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Automated live cell microscopy and computational image analysis for high content screening

Organizer:

Daniel Gerlich

Background:

Automated live cell microscopy and computational image analysis are powerful tools to study cellular phenotypes in high throughput/high content loss-of-function screens. In this workshop, we aim to provide the theoretical background on image analysis and machine learning methods and present computational scripting approaches towards microscope automation. We will then perform practical training with small groups of students on laser scanning confocal microscopes and on widefield epifluorescence screening microscopes and analyze the data.

Objectives:
  • To inform students about computational image analysis methods and software packages
  • To provide guidelines on experimental design for image-based screening
  • To perform practical training in automated microscopy
  • To practice efficient use of computer vision and machine learning methods

 

Contents:

Part 1: Theoretical background on image analysis methods

  • Image segmentation and object detection
  • Statistical feature extraction for quantification of shape and texture
  • Object tracking over time and motion analysis
  • Phenotype classification by supervised machine learning

Part 2: Introduction to microscope automation

  • Metamorph platform for widefield epifluorescence screening microscopes
  • Micronaut platform for laser scanning microscopes

Part 3: Practical session on automated microscopy

  • Live cell imaging with laser scanning microscope
  • Live cell imaging with widefield epifluorescence screening microscope

Part 4: Practical session on image analysis

  • Image analysis with CellCognition platform
  • Integrated workflows using Fiji, Cellprofiler, and R

MFPL

 

 

UniVie

 

 

GMI

 

 

IMBA Logo

 

 

FWF