Paul Jürß, M.Sc.

Universitätsklinikum Hamburg-Eppendorf (UKE)
Sektion für Biomedizinische Bildgebung
Lottestraße 55
2ter Stock, Raum 210
22529 Hamburg

Technische Universität Hamburg (TUHH)
Institut für Biomedizinische Bildgebung
Gebäude E, Raum 4.044
Am Schwarzenberg-Campus 3
21073 Hamburg

Tel.: 040 / 7410 25811
E-Mail: paul.juerss(at)tuhh.de
E-Mail: p.juerss(at)uke.de
ORCID: https://orcid.org/0000-0002-3475-8480

profile picture of Paul Jürß

Research Interests

  • Image Reconstruction
  • Machine Learning

Curriculum Vitae

Paul Jürß is a PhD student in the group of Tobias Knopp for Biomedical Imaging at the University Medical Center Hamburg-Eppendorf and the Hamburg University of Technology. In 2020, he graduated with a bachelor's degree in Computer Science in Engineering at the Hamburg University of Technology. From 2020 to 2022, he studied Technomathematics at the University of Hamburg and obtained his master's degree with a thesis on "Compensation of motion artifacts in HR-pQCT".

Conference Proceedings

[191962]
Title: TrainingPhantoms.jl: Simple and Versatile Image Phantom Generation.
Written by: P. Jürß, C. Droigk, M. Boberg, and T. Knopp
in: <em>International Journal on Magnetic Particle Imaging</em>. Mar (2025).
Volume: <strong>11</strong>. Number: (1 Suppl 1),
on pages: 1-2
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI: https://doi.org/10.18416/IJMPI.2025.2503031
URL: https://www.journal.iwmpi.org/index.php/iwmpi/article/view/802
ARXIVID:
PMID:

[www]

Note: inproceedings, opensoftware, generalsoftware

Abstract: Large collections of labeled data play a crucial role in supervised machine learning projects. Unfortunately, such datasets are quite rare in the medical domain. In this work, the Julia project TrainingPhantoms.jl is introduced, which provides a simple interface to generate large and diverse collections of randomly generated image phantoms. The proposed phantom generator has been successfully used to train an image quality enhancement network that managed to generalize to unseen experimental out-of-distribution data.