Image Analysis II

1-3 October 2024
Milan

Organizers: Emanuele Martini & Chiara Soriani

Dates: 1-2-3 October 2024

Venue: IFOM, Meeting Room 1-2

The aim of this course is to help students acquire new skills in the analysis of microscopy images using advanced image analysis tools. Through theoretical and practical lessons, the course will cover topics related to python/jython programming in Fiji and the application of machine learning and deep learning algorithms for the analysis of microscopy data.

Timetable
9.00-12.30
Lesson Argument

Introduction to Python/Jython programming languages:

  • From ImageJ macro language to Jython programming language in Fiji
  • Python basics: syntax, statements, expressions, list, loops, conditional statements, functions
  • From Python to Jython: object oriented programming
  • Jython main APIs and Classes for Fiji
  • Python vs Jython: Pros and cons

Hands-on: practice on a nuclei segmentation macro and comparison with Jython language

  • Implementation of nuclei segmentation
  • Focus on Regions Of Interest Manager
  • Creation of customized Results table
Teachers

Emanuele Martini, Fabrizio Orsenigo & Serena Magni,

Affiliations

IFOM

Lesson Location
IFOM - Meeting Room 1-2
Timetable
14.00-17.00
Lesson Argument

More complex image analysis and Analysis of Multiple Images

Hands-on: nuclei segmentation with spots count and intensity measurement

  • Bio-formats handling
  • User interaction
  • Multiple channel handling
  • Parent-children objects handling
  • Multiple Results Table management
  • Multiple images analysis
Teachers

Fabrizio Orsenigo & Serena Magni,

Affiliations

IFOM

Lesson Location
IFOM - Meeting Room 1-2
Timetable
9.00-12.30
Lesson Argument

Introduction to machine learning and deep learning in image analysis

  • Theoretical introduction about machine learning and deep learning
  • Machine learning as tool for semantic segmentation/pixel classification
  • Deep Learning as tool for image restoration and instance segmentation

Hands-on: Semantic segmentation/pixel classification with Labkit

Teachers

Damian Edward Dalle Nogare,

Affiliations

Human Technopole

Lesson Location
IFOM - Meeting Room 1-2
Timetable
14.00-17.00
Lesson Argument

Image restoration and instance segmentation using Deep Learning in Python

  • Introduction to Jupyter (Python) notebooks and their use in cloud with ZeroCostDL4Mic
  • Deep Learning algorithms for image restoration and instance

Hands-on: Image restoration and nuclear segmentation with Deep Learning algorithms

  • Image restoration using Noise2Void algorithm; model training and application using Jupiter notebooks in ZeroCostDL4Mic
  • Nuclear segmentation using 2D and 3D Stardist using Jupyter notebooks in ZeroCostDL4Mic
Teachers

Joran Deschamps,

Affiliations

Human Technopole

Lesson Location
IFOM - Meeting Room 1-2
Timetable
9.00-12.00
Lesson Argument

Segmentation in tissues using Stardist with QuPath software:

  • Introduction to QuPath for tissue sections analysis
  • Cell segmentation in tissues

Hands-on: Cell segmentation in tissues using Stardist and cell-type classification in different populations

Teachers

Chiara Soriani,

Affiliations

IEO

Lesson Location
IFOM - Meeting Room 1-2
Timetable
14.00-17.00
Lesson Argument

Cell segmentation using Cellpose software in Python:

  • Introduction to conda environments and their utility for using local Jupyter (Python) notebooks
  • CellPose Deep Learning software for cell and nuclear segmentation

Hands-on: Segmentation of cells and nuclei using CellPose and evaluation of cytoplasmic signal using local Jupyter notebooks

Teachers

Mattia Marenda,

Affiliations

IEO

Lesson Location
IFOM - Meeting Room 1-2