AG Artificial Intelligence in Cardiovascular Medicine
About Us
Modern hospitals generate a vast amount of heterogeneous digital data e.g. in form of multimodal medical images, diagnostic reports, genetic information or real-time sensor streams. One core idea of precision medicine is to utilize such information for making predictions that assist in optimal treatment selection on a case-by-case basis. However, there persists an unmet clincial need to integrate such records for automatic processing, querying and adequate visualization along the entire treatment path.
The goal of our newly established working group is to leverage methods from the field of Artificial Intelligence for the analysis of heterogenous data collected from patients with cardiovascular diseases. We will especially focus on exploiting the potential of multimodal time-resolved cardiac images, such as Echocardiography, MRI, CT and Endoscopy for objective decision support in diagnosis and treatment. Beyond that, we continuously work in the direction of increasing the safety of surgical and interventional cardiovascular procedures. For example, building customized surgical training modules and intraoperative assistance systems are an integral part of our research topics.
It is our strong belief that research can only thrive through collaboration, hence we follow a translational approach and work very closely together with clinical partners. This enables us to address highly relevant clinical questions at the interface of cardiac surgery, cardiac intervention and cardiology. The recently established „Informatics 4 life“ consortium provides us with the optimal conditions to achieve this mission and to contribute towards the heart center of the future.
Head
Jun. Prof. Dr. Sandy Engelhardt
Focus
Medical Image Processing, Deep Learning, Computer-assisted Surgery
Projects
Augmented Reality and Deep Generative Models for Surgical Training
We have the ambitious goal to radically improve surgical training.
In order to achieve that, we enhance surgical training with augmented reality concepts that make surgical training more realistic (we coined this approach ‚Hyperrealism‘) and provide quantitative information about the training process itself.
Another of our core research topics is to build patient-specific simulators for surgical training to make training more effective and procedures safer for the patient.
Supplemental Material:
Heart Valve Modelling and Visualization, Computer-assistance in Heart Valve Surgery
Heart Valve Modelling and Visualization, Computer-assistance in Heart Valve Surgery
Heart valves are complex and highly dynamical anatomical structures. We are developing methods for extracting such complicated geometrical information from 3D+t echocardiographic data. Furthermore, our aim is to quantitatively and qualitatively present the patient information to provide deeper insights into pathological changes of individual valves.
Beyond that, we are working on computer-based intraoperative assistance modules for mitral valve repair to enable decision support e.g. for optimal prosthesis selection.
Supplemental Material:
MRI Analysis for Congenital Heart Disease
Magnetic resonance imaging (MRI) is a valuable tool to non-invasively assess geometrical, contraction-related, and tissue-related pathological changes.
By the help of machine learning techniques, we characterize these properties to better predict therapy response.
Analysis of CCTA Images of Patients with Coronary Artery Disease
Invasive heart catheters are a common approach to diagnose coronary artery disease. However, 3D-computed tomography (CT) can provide equal information on stenotic regions and plaque by being less invasive at the same time. Our aim is to analyse these data sets by the help of radiomics and deep learning methods.
Echocardiography Compounding
Echocardiography is the most employed modality in cardiovascular diseases due to its excellent capabilities of resolving anatomy and motion in real-time at all stages of the treatment process. Unfortunately, the analysis of the data is often hampered by image artefacts and a small field of view. Within the research campus STIMULATE, we work on deep learning-based image compounding and registration approaches to overcome these issues.
Team
Head of research group
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Jun. Prof. Dr. Sandy Engelhardt
Focus
Medical Image Processing, Deep Learning, Computer-assisted Surgery
Research Fellows
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Roger Karl, M.Sc.
Focus
Hemodynamic simulators and patient-specific replicas in Cardiovascular medicine
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Sven Köhler, M.Sc.
Focus
Computer Vision, Deep Learning and Motion analysis in Cardiovascular medicine
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Robert Kreher, M.Sc.
Focus
external Research Campus STIMULATE Magdeburg
Medical students
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Samantha Fischer
Focus
Simulating Mitral Valve Repair Surgeries with Patient-Specific Silicone Valve Models
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Arman Ghanaat
Focus
Deep learning-based coronary artery analysis of coronary CT angiography-data
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Josephin Marx
Focus
Creation of patient-specific 3D models for mitral valve reconstruction and interventional procedures: comparison of pathologies and forms of therapy
Master's students
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Antonia Stern
Bachelor student
Student assistant
Recent publications
Recent Publications
Garrow, C. R., Kowalewski, K., Li, L., Wagner, M., Schmidt, M. W., Engelhardt, S., Hashimoto, D. A., Kenngott, H. G., Bodenstedt, S., Speidel, S., Müller-Stich, B. P., Nickel, F.
Machine Learning for Surgical Phase Recognition: A Systematic Review
In: Annals of Surgery 2020
PDF | BibTeX
Engelhardt, S., Sharan, L., Karck, M., De Simone, R., Wolf, I.
Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training
In: Bildverarvbeitung für die Medizin (BVM) 2020
PDF | BibTeX | BVM2020Talk
Lichtenberg, N., Eulzer, P., Romano, G., Brcic, A., Karck, M., Lawonn, K., de Simone, R., Engelhardt, S.
