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AG Artificial Intelligence in Cardiovascular Medicine

Belongs to Klinik für Herzchirurgie and Klinik für Kardiologie, Angiologie, Pneumologie

News

May 21, 2021: We congratulate our medical student Julian Kuhm for receiving the Kaltenbach-Doktorandenstipendium (Scholarship) from the Deutschen Herzstiftung e.V.!

May 17, 2021: We will give a talk on AI in Multisystem Inflammatory Syndrome in Children (MIS-C) in the Special Theme Session II at FIMH 2021.

May 07, 2021: AdaptOR is featured as challenge of the month on Computer Vision News!

April 28, 2021: We are co-organizing the first "Deep Generative Models 4 MICCAI" (DGM4MICCAI) workshop.

April 07, 2021: Research of the month: Our IEEE TMI publication on Cardiac MRI analysis was featured on Computer Vision News. Preprint

April 06, 2021: We are organizing the MICCAI AdaptOR Challenge 2021. Data is now available: https://adaptor2021.github.io/

April 01, 2021: Malte Tölle, MSc. joined our team as PhD student. Welcome!

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


+49 6221 56-37173

Projects

Team

Head of research group

Research Fellows

Medical students

  • Portrait of Jimmy Chen

    Jimmy Chen

    Focus

    Surgical Process Modelling for Patients with Mitral Regurgitation


  •  Portrait of Samantha Fischer

    Samantha Fischer

    Focus

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


  • Portrait of Arman Ghanaat

    Arman Ghanaat

    Focus

    Deep learning-based coronary artery analysis of coronary CT angiography-data


  • Julian Kuhm

    Focus

    Cardiac Motion Vector Field Visualization and Clustering


  • Josephin Marx

    Focus

    Creation of patient-specific 3D models for mitral valve reconstruction and interventional procedures: comparison of pathologies and forms of therapy


Student members

  • Portrait of Lukas Burger, B.Sc.

    Lukas Burger, B.Sc.

    Student Assistant

    Focus

    Depth sensing with infrared stereo and structured light approaches


  • Halvar Kelm

    Internship


Alumni

Alexander Rogausch
Antonia Stern
Florian Ritzmann
Jean-Luc Busch
Jonathan Kloss
Julian Brand
Robert Kreher
Simon Sauerzapf
Ulrike Schnaithmann

Recent publications

Recent Publications

Friedrich, S., Groß, S., König, I. R., Engelhardt, S., Bahls, M., Heinz, J., Huber, C., Kaderali, L., Kelm, M., Leha, A., Rühl, J., Schaller, J., Scherer, C., Vollmer, M., Seidler, T., Friede T.
Applications of AI/ML approaches in cardiovascular medicine: A systematic review with recommendations
In: European Heart Journal - Digital Health 2021
PDF|BibTeX


Koehler, S., Hussain, T., Blair, Z., Huffaker, T., Ritzmann, F., Tandon, A., Pickardt, T., Sarikouch, S., Latus, H., Greil, G., Wolf, I., Engelhardt, S.
Unsupervised Domain Adaptation from Axial to Short-Axis Multi-Slice Cardiac MR Images by Incorporating Pretrained Task Networks
In: IEEE Transactions on Medical Imaging 2021
PDF| BibTeX  |  PreprintRepo

Stern, A., Sharan, L., Romano, G., Koehler, S., Karck, M., De Simone, R., Wolf, I., Engelhardt, S.
Heatmap-based 2D Landmark Detection with a Varying Number of Landmarks
In: Bildverarbeitung für die Medizin (BVM), Informatik aktuell. Springer Vieweg, Wiesbaden 2021
PDF| BibTeX  |  Preprint

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: Bildverarbeitung 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, Volume 14, Issue 1, January 2021
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, Volume 67, January 2021
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