Ongoing projects

Radiology and pathology join forces through Artificial Intelligence for Integrated Diagnostics (AIID)

Imaging infrastructure for the COllaboration for New TReatments of Acute Stroke Consortium

A European-wide foundation to accelerate data-driven cancer research.

Building a Data-Driven Future for Cancer Care

Advanced MRI, histopathology and artificial intelligence (AI) for glioma diagnosis

ICAI Stroke Lab: From 112 to rehabilitation

To improve the quality of MRI-based diagnosis with trustworthy AI methods.

Towards making cardiovascular disease risk predictions available for patients in digital health environment (PGO).


Prenatal image analysis

Early diagnosis of dementia in first-line care

Timely, accurate and personalized diagnosis of dementia

A benchmark dataset and optimized machine learning methods for MRI-based diagnosis of solid appearing liver lesions

Improved diagnostic work-up for patients with soft-tissue tumors using AI

An artificial intelligence (AI)-based model for detection of incidental pulmonary embolism in chest CTs

Finished projects

Development of novel morphological measurements on 3D bone models

3D modeling and visualization for surgical applications

4D ultrasound for improved image guidance in minimally invasive interventions

Accurate: Automatic CTA Image Analysis to Support Treatment Selection in Acute Stroke

COPD Machine Learning Datasets

Methodological Approaches for Craniofacial Shape Analysis

Improved image guidance for CT-guided liver ablations

CT-US Fusion for RFA

Towards a European cancer imaging platform for enhanced Artificial Intelligence in oncology.

Improved image guidance in liver interventions: TACE

IMAGIC - Intelligent image guidance in cardiac interventions

US-based vertebral level localization for hernia surgery

Personalized prostate cancer management using multi-parametric MRI and Machine Learning.

Quantitative Microvasculature AssEssment in projection angiography of ischemic STROke patients

Aims at improving surgical proceedures by visualizing the preoperative 3D data directly overlaid on the patient