Jacopo Bracci

I am a doctoral researcher at the Pattern Recognition Lab, FAU Erlangen-Nürnberg, working in collaboration with Siemens Healthineers. I develop machine learning methods for medical image analysis, with a particular focus on vascular imaging and photon-counting CT. My research explores how we can turn the information in these scans into a detailed, quantitative understanding of blood vessels and their anatomy.

I am particularly interested in how models learn anatomical structure, how they can make better use of limited expert annotations, and how new imaging technologies can support automated analysis. These questions connect two important aspects of my work: representing anatomy accurately and reducing the manual effort needed to build and apply learning-based methods. The broader goal is to support more consistent assessment of vascular disease.

I also investigate how automated vascular analysis can be extended to non-contrast CT, where blood vessels are less clearly visible. This includes exploring how contrast-enhanced scans can support learning through image synthesis and accurate alignment between scans, while preserving the anatomical and tissue information needed for reliable assessment.

Working with both academic and industrial collaborators, I aim to develop methods that address practical challenges in medical imaging while remaining grounded in careful evaluation.