CELLENGER CELLENGER Automated High Automated High Content - - PDF document
CELLENGER CELLENGER Automated High Automated High Content Content Analysis of Analysis of Biomedical Biomedical Imagery Imagery Dr. Maria Athelogou the cognitive computing company the cognitive computing company Founded in 1994 as
Founded in 1994 as Delphi 2 Creative Technlogies GmbH Founded by Prof. Dr. Gerd K. Binnig, Nobel Laureate in Physics 1986, and Dieter Herold, Science Journalist Headquartered in Munich, Germany
Image Image Understanding Understanding with with Cognition Cognition Networks Networks
Automated Image Understanding Automated Image Understanding
Find all relevant objects and their mutual relations
create a hierarchical linked object structure „segmentation“
Link these objects with the knowledge about them and their relations
knowledge representation hierarchical classification
Cellenger technology supports a GUI based meta language that allows for fast and efficient development of rule bases. A rule base addresses the solution of a specific image analysis task Basic components are processes and fuzzy classification that support knowledge based segmentation
Cellenger meta language for Image Understanding Cellenger meta language for Image Understanding
Classes with fuzzy class descriptions Locally specific processes
Network of objects of interest with attributes and mutual relations Result
Representation of Input Data in a Hierarchical Structure Representation of Input Data in a Hierarchical Structure
Input: image data noisy, textured, heterogeneous structures of interest Fuzzy knowledge base
Object segmentation and processing domain Semantics: fuzzy classification domain Result Network of
interest in proper shape with correct labeling
Methodical advantages Methodical advantages Extensive integration of processes and semantics Feature (Attribute) Feature (Attribute) Hierarchy Hierarchy
Object related
Color Form Texture
Classification related
z.B. Rel. Border Length to Objects of Class „Endothel“
Relations between Objects
Length of common border
Globale Attributes
Number of Objects of a Class Meta-Data
Variables
Class Class Hierarchy Hierarchy: : The The „ „Knowledge Knowledge Base“ Base“
describes
problem expert knowledge internal analysis classes
Inheritance Semantic Grouping Class Description and Classification by Fuzzy – Logik - Expressions
Internal Internal Class Class Description Description
Fuzzy – Membership Functions Integration of other Classificators
Adaptive local Processing by Domains Adaptive local Processing by Domains
each process consists of
algorithm domain
algorithm describes what happens domain describes where it happens intuitive support of
local processing adaptiv processing
Cellbased Cellbased Solutions Solutions for for Cellbased Cellbased Quantification Quantification
Fluoresce /RNAi (Cells):
Find Nuclei/Separate Nuclei/Classify Nuclei Find Cells/Separate Cells/Classify Cells Mutual relations between Cells and Nuclei Classify Cells accoding morphological properties
Her2Neu Membran Expression (Tissue):
Find Nuclei/Separate Nuclei/Classify Nuclei Find Cells/Separate Celles/Classify Cells Mutual relations between Cells and Nuclei Classify Cells according the membran expression of Her2Neu %
Benefit Benefit of
Cellenger
Request from the FDA / EMEA) additional Tox./ Histopath. – Data for the final submission Stack of approx. 15.000 images Based on state of the art technologies Expected 3 -4 Pathologists 6 months Automated, quantitative Analysis of cell & tissues Benchmark (Pathologist / Cellenger) Result within a few weeks Reduced the time from 6 to 3 months with 2 Pathologists Time to market benefit
Diagnostics
Cell&Tissue-based assays in R&D
Lead Optimization ADME/TOX Phase I-IV
Pre-Clinical Trials Clinical Trials Phase: Market approved Discovery
Disease Selection Target Identification Animal
Rodent Non-Rodent
Human
Control Patient
Lead Identification
It is generally recognized that cell & tissue-based assays which better mirror physiology and disease are the future of research and drug development in the life sciences sector
Detailed quantitative information in Detailed quantitative information in cells & tissues cells & tissues The bottleneck: The bottleneck: automated image analysis automated image analysis
Biomedical image data set a significant challenge for automated image analysis: current standard, pixel based procedures for image analysis fail A lot of manual intervention is needed for analysis expensive, subjective and time consuming: many projects cannot be driven Only rough and qualitative description is provided no detailed, quantitative description of cell and tissue structure
Based on the new object-based image analysis technology Cellenger overcomes this operational gap
Cellenger Solutions through through the the whole whole R&D R&D process process
Medical Diagnostics
Pre-Clinical Trials Clinical Trials R&D Phase: Discovery
Lead Optimization ADME/TOX Animal
Rodent Non-Rodent
Human
Control Patient
Phase I-IV Lead Identification
HT/HC Screening Histopathology CT/MR/US..
Disease Selection Target Identification
European Journal of Medical Research, Juli 2003 Definiens AG, ICF LMU, Klinikum Großhadern
Ultra structure research, Electron microscopy Ultra structure research, Electron microscopy
MPI-CBG/Cenix Dresden
Original 0 : her2Neu_174 Original 2 : her2Neu_177 Original 3 : her2Neu_179 DacoCytomation
Statistics Statistics
1122 232 39 4 more than 75 % membran expr her2/Neu 50% to 75% membran expr her2/Neu 25% to 50% membran expr her2/Neu less than 25% membran expr her2/Neu 109 360 79 9 more than 75 % membran expr her2/Neu 50% to 75% membran expr her2/Neu 25% to 50% membran expr her2/Neu less than 25% membran expr her2/Neu 3 2
more than 75 % membran expr her2/Neu 50% to 75% membran expr her2/Neu 25% to 50% membran expr her2/Neu less than 25% membran expr her2/Neu
Count of cells of different classes
Original 0 : her2Neu_174 Original 2 : her2Neu_177 Original 3 : her2Neu_179
KI-67-Pathology- Berlin
Pathology LMU Munich
Histology, Pathology: Histology, Pathology: I Inflamatory Areas in Liver Tissue
Image data courtesy Novartis Pharma AG; Pathology / Toxicology EU
Pathology / Toxicology: analysis of proliferation index in jejunum
Siemens Medical Erlangen
2D+T/3D/3D+T Cellenger Solutions
Brain Imaging Center, Universit at Frankfurt am Main
Brain Imaging Center, University at Frankfurt am Main
Cellenger Enterprise Cellenger Enterprise
modular client-server system designed to fully automate image analysis and report generation for biomedical solutions
integration Enterprise Client enables user access to all details of object-oriented image analysis results
Database: Database: Image Content Query System Image Content Query System
Storage of image data Scalable: few to large data amounts Administration: organize in experiments, groups, scenes, tiles .. Annotation ODBC protocol, support of Oracle (DB2, MS SQL ... ) Storage of results Case analysis and statistics per scene Case analysis and statistics per experiment Optional: morphometric data per scene Optional: network of extracted image objects
Business Business Benefit Benefit of
Cellenger
Automated, quantitative Analysis of cell & tissues Understanding of complex events, mechanism of action Decision support for non hit/ hit / potential hit (HCS/HTS) Reduces the # of screens Decision support for Toxicity / Efficacy (Tox./ Histopath.) Reduce the # of leads (fail earlier/ fail cheaper >30%)) Harmonisation benefit Time to market benefit Thank you for your attention Thank you for your attention
Image data courtesy MPI Cell Biology and Genetics Dresden