Revision as of 14:55, 18 September 2009

Audience

• Researchers in academic & industrial laboratories who make active use of optical microscopes, and desire to make image-based quantitative measurements for hypothesis testing, screening, and modeling
• Particular emphasis is placed on fluorescence based multi-dimensional microscopy (3-D confocal/multi-photon, time-lapse, multi-channel, etc.)

Instructional Methodology

This course will provide instruction on biological image analysis by combining three kinds of sessions:

• Concepts / theory presentations
• Hands-on practical exercises
• participant and guest presentations

Learning Objectives

• Upon successful completion of this course, the participants will have a working knowledge of basic open-source computational image analysis tools (FARSIGHT, ITK, VTK, Paraview, Overview) to generate quantitative measurements from 3D/4D/5D microscopy image data.

• Developing skills on the use of a selected subset of computational methods from the ITK and FARSight libraries.

• Developing awareness of more advanced tools that are part of the ITK and FARSight libraries, as well as the support resources available such as email forums and Wiki pages.

• Specifically,
  • Ability to read, visualize and write images files for various common formats.
  • Ability to design computational scripts for image pre-processing to correct for common imaging artifacts (noise, blur, spectral overlap).
  • Being able to segment common types of biological objects (cell, nuclei, membranes, micro-vasculature, neurites, foci) in 2D and 3D images of cells and tissue.
  • Being able to perform edit-based validation of segmentation results.
  • Being able to perform registration and montaging.
  • Being able to perform cell and organelle tracking, and analysis of changes in time-lapse image series.
  • Being able to compute morphological measurements of biological objects.
  • Being able to compute associative measurements linking two or more biological objects.
  • Being able to summarize and conduct statistical analysis of measurements.

Pre-requisites

• Familiarity with contemporary computers PC/Mac/Linux
• Basic knowledge of programming using scripting languages
  • We will use Python
• Familiarity with common viewing tools and file formats

Content

Day 1

Morning

• 8:00am-9:00am Introduction and Overview (B.R.)
• 9:00am-10:00am Programming environment and data visualization (L.I.)
• 10:00am-10:30am Break
• 10:30am-12:00pm Hands-on session 1
  • Reading / Writing images from different file formats (C.R.)
  • Visualization methods
• 12:00pm-1:00pm Working Lunch
  • Two participant presentations (15 min each)

Afternoon

• 1:00pm-1:20pm More Python basics (L.I.)
• 1:20pm-2:00pm Hands-on exercises on pre-processing, segmentation and visualization using Python-wrapped ITK (L.I.)
• 2:00pm-2:30pm Break
• 2:30pm-3:10pm Fundamental of image processing (B.R.)
• 3:15pm-5:00pm Hands-on session
  • Pixel level image processing
    • Grayscale morphology
    • Image smoothing, denoising
    • Deconvolution (C.P).
• 6:00-8:00pm Dinner
  • Three participant presentations (15 min each)

Day 2

Morning

• 10:00am-10:30am Break

• 12:00pm-1:00pm Working Lunch

Afternoon

• 2:00pm-2:30pm Break

Day 3

Morning

• 10:00am-10:30am Break

• 12:00pm-1:00pm Working Lunch

Afternoon

• 2:00pm-2:30pm Break

Day 4

Morning

• 10:00am-10:30am Break

• 12:00pm-1:00pm Working Lunch

Afternoon

• 2:00pm-2:30pm Break

Day 5

Morning

• 10:00am-10:30am Break

• 12:00pm-1:00pm Working Lunch

Afternoon

• 2:00pm-2:30pm Break