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/ / Automated Analysis of Fungal-Infected Tissue using Deep Learning

Automated Analysis of Fungal-Infected Tissue using Deep Learning

J.-P. Praetorius, C.M. Svensson, S. M.T. Figge

Image analysis IA-51

01/09/2019

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Verifying Hypotheses on Immune Evasion by Pathogens in Human Whole Blood by State-based Virtual Infection Models
Quantification of factor H mediated self and non-self discrimination by mathematical modeling

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Posters ASB-82458 Deep learning supported image analysis of angle-resolved scattered light images of bacteria in microfluidic droplets

ASB-82457 A spatio-temporal model for simulating hyphal growth of filamentous fungal species

IA-68 JIPipe: Designing automated image analysis
pipelines without programming

IbSB-13 Mcat: a multispectral cluster analysis toolkit for deep learning-based segmentation and clinical biomarker quantification of MSOT data

M-40 Modeling Candida albicans Complement Regulator Pra1

IA-67 Automated Characterization of Neutrophil Activation Phenotypes for Human Candida Bloodstream Infections

IA-62 AMIT: A high performance segmentation and tracking framework for migration and confrontation assays

IA-63 Automatic segmentation and classification of fungal-infected tissue using Deep Learning

IA-61 3D-HyTracer: Quantitative hyphal growth analysis through hyphae-to-graph transormation

IA-66 Machine learning supported image analysis of microfluidic droplets

IA-65 Algorithm for confrontation assay quantification (ACAQ): Label-free image analysis supported by Hessian filtering

IA-64 From raw images to abstract surface models: deconvolution, DeConvTest and DynSPHARM

IA-58 MISA++: A standardized interface for automated bioimage analysis

IA-57 JIPipe: Designing automated image analysis pipelines without programming

M-36 Virtual whole-blood infection assays

IbSB-12 Causal inference analysis of live cell imaged confrontation
assays with agent-based modelling

IbSB-10 Research Group Applied Systems Biology

IbSB-9 Leibniz ScienceCampus InfectoOptics - Combating infectious diseases with advanced optical methods

IA-60 Mcat: A toolkit for deep learning-based segmentation and automated analysis of MSOT image data

IA-59 Methods for quantitative analysis of bone degradation and remodeling from μCT images

M-37 Studies on virtual whole-blood infection assays

M-39 Design of an optimal COVID-19 surveillance protocol for child care facilities using an infection spread model

M-35 Comparative simulations of fungal infection dynamics in the human and murine lung

M-38 DynaCoSys: Dynamic model of the complement system to understand pathogen immune evasion

M-34 Hybrid agent-based modeling framework to simulate A. fumigatus infection scenario in the lung

IA-56 JIPipe: Designing automated image analysis pipelines without programming

IbSB-11 Virtual phagocytosis assays reveal strain‐speciﬁc diﬀerences in the microscopic parameters of the interaction between alveolar macrophages and two A. fumigatus strains

IA-54 MSOT cluster analysis toolkit to objectively assess pharmacokinetics and biodistribution of clinical biomarkers

IA-52 MISA++: a standardized interface for automatedhigh-performance big volume image analysis

IA-55 Quantitative image analysis of label-free cells

IbSB-8 Virtual phagocytosis assays reveal strain‐speciﬁc differences in the microscopic parameters of the interaction between alveolar macrophages and two A. fumigatus strains

IA-53 Morphokinetic analysis of live-cell imaging data

M-31 Simulation of virtual phagocytosis assays with alveolar macrophages and Aspergillus fumigatus conidia

M-33 Quantification of the dynamics of fungal confrontation assays

M-32 Quantification of factor H mediated self and non-self discrimination by mathematical modeling

IA-51 Automated Analysis of Fungal-Infected Tissue using Deep Learning

M-30 Verifying Hypotheses on Immune Evasion by Pathogens in Human Whole Blood by State-based Virtual Infection Models

IA-50 DeconvTest: an in silico microscopy framework to evaluate the accuracy of deconvolution

