Introduction to QuPath - 4th Edition (PPBI)

21-23 May 2025 | Hybrid

This course intends to give a brief introduction (Theoretical & Practical) to the use of QuPath (free open-source software for "Quantitative Pathology") for analysis of tile & stitched large image datasets, such as those obtained at slide scanners from the Histopathology and Bioimaging Units.

The course will include:

1. Introduction to image analysis basic concepts (21st May)
2. Introduction to QuPath software (22nd-23rd May)
3. Data visualization and representation
4. Image segmentation
5. Classification with machine learning
6. Brief introduction to deep learning with QuPath extensions and automatization with groovy workflows
7. Exercises

Requirements:

• Students must bring their own laptop, transformer, mouse and must have had previous contact with ImageJ/FIJI at a basic level.
• Install QuPath link >>

Poster >> 

Day 1 | 21^st May

Session I - Introduction to image analysis

14h00 - 14h15 Presentation of PPBI

14h15 - 14h55 What is an Image?

14h55 - 15h35 Types of images, colormaps and stain vectors

15h35 - 15h50 Coffee Break

15h50 - 16h30 Concepts of thresholding, ML and DL

16h30 - 17h00 QuPath in the world of pathology

Day 2 | 22^nd May

Session II - Introduction to QuPath

09h30 - 10h15 How to handle images on QuPath?

• Image properties
• Create a project

10h15 - 11h15 Tools - Annotations

• Types of ROIs available and how to use them
• Hierarchy
• Properties and Classes
• Calculating features
• Exercises

11h15 - 11h30 Coffee Break

11h30 - 12h30 Tools - Detections

• Cell detection
• Properties, Measurements and tips
• Positive Cell Detection
• Exercises

12h30 - 14h00 Lunch Break

Session III - Brightfield Images

14h00 - 14:45 Stain Vectors

• Setting a stain vector
• Estimating a stain vector

14h45 - 15h45 Pixel Classification

• Tissue detection
• Create and measure objects
• Exercises

15:45 - 16:00 Coffee Break

16:00 - 16:30 Object Classification

• Training an object classifier (machine learning)

16h30 - 17:30 Density Maps

• Creating a density map
• Finding hotspots
• Creating annotations based on density

Day 3 | 23rd May

Session IV - Fluorescence Images

09h30 - 10:30 Multiplexed analysis

• Visualization of multiple channels
• Cell Detection
• Creating a cell classifier
• Exercises

10h30 - 11h30 Object Classification

• Training an object classifier (machine learning)
• Training images
• Composite classifiers
• Improving training
• Heatmaps

11h30 - 11h45 Coffee Break

11h45 - 12h15 Pixel Classification

• Training a pixel classifier (machine learning)
• Creating objects based on classifier

Session V - Automated workflows

12h15 - 13:00 Introduction to groovy scripting

• Automated scripts
• Extensions - Stardist and Cellpose (deep learning)

13h00 - 14h00 Lunch Break

Session VI - Call4Help (Optional)

14:00 - 16:00 Working on your images

• wrap-up
• call4help

Speakers

Theoretical
Luisa Cortes, CNC-UC
Bruno Monteiro, i3S
Teresa Rodrigues
Anna Pezzarossa, Champalimaud Foundation
Pedro Faísca, GIMM

Practical
Patrícia Rodrigues, GIMM

Trainers:

Lisbon node:
Ana Beatriz Barbosa, University of Medicine of Lisbon
Anna Pezzarossa, Champalimaud Foundation
Diogo Coutinho, University of Medicine of Lisbon
Telmo Pereira, NOVA Medical School

Coimbra node:
Luisa Cortes, CNC-UC
Margarida Caldeira, CNC-UC
Tatiana Catarino, CNC-UC

Porto node:
Paula Sampaio, i3S
Maria Azevedo, i3S
María Lázaro, i3S
Dalila Pedro, i3S

Covilhã node:
Ana Raquel Costa, Faculdade de Ciências da Saúde - UBI
Catarina Ferreira, Faculdade de Ciências da Saúde - UBI

Cost: Free

Application deadline: 11 May 2025

Notification of acceptance: 12 May 2025

The course is full.

More information:
Advanced Training Unit | E-mail: training@i3s.up.pt | Tel: +351 226 074 900