Joshua Scheuplein

Researcher

Department of Computer Science
Chair of Computer Science 5 (Pattern Recognition)

Room 09.153
Martensstr. 3
91058 Erlangen

Research Topic: Metal Artifact Avoidance in Intraoperative CBCT Imaging

Research Group: Inverse Problems and Applications

Cooperation Partner: Siemens Healthineers AG, Forchheim

Since 02/2025:
PhD candidate at the Pattern Recognition Lab in cooperation with Siemens Healthineers
Topic: “Metal Artifact Avoidance (MAA) in Intraoperative Cone-Beam CT (CBCT) Imaging”
10/2022 – 01/2025:
Master of Science, Advanced Signal Processing and Communications Engineering (ASC)
Master’s Thesis: “Development of a Foundational Feature Extraction Model for Medical X-Ray Images”
10/2018 – 09/2022:
Bachelor of Science, Elektrotechnik – Elektronik – Informationstechnik (EEI)
Bachelor’s Thesis: “Analysis of ECG-derived Respiration for Use in Transcutaneous Vagus Nerve Stimulation (tVNS) Device”

Student	Type	Title	Status
Maeen Alikarrar	Master Thesis	Towards Domain Adaptation of Foundational Models in Medical Imaging	running
Anindya Banerjee	Master Thesis	Deep Learning-based Classification of Body Regions in Intraoperative X-Ray Images	running
Shadi Khamseh	Master Thesis	Development of an AI-Based Algorithm for the Correction of Moiré Artifacts in Digital Radiography	running
Anne Leipertz	Master Thesis	Deep Learning-based Orientation Estimation in Intraoperative X-Ray Images	running
Tobias Riß	Master Thesis	Prediction of CBCT Image Quality based on Fluoro Scout Images	running
Maximilian Rittler	Master Thesis	Investigating DETR Architectures as Implant Detection Model for Metal Artifact Avoidance Application	running
Yang Yuan	Master Thesis	Investigating YOLO Architectures as Implant Detection Model for Metal Artifact Avoidance Application	running

Academic CV