Group Lead

 

Prof. Dr. med. Meinrad Beer

Director of the Clinic for Diagnostic & Interventional Radiology, Ulm University Hospital


Profile

Prof. Dr. Meinrad Beer is the Medical Director of the Department of Diagnostic and Interventional Radiology at Ulm University. After his professorships in Clinical Radiology (University of Wuerzburg) and Pediatric Radiology (Medical University of Graz, Austria) he has been appointed chairman of the Radiology Department (Ulm University) in 2013.

 sekretariat.radiologie1@uniklinik-ulm.de

 

Prof. Dr. rer-nat. habil. Timo Ropinski

Head of the Visual Computing Research Group, Ulm University


Profile

Prof. Ropinski is head of the Visual Computing Group at Ulm University. Before moving to Ulm, he was Professor in Interactive Visualization at Linköping University in Sweden where he was heading the Scientific Visualization Group. Prof. Ropinski has received his Ph.D. in computer science in 2004 from the University of Münster, where he has also completed his habilitation in 2009.

 timo.ropinski@uni-ulm.de

 

Jun.-Prof. Dr.-Ing. Michael Götz

Head of the Section Experimental Radiology, Ulm University Hospital


Profile

Professor Götz is head of the experimental radiology section at Ulm University Hospital. Before moving to Ulm he worked as Postdoctoral researcher at the German Cancer Research Center (DKFZ) were he also received his Ph.D. in 2017.

 michael.goetz@uni-ulm.de


Medical Researchers

 

Prof. Dr. med. Stefan Schmidt

Deputy Director of the Clinic for Diagnostic & Interventional Radiology, Ulm University Hospital

Profile

 

Dr. med. Catharina Lisson

Senior Physician Diagn. & interv. Radiology, Head of Division Oncological Imaging / Thoracic Imaging, Ulm University Hospital

Profile

 

Dr. med. Wolfgang Thaiss

Senior Physician Nuclear Medicine & Diagn. & interv. Radiology, Scientific Director Core Facility PET/MR, Ulm University Hospital

Profile

 

Dr. med. Daniel Vogele

Senior Physician Diagn. & interv. Radiology, Head of Division Musculoskeletal radiology, Ulm University Hospital

Profile


Physics Researchers

 

Dr. rer. nat. Arthur Wunderlich

Diplomphysiker

Profile

 arthur.wunderlich@uniklinik-ulm.de


Computer Science Researchers

 

Hannah Kniesel

M.Sc. Computer Science, Research Associate / PhD Candidate, Ulm University


Profile

Reserach Focus:

  • Deep Learning Techniques for Biomedical Image Processing
  • Explainable AI
  • Deep Learning Techniques for Computer Graphics
  • Differentiable Rendering

 hannah.kniesel@uni-ulm.de

 

Daniel Wolf

M.Sc. Electrical Engineering, Research Associate / PhD Candidate, Ulm University


Profile

Reserach Focus:

  • Medical image analysis with Deep Learning
  • PreTraining with self-supervised learning (Contrastive Learning)
  • Data Augmentation

 daniel.wolf@uniklinik-ulm.de

 

Tristan Payer

M.Sc. Artificial Intelligence, Research Associate / PhD Candidate, Ulm University


Profile

Reserach Focus:

  • Medical image analysis with Deep Learning
  • PreTraining with self-supervised learning (Contrastive Learning)
  • Image Segmentation

 tristan.payer@uni-ulm.de

 

Faraz Nizamani

M.Sc. Medical Systems Engineering, Research Associate / PhD Candidate, Ulm University


Profile

Reserach Focus:

  • Medical image analysis with Deep Learning
  • Semisupervised learning

 faraz.nizamani@uni-ulm.de

 

Luisa Gallee

M.Sc. Computational Science and Engineering, Research Associate / PhD Candidate, Ulm University


Profile

Reserach Focus:

  • Medical image analysis with Deep Learning
  • Explainable AI

 luisa.galee@uni-ulm.de

 

Heiko Hillenhagen

M.Sc. Mechanical Engineering, Research Associate / PhD Candidate, Ulm University


Profile

Reserach Focus:

  • Medical image analysis with Deep Learning
  • Segmentation of PET/MRI images
  • Outlier/ Anomaly detection

 heiko.hillenhagen@uni-ulm.de


Students / Research Assistants

 

