Radiomics
About the course
Target group
Physicians, Technicians
Key words
Course introduction
With this course we aim to improve skills and competences of medical professionals and technicians in clinical management based on medical imaging. 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 strategies to process digital imaging is opening new possibilities to make automatic image processing operations feasible and surprisingly fast. In order to use these new strategies for clinical management, physicians and technical staff must increase their skills and competences in this peculiar area. Using these tools, they can reduce the working time, obtained important new data for patient management and improve clinical outcome.
Details to know
Downloadable certificate
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Assessment
5 Quizzes
Learning outcomes
After successfully completing this Course, the learner will be to:
- Know the tools for image processing
- Know the software available for these task
- Know the procedure for image segmentation
- Know the procedure for structure quantification
- Know how to use radiological features to improve the use of clinical images and data
After successfully completing this Course, the learner will be able to:
- Visualize medical images
- Segment organs and tissue in automatic way
- Segment organs and tissue using AI algorithms
- Estimate volume dimensions and numerical data of segmented volumes
- Export data of surface and volume models
- Investigate data generated by medical image processing
More detailed Learning Outcomes can be found in module introductions.
Introduction
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.