Mitral valve flattening and parameter mapping for patient-specific valve diagnosis
In: International Journal of Computer Assisted Radiology and Surgery 2020
PDF | BibTeX
Wang, D.D., Qian, Z., Vukicevic, M., Engelhardt, S., Kheradvar, A., Zhang, C., Little, S.H., Verjans, J., Comaniciu, D., O’Neill, W.W., Vannan M.A.
3D Printing, Computational Modeling, and Artificial Intelligence for Structural Heart Disease
In: JACC: Cardiovascular Imaging 2020
PDF | BibTeX
Xiong, Z., Xia, Q., Hu, Z., Huang, N., Bian, C., Zheng, Y., Vesal, S., Ravikumar, N., Maier, A., Yang, X., Heng, P., Ni, D., Li, C., Tong, Q., Si, W., Puybareau, E., Khoudli, Y., Géraud, T., Chen, C., Bai, W., Rueckert, D., Xu, L., Zhuang, X., Luo, X., Jia, S., Sermesant, M., Liu, Y., Wang, K., Borra, D., Masci, A., Corsi, C., Vente, C., Veta, M., Karim, R., Preetha, C. J., Engelhardt, S., Qiao, M., Wang, Y., Tao, Q., Nuñez-Garcia, M., Camara, O., Savioli, N., Lamata, P., Zhao, J.
A Global Benchmark of Algorithms for Segmenting the Left Atrium from Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
In: Medical Image Analysis 2020
PDF | BibTeX | Preprint
Sharan, L., Burger, L., Kostiuchik, G., Wolf, I., Karck, M., De Simone, R., Engelhardt, S.
Domain gap in adapting self-supervised depth estimation methods for stereo-endoscopy
In Current Directions in Biomedical Engineering (CDBME) 2020 - (3rd Place CURAC Best Poster Award)
PDF | BibTeX
Kreher, R., Groscheck, T., Qarri, K., Preim, B., Schmeisser, A., Rauwolf, T., Christian, R., Engelhardt, S.
A Novel Calibration Phantom for Combining Echocardiography with Electromagnetic Tracking
In: Current Directions in Biomedical Engineering (CDBME) 2020
PDF | BibTeX | Calibration Phantom
Koehler, S., Tandon, A., Hussain, T., Latus, H., Pickardt T., Sarikouch, S., Beerbaum, B., Greil, G., Engelhardt, S., and Wolf, I.
How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalize to rare congenital heart diseases for surgical planning?
In: Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113151K (16 March 2020)
PDF | BibTeX | Preprint
Preetha, C.J., Wehrtmann, F.S., Sharan, L., Fan, C., Kloss, J., Müller-Stich, B.P., Nickel, F., Engelhardt, S.
Towards augmented reality-based suturing in monocular laparoscopic training
In: Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113150X (16 March 2020)
PDF | BibTeX | Preprint
Eulzer, P., Engelhardt, S., Lichtenberg, N., De Simone, R., Lawonn, K.
Temporal Views of Flattened Mitral Valve Geometries
In IEEE Trans Vis Comput Graph 2020
PDF | BibTeX | SciVis2019Talk | SciVis2019Preview | Supplemental Material Video
Pfeiffer, M., Funke, I., Robu, R. M., Bodenstedt, S., Strenger, L., Engelhardt, S., Roß, T., Clarkson, M.J., Gurusamy, K., Davidson, B.R., Maier-Hein, L., Riediger, C., Welsch, T., Weitz, J., Speidel, S.
Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation
In: Shen D. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
PDF | BibTeX | Preprint | Dataset | Code
Engelhardt, S., Sharan, L., Karck, M., De Simone, R., Wolf, I.
Cross-Domain Conditional Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training
In: Shen D. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
PDF | BibTeX | Preprint | Supplemental Material Video
Engelhardt, S., Sauerzapf, S., Preim, B., Karck, M., Wolf, I., De Simone, R.
Flexible and Comprehensive Patient-Specific Mitral Valve Silicone Models with Chordae Tendinae Made From 3D-Printable Molds
In: IJCARS Special Issue IPCAI 2019
PDF | BibTeX | Preprint | Video
Engelhardt S., De Simone R., Full P.M., Karck M., Wolf I.
Improving Surgical Training Phantoms by Hyperrealism: Deep Unpaired Image-to-Image Translation from Real Surgeries
In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
PDF | BibTeX | Preprint
Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.A., Cetin, I., Lekadir, K., Camara, O., Gonzalez Ballester, M.A.; Sanroma, G., Napel, S., Petersen, S., Tziritas, G., Grinias, E., Khened, M., Kollerathu, V.A., Krishnamurthi, G., Rohé, M.M., Pennec, X; Sermesant, M., Isensee, F., Jäger, P., Maier-Hein K.H., Full, P.M., Wolf, I., Engelhardt, S., Baumgartner, C.F., Koch, L.M., Wolterink, J.M., Išgum, I., Jang, Y., Hong, Y., Patravali, J., Jain, S., Humbert, O., Jodoin, P-M.
Deep Learning Techniques for Automatic MRI Cardiac Multi-structures Segmentation and Diagnosis: Is the Problem Solved?
In: IEEE Transactions on Medical Imaging 2018
PDF | BibTeX | Preprint