M-28 Quantitative Simulations Predict Treatment Strategies Against Fungal Infections in Virtual Neutropenic Patients Fungal Infections in Virtual Neutropenic Patients

IA-49 B4: Cause - Effect Relationships in Confrontation Assays with C. glabrata and Human Neutrophils using Bayesian Networks

M-29 Agent-based modeling of early Aspergillus fumigatus infection in mice

M-27 Mathematical model of the factor H mediated self and non-self discrimination by the complement system

IbSB-4 Simulation of virtual phagocytosis assays with alveolar marcrophages and Aspergillus fumigatus conidia reveals immune reaction rates

IA-44 Towards an open high-performance platform for fully-automated analysis of whole organ light-sheet fluorescence microscopy data

IA-37 DeconvTest: An in silico microscopy framework to evaluate the accuracy of deconvolution

IA-40 ACAQ: a Fiji and R toolkit targeted for automated confrontation assay quantification

IA-43 Measured and simulated 3D spatial distribution of plasma cells in the mouse bone marrow

M-22 Virtual Infection Modeling of the Early Immune Response to Fungal Infections in Murine Whole Blood

IA-42 Combining whole-blood infection assays with life-cell imaging to identify morphokinetic parameters for infection classification

M-24 Mathematical model of the factor H mediated self and non-self discrimination by the complement system

M-25 Agent-based modeling of early Aspergillus fumigatus infection in mice

IA-33 Fast and efficient fully automated processing of lightsheet microscopy data to evaluate the glomerular number in kidneys

IA-34 Automated image analysis methods for the quantification of cell damage and adherent fungal cells

M-21 Predictive Virtual Infection Modeling of Fungal Immune Evasion in Human Whole Blood

M-26 Agent-based modeling of early Aspergillus fumigatus infection in mice

IA-41 Joint Intravital Microscopy and Imaging: a multi-institute collaborative platform to aid intravital microscopy

IA-36 Cell tracking of Neutrophils in Phagocytosis Assays

IA-35 Image Processing in Droplet Microfluidics

IA-39 Quantitative analysis of intracellular communication from life cell imaging

IA-47 Analysis of Dynamic Properties in Biological Systems from Image Data

IA-32 Machine learning supported image analysis of microfluidic droplets: Using Random Forest classifiers and Bayesian inference for identification of experimental conditions

IbSB-2 ICH SEHE WAS, WAS DU NICHT SIEHST: BILDANALYSE UND COMPUTERSIMULATIONEN FÜR DIE INFEKTIONSDIAGNOSTIK

IA-38 Lang Nacht der Wissenschaften - Lichtblattfluorescencemikroscopie (LSFM)

M-15 Predictions of antimicrobial treatment strategies based on in silico experiments of virtual neutropenic patients

IA-27 Morphokinetic analysis of neutrophils from whole-blood infection assays

M-19 Quantification of the innate immune function in whole-blood infection assays reveals pathogen-dependent immune defence of different sepsis phases

M-20 Quantification of Innate Immune Function in Whole-blood Infection Assays

M-13 Agent-based modeling of early Aspergillus fumigatus infection in mice

IA-29 Automated Image Analysis of the Interaction between Lichtheimia spp. and Alveolar Macrophages

IA-30 Multispectral optoacoustic tomography to assess liver and kidney function in sepsis by measuring dye clearance

M-14 Predictive Virtual Infection Modeling of Pathogenic Immune Evasion in Human Whole-blood

M-23 Quantitative virtual infection modeling in sepsis

IA-25 Automated tracking and characterization of cell dynamics for classifier models

M-12 Quantitative mathematical modelling of the alternative pathway of the complement system

M-16 In silico Experiments of Fungal and Bacterial Infections in Virtual Neutropenic Patients suggest Optimal Treatment Strategies

IA-45 Enhanced Detection of Unstained Cells in an Existing Segmentation and Tracking Framework

IA-28 Migration and Interaction Tracking for Quantitative Analysis of Phagocyte‐Pathogen Confrontation Assays

M-17 QUANTIM- Quantification of Innate Immune Function in Whole Blood Infection Assay

