Clinical Motivation and Objective
Patients with cancer metastases often have multiple CT scans taken of them over time, so that the progression of the disease can be tracked. However, manually tracking and measuring lesions is both a labor-intensive and time-consuming task. AI models may help in reducing radiologists' workload by providing assistance in longitudinal lesion tracking and follow-up diagnosis.
To effectively develop and train these AI models, human-annotated longitudinal CT scans are required. This position involves performing these annotations on a provided dataset of longitudinal CT scans. You will learn to work with the Grand Challenge platform to annotate, and gain insight into the process of tracking lesions across the whole body, among other types of annotations.
Requirements
- Master students with a major in medicine, biomedical engineering, technical medicine, or a related area in the final stage of master's studies
- Experience with medical imaging, and/or medical image analysis
- Interested in learning more about medical imaging and annotation workflows
Practical Information
Project Duration: Around 5 hours per week for a period that is flexible, based on your availability and the exact type of annotations to be performed, starting May 2025.
Location: Department of Medical Imaging, Radboud University Medical Center, Nijmegen. Working from home is also possible.
For more information, please contact Alessa Hering.
Application: Please send a short motivation letter to Alessa.Hering@radboudumc.nl .
