Funded Projects

Ongoing and completed projects

Ongoing

  • KIRAL (BMFTR): KI-gestützte Resektionsplanung und Anpassung in der Leberchirurgie nach neoadjuvanter Therapie 11/25 – 11/28
  • PEACOCC – German Cancer Aid (Deutsche Krebshilfe): 04/25 – 04/28: PEACOCC – Innovative and patient-specific visualization techniques for pre-operative optimization in oncologic surgery for perihilar cholangio-carcinoma
  • AI4IA  – Co-PI of DFG (SPP 2311) AI4IA project – Automated Segmentation and DIscrimination for Intracranial Aneurysms
  • DFG LIZARD 02/25 – 02/28: Liver Resection Zone Prediction Using Image-Based and Geometric Deep Learning
  • IDIR – Iniative for Digital Implant Research (financed by CAU Kiel University, University Hospital Schleswig-Holstein (UKSH) and Helmholtz Zentrum Hereon) 08/24 – 02/28; 2 phd positions;

Completed

  • DFG (SPP 2311) SCALE – Multi-scale coupling of vascular hemodynamics for AI-based standardized evaluation of neurological pathologies;
    Duration 10/21-10/24; Project:  https://www.spp2311.uni-stuttgart.de/en/ 
  • DFG GEPARD – GEfäßwandsimulation und -visualisierung zur Patientenindividualisierten Blutflussvorhersage für die intrakranielle Aneurysmamodellierung;
    Duration 05/19 – 05/22; Project: https://gepris.dfg.de/gepris/projekt/399581926 
  • Adaption einer virtuellen Aneurysmaexploration für den Einsatz in der klinischen  Praxis;
    Duration 10/21 – 03/22, Funder:EFRE – Land Sachsen-Anhalt, Europäischer Fonds für regionale Entwicklung
  • Deep-Learning basierte Extraktion von Fett- und Muskelmasse für die patientenspezifische onkologische Therapieplanung;
    Duration 01/22-03/22, Funder: EFRE – Land Sachsen-Anhalt, Europäischer Fonds für regionale Entwicklung
  • Wahrnehmungsbasierte Blutflussvisualisierung für die patientenspezifische Behandlungsoptimierung multipler Aneurysmen;
    Duration 04/17 – 09/18, Funder:EFRE – Land Sachsen-Anhalt, Europäischer Fonds für regionale Entwicklung
  • Sensitivitätsanalyse klinisch verwendeter Rekonstruktionskernel für Rotationsangiographien;
    Duration 03/17 – 03/19, Funder: EFRE – Land Sachsen-Anhalt, Europäischer Fonds für regionale Entwicklung

Specific Information

Project KIRAL

  • BMFTR-funded project KIRAL – AI-based resection planning and adaptation in liver surgery after
    neoadjuvant therapy
  • Cooperation partners with different expertises: AI-based image analysis specialists from the University Hospital Schleswig-Holstein (UKSH; AI in medical applications group), visualisation and AR/VR experts from Otto-von-Guericke-University of Madgeburg (OVGU) and 3DQR GmbH, surgeons from UMM Mainz, as well as data analysts from GENIE.
  • The UKSH project contribution focuses on AI-driven image processing for individualized resection planning. For each patient, a personalized 3D model including all relevant anatomical structures is generated to predict the optimal resection plane. By incorporating various factors—such as patient-specific anatomy, precise assessments of liver function, and response to neoadjuvant therapies—the risk of PHLF can be minimized and patient outcomes improved. Intraoperatively, IOUS enables necessary adjustments to the preoperative plan, which can be implemented
    with AI support, while XAI methods ensure that the suggested modifications are transparent and comprehensible for the surgical team.

Project AI4IA

  • In September 2024, an interdisciplinary research team from the Research Campus STIMULATE together with Otto-von-Guericke-University Magdeburg, Charité Universitätsmedizin Berlin and University Hospital Schleswig Holstein started joint research work in the AI4IA project (Automated Segmentation and DIscrimination for Intracranial Aneurysms). The main objective of the AI4IA project is to identify and reduce sources of uncertainty in the determination of morphological and hemodynamic parameters in order to enable translation into healthcare.
  • Ruptured brain aneurysms are the main cause of cranial hemorrhage, which can lead to severe disability or death. The treatment of aneurysms that have not yet ruptured, which occur comparatively frequently, presents a challenge, as there are numerous uncertainties regarding the individual prognosis, the optimal treatment strategy and possible treatment complications. Determining the shape of the aneurysm (morphology) and the blood flow in the aneurysm (hemodynamics) can contribute to risk assessment. However, the determination of these parameters has not yet been widely used clinically due to a lack of robustness.
  • The project is designed as a first step towards the development of robust rupture risk prediction systems and is planned to last 36 months as part of the second DFG priority program phase (SPP2311). It is planned as a cooperation project between the research groups in Berlin and Magdeburg specializing in neurovascular issues, with each research group being supported by a clinical partner site.
  • The research groups have many years of experience in modelling brain aneurysms, while the clinical partner sites have corresponding clinical expertise and provide geometries of real cases in pseudonymized form. In this context, the cooperation partners focusing on medical flows (PD Philipp Berg), neuroradiological evaluation (Prof Daniel Behme) and AI-based image analysis (Prof Sylvia Saalfeld) will work closely together.