep-learning-aided mitosis counting can re-grade the patients whose risks were underestimated. The applications proved that the predictive models based on hybrid neural networks were able to analyse multiform medical data for generating data-based cancer outcome prediction. The results can be used for realising personalised treatment planning, evaluating treatment quality, and aiding clinical decision-making

Computer aided classification of histopathological damage in images of haematoxylin and eosin stained human skin

EngD ThesisExcised human skin can be used as a model to assess the potency, immunogenicity and contact sensitivity of potential therapeutics or cosmetics via the assessment of histological damage. The current method of assessing the damage uses traditional manual histological assessment, which is inherently subjective, time consuming and prone to intra-observer variability. Computer aided analysis has the potential to address issues surrounding traditional histological techniques through the application of quantitative analysis. This thesis describes the development of a computer aided process to assess the immune-mediated structural breakdown of human skin tissue. Research presented includes assessment and optimisation of image acquisition methodologies, development of an image processing and segmentation algorithm, identification and extraction of a novel set of descriptive image features and the evaluation of a selected subset of these features in a classification model. A new segmentation method is presented to identify epidermis tissue from skin with varying degrees of histopathological damage. Combining enhanced colour information with general image intensity information, the fully automated methodology segments the epidermis with a mean specificity of 97.7%, a mean sensitivity of 89.4% and a mean accuracy of 96.5% and segments effectively for different severities of tissue damage. A set of 140 feature measurements containing information about the tissue changes associated with different grades of histopathological skin damage were identified and a wrapper algorithm employed to select a subset of the extracted features, evaluating feature subsets based their prediction error for an independent test set in a Naïve Bayes Classifier. The final classification algorithm classified a 169 image set with an accuracy of 94.1%, of these images 20 were an unseen validation set for which the accuracy was 85.0%. The final classification method has a comparable accuracy to the existing manual method, improved repeatability and reproducibility and does not require an experienced histopathologist