A computer system for the leukocytes classification in medical images

The paper is about the development of a computer system for the white blood cells images classification. Solving the leukocytes classification task makes it possible to diagnose not only the blood diseases, but also a wide range of other diseases, as well as to evaluate the overall state of a person's health. Modern leukocytes classification methods have a fairly large number of shortcomings, which make a problem of finding the most effective method as a tool to solve this task. In our computer system we use the method based on using a trained convolutional neural network as a binary classifier for the leukocytes classificatio

Numerical Insights for AAA Growth Understanding and Predicting: Morphological and Hemodynamic Risk Assessment Features and Transient Coherent Structures Uncovering

Les anévrismes de l'aorte abdominale (AAA) sont des dilatations localisées et fréquentes de l'aorte. En cas de rupture, seul un traitement immédiat peut prévenir la morbidité et la mortalité. Le diamètre maximal AAA ($D_{max}$) et la croissance sont les paramètres actuels pour évaluer le risque associé et planifier l'intervention, avec des seuils inférieurs pour les femmes. Cependant, ces critères ne sont pas personnalisés ; la rupture peut se produire à un diamètre inférieur et les patients vivre avec un AAA important. Si l'on sait que la maladie est associée à une modification de la morphologie et de la circulation sanguine, à un dépôt de thrombus intra-luminal et à des symptômes cliniques, les mécanismes de croissance ne sont pas encore entièrement compris. Dans cette étude longitudinale, une analyse morphologique et des simulations de flux sanguins sont effectuées et comparées aux sujets témoins chez 32 patients ayant reçu un diagnostic clinique d'AAA et au moins 3 tomodensitogrammes de suivi par patient. L'objectif est d'abord d'examiner quels paramètres stratifient les patients entre les groupes sains, à faible risque et à risque élevé. Les corrélations locales entre les paramètres hémodynamiques et la croissance de l'AAA sont également explorées, car la croissance hétérogène de l'AAA n'est actuellement pas comprise. Enfin, les paramètres composites sont construits à partir de données cliniques, morphologiques et hémodynamiques et de leur capacité à prédire si un patient sera soumis à un test de risque. La performance de ces modèles construits à partir de l'apprentissage supervisé est évaluée par les ROC AUC : ils sont respectivement de 0.73 ± 0.09, 0.93 ± 0.08 et 0.96 ± 0.10 . En incorporant tous les paramètres, on obtient une AUC de 0.98 ± 0.06. Pour mieux comprendre les interactions entre la croissance et la topologie de l'écoulement de l'AAA, on propose un worflow spécifique au patient pour calculer les exposants de Lyapunov en temps fini et extraire les structures lagrangiennes-cohérentes (SLC). Ce modèle de calcul a d'abord été comparé à l'imagerie par résonance magnétique (IRM) par contraste de phase 4-D chez 5 patients. Pour mieux comprendre l'impact de la topologie de l'écoulement et du transport sur la croissance de l'AAA, des SLC hyperboliques répulsives ont été calculées chez un patient au cours d'un suivi de 8 ans, avec 9 mesures morphologiques volumétriques de l'AAA par tomographie-angiographie. Les SLC ont défini les frontières du jet entrant dans l'AAA. Les domaines situés entre le SLC et le mur aortique ont été considérés comme des zones de stagnation. Leur évolution a été étudiée lors de la croissance de l'AAA. En plus des SLC hyperboliques (variétés attractives et répulsives) découvertes par FTLE, les SLC elliptiques ont également été considérées. Il s'agit de régions dominées par la rotation, ou tourbillons, qui sont de puissants outils pour comprendre les phénomènes de transport dans les AAA.Abdominal aortic aneurysms (AAA) are localized, commonly-occurring dilations of the aorta. In the event of rupture only immediate treatment can prevent morbidity and mortality. The AAA maximal diameter ($D_{max}$) and growth are the current metrics to evaluate the associated risk and plan intervention, with lower thresholds for women. However, these criteria lack patient specificity; rupture may occur at lower diameter and patients may live with large AAA. If the disease is known to be associated with altered morphology and blood flow, intra-luminal thrombus deposit and clinical symptoms, the growth mechanisms are yet to be fully understood. In this longitudinal study, morphological analysis and blood flow simulations for 32 patients with clinically diagnosed AAA and at least 3 follow-up CT-scans per patient, are performed and compared to control subjects. The aim is first to investigate which metrics stratify patients between healthy, low risk and high risk groups. Local correlations between hemodynamical metrics and AAA growth are also explored, as AAA heterogeneous growth is currently not understood. Finally, composite metrics are built from clinical, morphological, and hemodynamical data, and their ability to predict if a patient will become at risk tested. Performance of these models built from supervised learning is assessed by ROC AUCs: they are respectively, 0.73 ± 0.09, 0.93 ± 0.08 and 0.96 ± 0.10. Mixing all metrics, an AUC of 0.98 ± 0.06 is obtained. For further insights into AAA flow topology/growth interaction, a workout of patient-specific computational flow dynamics (CFD) is proposed to compute finite-time Lyapunov exponents and extract Lagrangian-coherent structures (LCS). This computational model was first compared with 4-D phase-contrast magnetic resonance imaging (MRI) on 5 patients. To better understand the impact of flow topology and transport on AAA growth, hyperbolic, repelling LCS were computed in 1 patient during 8-years follow-up, including 9 volumetric morphologic AAA measures by computed tomography-angiography (CTA). LCS defined barriers to Lagrangian jet cores entering AAA. Domains enclosed between LCS and the aortic wall were considered to be stagnation zones. Their evolution was studied during AAA growth. In addition to hyperbolic (attracting and repelling) LCS uncovered by FTLE, elliptic LCS were also considered. Those encloses rotation-dominated regions, or vortices, which are powerful tools to understand the flow transport in AAA

Die Integration von Multiskalen- und Multi-Omik-Daten zur Erforschung von Wirt-Pathogen-Interaktionen am Beispiel von pathogenen Pilzen

The ongoing development and improvement of novel measurement techniques for scientific research result in a huge amount of available data coming from hetero- geneous sources. Amongst others, these sources comprise diverse temporal and spatial scales including different omics levels. The integration of such multiscale and multi-omics data enables a comprehensive understanding of the complexity and dynamics of biological systems and their processes. However, due to the biologically and methodically induced data heterogeneity, the integration process is a well-known challenge in nowadays life science. Applying several computational integration approaches, the present doctoral thesis aimed at gaining new insights into the field of infection biology regarding host- pathogen interactions. In this context, the focus was on fungal pathogens causing a variety of local and systemic infections. Based on current examples of research, on the one hand, several well-established approaches for the analysis of multiscale and multi- omics data have been presented. On the other hand, the novel ModuleDiscoverer approach was introduced to identify regulatory modules in protein-protein interac- tion networks. It has been shown that ModuleDiscoverer effectively supports the integration of multi-omics data and, in addition, allows the detection of potential key factors that cannot be detected by other classical approaches. This thesis provides deeper insights into the complex relationships and dynamics of biological systems and, thus, represents an important contribution to the investigation of host-pathogen interactions. Due to the interactions complexity and the limitations of the currently available knowledge databases as well as the bioinformatic tools, further research is necessary to gain a comprehensive understanding of the complexity of biological systems