Bioimage informatics

Summary

The course provides a comprehensive overview of methods, algorithms, and computer tools used in bioimage analysis. It exposes fundamental concepts and practical computer solutions to extract quantitative information from multidimensional images, both using engineering methods and deep learning.

Content

Keywords

Bioimage, microscopy, image processing, image reconstruction, image analysis, visualization, multidimensional data analysis, machine learning, deep learning

Learning Prerequisites

Required courses

• Baisc knowledge in programming

Important concepts to start the course

• Basic knowledge in signal or image processing
• Baisc knowledge in programming

Learning Outcomes

• Identify quality of images in life science and expectation of the analysis
• Define the fundamental concepts of the computational bioimaging methods
• Select appropriately and compare methods and tools for common bioimage analysis tasks
• Design implements and experiment algorithms to solve specific tasks
• Develop a workflow for customized application
• Assess / Evaluate strategies for image-based experiments in life science

Transversal skills

• Demonstrate the capacity for critical thinking
• Use a work methodology appropriate to the task.
• Use both general and domain specific IT resources and tools

Assessment methods

20% Homework, individual

• In the first half of the semester: 4 homeworks on computer (2 weeks)

40% Mini-project by groups of 2-3 students

• In the second half of the semester: Development of an image analysis tool for a real application in biology

40% End-term exam, individual

• written exam with handwritten notes

Resources

Moodle Link

• https://go.epfl.ch/BIO-410