Clinical Data Science
The clinical data science group employs advanced statistics, machine learning and computer vision techniques in the context of clinical radiology to enable fast and precise AI-supported diagnosis and prognostication. Our direct embedding into the Department of Radiology provides close cooperation and interdisciplinary interaction with radiologists. This gives us the opportunity to identify and address clinical needs and to develop and evaluate AI solutions directly in the clinical context.
Image data is the most natural type of data in every radiology department. Consequently, a great part of our research is dedicated to image analysis. In particular, we use state-of-the-art deep learning methods to analyze CT and MR images in 2D or 3D.
Computer vision in the field of medicine differs from other application areas since medical image data is typically sparse and labels are very expensive (time and money-wise). This makes computer vision in medicine a challenging, but also very fascinating branch of deep learning research.
Our ongoing computer vision research includes - but is not limited to - the following topics:
- Analyzing medical time series data by feeding CT perfusion images into complex deep neural network architectures
- Predicting the character of potentially cancerous lesions on sparse 3D image datasets without additional information, e.g. segmentation masks or laboratory findings
- Predicting clinical endpoints for 2D and 3D CT images using established convolutional neural network architectures
Our group aims at solving a wide range of problems in the context of clinical radiology. Each task comes with different data and needs to be tackled with different tools. Among the tools we used most often are: classical machine learning algorithms (e.g. random forest), radiomics and classical statistics.
Typically, we apply classical machine learning algorithms and statistics to tabular data in order to solve a clinical task, predict a clinical endpoint or visualize complex data structures.
Radiomics extracts standardized image features from medical images, e.g. CT or MR images, thereby transforming complex information stored in 2D or 3D images into tabular data. The latter can then be analyzed with classical machine learning algorithms and classical statistics.
Recent projects include:
- Radiomics project in which we predict the character of potentially cancerous lesions detected in CT scans
- Survival analysis to study the effects of a reduced chest x-ray volume on intensive care units
Clinical data science within the context of research on radiological topics entails the use of advanced, specialized tools on complex data and consideration of department specifics within the application of these tools.
In order to accommodate the needs of the computer-vision and machine-learning research, using most recent and novel methods, and to answer clinical questions, based on the rich data from heterogeneous sources, we develop, deploy and maintain the tools and infrastructure needed for these tasks.
Our recent efforts include the projects of the like of:
- Structured acquisition of patient data, used for an exploratory analysis of Covid-19 patients undergoing radiological imaging
- Retrieval and pseudonymization of radiological images from our picture archive for a large dataset for supervised deep learning
- Monitoring, visualization and evaluation of the imaging activity of the radiology department
Dr. rer. nat. Balthasar Schachtner
Balthasar received his PhD for his research in the field of experimental particle physics. The focus of his postdoctoral research is the facilitation of machine learning in radiology and the development of imaging biomarkers for lung pathologies.
Dr. rer. nat. Katharina Jeblick, MA phil.
Katharina received her Phd in Physics in the field of computational physics for quantum materials and holds an additional master degree in Philosophy of Science and Technology focussing on ethical and social implications of technology.
Her postdoctoral research scope includes the application and advancement of deep learning for lung imaging to support clinical decision making.
Andreas Mittermeier, M.Sc.
Andreas has a master’s degree in physics and was interested in medical imaging since his undergraduate studies. His doctoral research project comprises perfusion imaging analysis in the context of novel machine learning methods.
Theresa Stüber, M.Sc.
Theresa got her master's degree in (bio-)statistics and pursued her great interest for machine learning in medicine already during university studies. In her doctoral research she develops a framework for the combinaton of deep learning with classical statistical modeling.
Tobias Weber, M.Sc.
Tobias has a master's degree in computer science with a focus on data analysis and machine learning. His doctoral research project concerns with the practical application of deep learning on large medical data.
Philipp Wesp, M.Sc.
Philipp has a medical physics background and focused primarily on proton computed tomography as a Master student. Today he analyzes CT images using modern machine learning techniques in order to predict valuable clinical endpoints, e.g. the character of potentially malignant lesions.
Matthias Bock, B.Sc.
Matthias holds a bachelor’s degree in physics and specializes in medical physics during his master’s studies. His master’s project concerns the analysis and development of robust deep learning-based lung segmentation models and the extraction of quantitative imaging biomarkers based on radiomics.
Andreas Klaß, B.Sc.
Andreas has a background in Economics and Statistics/Data Science. His master's thesis focuses on estimating and visualizing uncertainty in deep learning-based medical image segmentation.
Johanna Topalis, B.Sc.
Johanna has a bachelor‘s degree in physics and specializes in medical physics during her master‘s studies. Her master‘s project addresses deep-learning reconstruction of interventional MRI data.
Marvin Weber, B.Sc.
Marvin has a bachelor's degree in computer science. His master's project focuses on uncertainty quantification in deep learning-based forensic age estimation. Different uncertainty quantification methods are compared to the model test error and standard age assessment methods.
