Radiomics
About the course
Target group
Physicians, Technicians
Key words
Course introduction
The aim of this course is to improve skill and competences of medical professionals and technicians in image based clinical management. Today there is an increased use of digital images for clinical purposes. New powerful scanners continue to increase the quality of medical imaging and reduce the acquisition time. The recent application of artificial intelligence (AI) strategies to process digital imaging is opening new possibility to make feasible automatic image processing operations. In order to use of these new strategies for clinical management, physicians and technical staff must increase their skill and competences in this area. Using these tools they can reduce the working time and improve clinical outcome.
Details to know
Downloadable certificate
Share your certificate on Linkedin
Assessment
5 Quizzes
Learning outcomes
Module 1
- Competence
- Is able to navigate and utilise medical imaging tools for effective image processing and segmentation.
- Knowledge
- Knows the nature and file formats of medical images.
- Understands the principles of image segmentation.
- Skills
- Identifies and manages DICOM medical image formats.
- Utilises DICOM viewers for image analysis.
- Applies segmentation techniques to extract relevant anatomical structures from medical images.
Module 2
- Competence
- Is able to apply digital image segmentation techniques to process and analyse medical images.
- Knowledge
- Knows how to use 3D Slicer for medical image visualisation and segmentation.
- Skills
- Utilises 3D Slicer tools for volume visualisation and segmentation.
- Stores and manages segmented volumes derived from radiological image processing.
Module 3
- Competence
- Is able to implement AI-assisted segmentation techniques to improve clinical image analysis.
- Knowledge
- Knows the structure of AI-based segmentation tools, including U-net.
- Skills
- Applies AI algorithms for automatic segmentation using 3D Slicer.
- Visualises AI-derived volume segmentation for clinical assessment.
Module 4
- Competence
- Is able to generate, visualise, and analyse 3D models from medical imaging data.
- Knowledge
- Knows the procedures for generating 3D objects from medical images.
- Skills
- Uses SimVascular for image segmentation and surface model generation.
- Generates and visualises volume models using ParaView.
- Exports surface and volume model data for further analysis.
More detailed Learning Outcomes can be found in module introductions.
Introduction to Radiomics
Lessons
Introduction 1. Introduction to Radiomics 2. Radiological ImagingImage Segmentation
Lessons
Introduction 1. Image Segmentation 2. Image Segmentation Using AIDigital Image Processing and Image Segmentation with AI
Lessons
Introduction 1. Image Segmentation and Volume Reconstruction 2. AI Based Segmentation 3. Volume QuantificationGeneration of 3D Digital Models
Lessons
Introduction 1. Introducing SimVascular 2. Blood Vessel Segmentation 3. 3D Model ReconstructionDeep Learning to Address Challenges in Radiomics
Lessons
Introduction 1. Radiomics Features in Kidney MRI I 2. Radiomics Features in Kidney MRI II Course Evaluation
Co-funded by the Erasmus+ programme of the European Union under Grant Agreement number 101056563.
Co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or EACEA. Neither the European Union nor the granting authority can be held responsible for them.