IA-26 Automated tracking and characterization of cell dynamics for classifier models

IbSB-6 B4: Image data analysis and agent-based modelling of the spatio-temporal interaction between immune cells and human-pathogenic fungi

IA-31 In vivo quantification of arthritis-induced alterations of murine bones based on PET/CT image data

IA-20 Enhanced Detection of Unstained Cells in an Existing Segmentation and Tracking Framework

IA-18 Track-based analysis of cell migration

M-18 Dimensionality of motion and binding valency govern receptor-ligand kinetics as revealed by agent-based modelling

M-11 Investigating Candida albicans resistance in whole-blood assays by virtual infection models using parallelized parameter estimation

IA-15 Morphological restoration: A fast alternative to deconvolution of cells in 3D images

IA-19 Enhanced Detection of Unstained Cells in an Existing Segmentation and Tracking Framework

IA-24 Striped Illumination Microscopy with an enhanced iterative processing algorithm provides a novel high-resolution deep-tissue imaging technique

IA-17 Automated enumeration of circulating tumor cells: machine learning techniques and interobserver variability

IA-16 Combined [18F]-Fluoride Imaging in Experimental Arthritis

IA-23 Image Segmentation Methods for the Quantification of C. albicans cells in Fluorescence Images

M-10 Virtual infection model for Aspergillus fumigatus in human alveoli

IA-22 Image Segmentation Methods for the Quantification of C. albicans cells in Fluorescence Images

IA-21 AMIT: a novel algorithm for migration and interaction tracking for high-throughput analysis of phagocytosis assays

IbSB-3 Angewandte Systembiologie

IA-13 Automated Characterization of Cell Tracks: Step forward

M-9 Simulation of Immune Cell Deficiencies in a State-based Virtual Infectin Model of Human Whole-blood Assays for Candida albicans

M-8 Investigation of spatial dynamics of fungal-host-interactions of Candida albicans and innate immune cells

IA-14 Automated Image Analysis for Quantifying Fungus-host Interactions

M-7 Hybrid agent-based modeling of human alveoli reveals chemokine properties in Aspergillus fumigatus infection

M-5 Agent-based Modeling on Cellular and Molecular Scales

M-6 Virtual Infection Models of Candida albicans in Whole Blood

IbSB-1 Epithelial Invasion Outcompetes Hypha Development During Candida albicans Infection as Revealed by an Image-based Systems Biology Approach

M-4 A hybrid agent-based modeling approach of Aspergillus fumigatus infection in the human alveolus

IA-7 Automated Detection of Circulating Tumour Cells

IA-10 Automated Image Analysis of Microfluidic Droplets

IA-8 Automated image analysis of the host-pathogen interaction between immune cells and human-pathogenic fungi

IA-5 Image analysis of immune cell interactions from two-photon microscopy dat

IA-6 In vivo Quantiﬁcation of Arthritis Severity in a Murine Model by µCT Imaging

IA-11 Quantitative image analysis of cell colocalization in murine bone marrow

IA-12 Automated characterization of cell tracks based on local migration behavior

IA-9 Comparative Study of Phagocytosis-assays for Lichtheimia Species by Automated Analysis of Fluorescence Microscopy Images

IA-4 Automated characterization of cell tracks based on local migration behavior

M-3 Virtual infection model of the immune response against Candida albicans in human whole-blood assay

IA-3 Quantiﬁcation of Arthritis Progression From CT Images

IA-2 Automated Detection of Circulating Tumour Cells

IA-46 Image Processing in Droplet Microfluidics

M-2 Parameter Estimation by Simulated Annealing for Models of Whole-Blood Infection Assays with Candida Albicans

M-1 A Spatio-Temporal Modeling Framework to Simulate Host-Pathogen Interaction

IA-1 Single Cell Track Analysis of Two Photon Microscopy on Th17 Cells in the Gut

IA-48 Automated Image Analysis of the Interaction between Phagocytes and Aspergillus fumigatus during infection

IbSB-5 Quantitative description of fungal morphology by automated image analysis

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