Medical Advisor Radiology
Associated Clinical Researchers
Mittermeier A, Reidler P, Fabritius MP, Schachtner B, Wesp P, Ertl-Wagner B, Dietrich O, Ricke J, Kellert L, Tiedt S, Kunz WG, Ingrisch M. End-to-End Deep Learning Approach for Perfusion Data: A Proof-of-Concept Study to Classify Core Volume in Stroke CT. Diagnostics. 2022. doi: 10.3390/diagnostics12051142
Matic A, Monnet M, Lorenz JM, Schachtner B, Messerer T. Quantum-classical convolutional neural networks in radiological image classification. arXiv. 2022 Apr. doi: 10.48550/ARXIV.2204.12390
Wesp P, Grosu S, Graser A, Maurus S, Schulz C, Knösel T, Fabritius MP, Schachtner B, Yeh BM, Cyran CC, Ricke J, Kazmierczak PM, Ingrisch M. Deep learning in CT colonography: differentiating premalignant from benign colorectal polyps. European Radiology. 2022 Jan. doi: 10.1007/s00330-021-08532-2
Öcal O, Ingrisch M, Ümütlü MR, Peynircioglu B, Loewe C, van Delden O, Vandecaveye V, Gebauer B, Zech CJ, Sengel C, Bargellini I, Iezzi R, Benito A, Pech M, Malfertheiner P, Ricke J, Seidensticker M. Prognostic value of baseline imaging and clinical features in patients with advanced hepatocellular carcinoma. Br J Cancer. 2022 Feb;126(2):211-218. doi: 10.1038/s41416-021-01577-6. Epub 2021 Oct 22. PMID: 34686780.
Weber T, Ingrisch M, Fabritius M, Bischl B, Rügamer D. Survival-oriented embeddings for improving accessibility to complex data structures. NeurIPS 2021 Bridging the Gap: From Machine Learning Research to Clinical Practice. 2021 Oct 28;abs/2110.11303. DBLP: https://dblp.uni-trier.de/rec/journals/corr/abs-2110-11303.html
Weber T, Ingrisch M, Bischl B, Rügamer D. Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation. NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications. 2021 Oct 19;abs/2110.11312. DBLP: https://dblp.uni-trier.de/rec/journals/corr/abs-2110-11312.html
Fabritius MP, Seidensticker M, Rueckel J, Heinze C, Pech M, Paprottka KJ, Paprottka PM, Topalis J, Bender A, Ricke J, Mittermeier A, Ingrisch M. Bi-Centric Independent Validation of Outcome Prediction after Radioembolization of Primary and Secondary Liver Cancer. Journal of Clinical Medicine. 2021 Aug 19. doi: 10.3390/jcm10163668
Mahnken AH, Nadjiri J, Schachtner B, Bücker A, Heuser LJ, Morhard D, Landwehr P, Hoffmann RT, Berlis A, Katoh M, Reimer P, Ingrisch M, Paprottka P. Availability of interventional-radiological revascularization procedures in Germany - an analysis of the DeGIR Registry Data 2018/19. Rofo. 2021 Aug 4. English, German. doi: 10.1055/a-1535-2774. Epub ahead of print. PMID: 34348401.
Grosu S, Wesp P, Graser A, Maurus S, Schulz C, Knösel T, Cyran CC, Ricke J, Ingrisch M, Kazmierczak PM. Machine Learning-based Differentiation of Benign and Premalignant Colorectal Polyps Detected with CT Colonography in an Asymptomatic Screening Population: A Proof-of-Concept Study. Radiology. 2021 Feb 23;202363. doi: 10.1148/radiol.2021202363. Epub ahead of print. PMID: 33620287.
Scherer J, Nolden M, Kleesiek J, Metzger J, Kades K, Schneider V, Bach M, Sedlaczek O, Bucher AM, Vogl TJ, Grünwald F, Kühn JP, Hoffmann RT, Kotzerke J, Bethge O, Schimmöller L, Antoch G, Müller HW, Daul A, Nikolaou K, la Fougère C, Kunz WG, Ingrisch M, Schachtner B, Ricke J, Bartenstein P, Nensa F, Radbruch A, Umutlu L, Forsting M, Seifert R, Herrmann K, Mayer P, Kauczor HU, Penzkofer T, Hamm B, Brenner W, Kloeckner R, Düber C, Schreckenberger M, Braren R, Kaissis G, Makowski M, Eiber M, Gafita A, Trager R, Weber WA, Neubauer J, Reisert M, Bock M, Bamberg F, Hennig J, Meyer PT, Ruf J, Haberkorn U, Schoenberg SO, Kuder T, Neher P, Floca R, Schlemmer HP, Maier-Hein K. Joint Imaging Platform for Federated Clinical Data Analytics. JCO Clin Cancer Inform. 2020 Nov;4:1027-1038. doi: 10.1200/CCI.20.00045. PMID: 33166197; PMCID: PMC7713526.