Gesa Mittmann

Masterthesis Computer Science

Focus: Deep Learning on MRI images

 

Christoph Meyer

Masterthesis Computer Science

Focus: Deep Learning on PET/CT and PET/MRI images

 

Sabitha Manoj

Research assistant

Focus: Machine Learning with Radiomics


Project Partners

 

Prof. Dr. med. Ambros J. Beer

Head of the Department of Nuclear Medicine, Ulm University Hospital

 

Prof. Dr. Hans Kestler

Head of the Department of Medical Systems Biology, Ulm University

Publications

 

Deep Neural Networks and Machine Learning Radiomics Modelling for Prediction of Relapse in Mantle Cell Lymphoma

Example image

Example image

 

Abstract

Mantle cell lymphoma (MCL) is a rare lymphoid malignancy with a poor prognosis characterised by frequent relapse and short durations of treatment response. Most patients present with aggressive disease, but there exist indolent subtypes without the need for immediate intervention. The very heterogeneous behaviour of MCL is genetically characterised by the translocation t(11;14)(q13;q32), leading to Cyclin D1 overexpression with distinct clinical and biological characteristics and outcomes. There is still an unfulfilled need for precise MCL prognostication in real-time. Machine learning and deep learning neural networks are rapidly advancing technologies with promising results in numerous fields of application. This study develops and compares the performance of deep learning (DL) algorithms and radiomics-based machine learning (ML) models to predict MCL relapse on baseline CT scans. Five classification algorithms were used, including three deep learning models (3D SEResNet50, 3D DenseNet, and an optimised 3D CNN) and two machine learning models based on K-nearest Neighbor (KNN) and Random Forest (RF). The best performing method, our optimised 3D CNN, predicted MCL relapse with a 70% accuracy, better than the 3D SEResNet50 (62%) and the 3D DenseNet (59%). The second-best performing method was the KNN-based machine learning model (64%) after principal component analysis for improved accuracy. Our optimised CNN developed by ourselves correctly predicted MCL relapse in 70% of the patients on baseline CT imaging. Once prospectively tested in clinical trials with a larger sample size, our proposed 3D deep learning model could facilitate clinical management by precision imaging in MCL.

 

Reference

Lisson, C. S., Lisson, C. G., Mezger, M. F., Wolf, D., Schmidt, S. A., Thaiss, W., Tausch, E., Beer A. J., Stilgenbauer, S., Beer, M., Goetz, M.. Deep Neural Networks and Machine Learning Radiomics Modelling for Prediction of Relapse in Mantle Cell Lymphoma. Cancers. 2022. Link


Longitudinal CT Imaging to Explore the Predictive Power of 3D Radiomic Tumour Heterogeneity in Precise Imaging of Mantle Cell Lymphoma (MCL)

Example image

 

Abstract

The study’s primary aim is to evaluate the predictive performance of CT-derived 3D radiomics for MCL risk stratification. The secondary objective is to search for radiomic features associated with sustained remission. Included were 70 patients: 31 MCL patients and 39 control subjects with normal axillary lymph nodes followed over five years. Radiomic analysis of all targets (n = 745) was performed and features selected using the Mann Whitney U test; the discriminative power of identifying “high-risk MCL” was evaluated by receiver operating characteristics (ROC). The four radiomic features, “Uniformity”, “Entropy”, “Skewness” and “Difference Entropy” showed predictive significance for relapse (p < 0.05)—in contrast to the routine size measurements, which showed no relevant difference. The best prognostication for relapse achieved the feature “Uniformity” (AUC-ROC-curve 0.87; optimal cut-off ≤0.0159 to predict relapse with 87% sensitivity, 65% specificity, 69% accuracy). Several radiomic features, including the parameter “Short Axis,” were associated with sustained remission. CT-derived 3D radiomics improves the predictive estimation of MCL patients; in combination with the ability to identify potential radiomic features that are characteristic for sustained remission, it may assist physicians in the clinical management of MCL.

 

Reference

Lisson, C. S., Lisson, C. G., Achilles, S., Mezger, M. F., Wolf, D., Schmidt, S. A, Thaiss, W., Johannes, B., Beer A. J., Stilgenbauer, S., Beer, M., Goetz, M.. Longitudinal CT Imaging to Explore the Predictive Power of 3D Radiomic Tumour Heterogeneity in Precise Imaging of Mantle Cell Lymphoma (MCL). Cancers. 2022. Link