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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

Portrait von Jun. Prof. Dr. Sandy Engelhardt
Jun. Prof. Dr. Sandy Engelhardt
Focus

Medical Image Processing, Deep Learning, Computer-assisted Surgery


+49 6221 56-37173

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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

Research group head

Research associates

Medical students

  • Jimmy Chen

  • Samantha Fischer

    Focus

    Simulating Mitral Valve Repair Surgeries with Patient-Specific Silicone Valve Models


  • Josephine Marx

Master's students

Student assistant

  • Lukas Burger

    Focus

    Depth sensing with infrared stereo and structured light approaches


Recent publications

Recent Publications

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? Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113151K (16 March 2020) doi.org/10.1117/12.2550651

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. Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113150X (16 March 2020); doi.org/10.1117/12.2550830

Eulzer, P., Engelhardt, S., Lichtenberg, N., De Simone, R., Lawonn, K., Temporal Views of Flattened Mitral Valve Geometries, IEEE Trans Vis Comput Graph. 2020 Jan; 26(1):971-980. https://doi.org/10.1109/TVCG.2019.2934337

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. MICCAI 2019. Lecture Notes in Computer Science, vol 11768. Springer, Cham, pp 155-163, doi.org/10.1007/978-3-030-32254-0_18

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. IJCARS Special Issue IPCAI 2019. 14(7):1177–1186, https://doi.org/10.1007/s11548-019-01971-9

Engelhardt S., De Simone R., Full P.M., Karck M., Wolf I. (2018) Improving Surgical Training Phantoms by Hyperrealism: Deep Unpaired Image-to-Image Translation from Real Surgeries. In: Frangi A., Schnabel J., Davatzikos C., Alberola-López C., Fichtinger G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11070. Springer, Cham, https://doi.org/10.1007/978-3-030-00928-1_84

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?, IEEE Transactions on Medical Imaging  37:11 , 2018; 2514 – 2525, https://doi.org/10.1109/TMI.2018.2837502