Rueckel J, Kunz WG, Hoppe BF, Patzig M, Notohamiprodjo M, Meinel FG, Cyran CC, Ingrisch M, Ricke J, Sabel BO. Artificial Intelligence Algorithm Detecting Lung Infection in Supine Chest Radiographs of Critically Ill Patients With a Diagnostic Accuracy Similar to Board-Certified Radiologists. Crit Care Med. 2020 Jul;48(7):e574-e583. doi: 10.1097/CCM.0000000000004397. PMID: 32433121.
Rueckel J, Trappmann L, Schachtner B, Wesp P, Hoppe BF, Fink N, Ricke J, Dinkel J, Ingrisch M, Sabel BO. Impact of Confounding Thoracic Tubes and Pleural Dehiscence Extent on Artificial Intelligence Pneumothorax Detection in Chest Radiographs. Invest Radiol. 2020 Jul 15. doi: 10.1097/RLI.0000000000000707. Epub ahead of print. PMID: 32694453.
Nadjiri J, Schachtner B, Bücker A, Heuser L, Morhard D, Landwehr P, Mahnken A, Hoffmann RT, Berlis A, Katoh M, Reimer P, Ingrisch M, Paprottka PM. Availability of Transcatheter Vessel Occlusion Performed by Interventional Radiologists to Treat Bleeding in Germany in the Years 2016 and 2017 - An Analysis of the DeGIR Registry Data. Rofo. 2020 Oct;192(10):952-960. English, German. doi: 10.1055/a-1150-8087. Epub 2020 Jul 7. PMID: 32634837.
Mittermeier A, Ertl-Wagner B, Ricke J, Dietrich O, Ingrisch M. Bayesian pharmacokinetic modeling of dynamic contrast-enhanced magnetic resonance imaging: validation and application. Phys Med Biol. 2019 Sep 17;64(18):18NT02. doi: 10.1088/1361-6560/ab3a5a
Fasler DA, Ingrisch M, Nanz D, Weckbach S, Kyburz D, Fischer DR, Guggenberger R, Andreisek G. Rheumatoid cervical pannus: feasibility of volume and perfusion quantification using dynamic contrast enhanced time resolved MRI. Acta Radiol. 2020 Feb;61(2):227-235. doi: 10.1177/0284185119854200. Epub 2019 Jun 6. PMID: 31169411.
Smith EE, Biessels GJ, De Guio F, de Leeuw FE, Duchesne S, Düring M, Frayne R, Ikram MA, Jouvent E, MacIntosh BJ, Thrippleton MJ, Vernooij MW, Adams H, Backes WH, Ballerini L, Black SE, Chen C, Corriveau R, DeCarli C, Greenberg SM, Gurol ME, Ingrisch M, Job D, Lam BYK, Launer LJ, Linn J, McCreary CR, Mok VCT, Pantoni L, Pike GB, Ramirez J, Reijmer YD, Romero JR, Ropele S, Rost NS, Sachdev PS, Scott CJM, Seshadri S, Sharma M, Sourbron S, Steketee RME, Swartz RH, van Oostenbrugge R, van Osch M, van Rooden S, Viswanathan A, Werring D, Dichgans M, Wardlaw JM. Harmonizing brain magnetic resonance imaging methods for vascular contributions to neurodegeneration. Alzheimers Dement (Amst). 2019 Feb 26;11:191-204. doi: 10.1016/j.dadm.2019.01.002. PMID: 30859119; PMCID: PMC6396326.
Debus C, Floca R, Ingrisch M, Kompan I, Maier-Hein K, Abdollahi A, Nolden M. MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging - design, implementation and application on the example of DCE-MRI. BMC Bioinformatics. 2019 Jan 16;20(1):31. doi: 10.1186/s12859-018-2588-1. PMID: 30651067; PMCID: PMC6335810.
Suchorska B, Schüller U, Biczok A, Lenski M, Albert NL, Giese A, Kreth FW, Ertl-Wagner B, Tonn JC, Ingrisch M. Contrast enhancement is a prognostic factor in IDH1/2 mutant, but not in wild-type WHO grade II/III glioma as confirmed by machine learning. Eur J Cancer. 2019
The Clinical Data Science group gratefully acknowledges research funding by:
Bundesministerium für Gesundheit
Deutsches Zentrum für Lungenforschung (DZL)
RACOON, Netzwerk Universitätsmedizin
Schwerpunktprogramm Radiomics, DFG Deutsche Forschungsgemeinschaft
Research training group GRK 2274 of the DFG, Deutsche Forschungsgemeinschaft
We can regularly offer projects for Bachelor (3 months) and Master Theses (6 months / 1 year) in the field of Data Science and Machine Learning applied in the context of clinical radiology. Projects are assigned either after applying for a specific project proposal (see below) or upon qualified request. If you would like to join us for a PhD Thesis, please contact Prof. Michael Ingrisch (email@example.com).
If you are curious about our group and wish to learn more about our work, do not hesitate to get in touch with us!
There are no project proposals at the moment.
Last edited: 15.06.2022