Augmented Reality and Deep Generative Models in Endoscopy

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tempCycleGAN: Generative Adversarial Network Improves Realism During Surgical Training - MICCAI 2018 (Youtube)

From intraoperative empirical valve inspection towards quantitative optical tracking analysis (Video)

Surgical Simulation with 3D Printing

Training Reconstructive Mitral Valve Surgery on Patient-Specific Mitral Valve Models (Youtube)

Temporal Views of Flattened Mitral Valve Geometries (Youtube)

Deep Cardiac MRI processing

3D CMR domain adaptation

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  Prof. Dr. Sandy Engelhardt

  Focus

  Medical Image Processing, Deep Learning, Computer-assisted Surgery

  Email
  +49 6221 56-37173
  

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  Dr. Salman Ul Hassan Dar

  Focus

  Medical image processing, Computer vision, Deep learning

  Email
  +49 6221 56-310341
  
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  Dr. Roger Karl

  Focus

  Hemodynamic simulators and patient-specific replicas in Cardiovascular medicine

  Email
  +49 6221 56-32089
  

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  Sören Bergt, M. Sc.

  Focus

  Research on Mitral Valve Materials for Dynamic Simulation

  Email
  +49 6221 56-32043
  

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  Dr. med. Samantha Fischer

  Focus

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

  Email
  

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  Sven Köhler, M.Sc.

  Focus

  Computer Vision, Deep Learning and Motion analysis in Cardiovascular medicine

  Email
  +49 6221 56-310341
  

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  Georgii Kostiuchik, M.Sc.

  Focus

  Surgical Phase Recognition

  Email
  
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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

  Email
  
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  Sarah Kaye Müller, M.Sc.

  Focus

  AI-based cardiac magnetic resonance imaging analysis

  Email
  
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  Marvin Seyfarth, M.Sc.

  Focus

  Medical Image Synthesis, Deep Learning

  Email
  
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  Malte Tölle, M.Sc.

  Focus

  Computer Vision and Deep Learning

  Email
  +49 6221 56-310341
  

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  Lukas Burger, M.Sc.

  Focus

  Depth sensing with infrared stereo and structured light approaches

  Email
  
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  Dr. sc. hum Max Seitz

  Email
  +49 6221 56-7368
  +49 6221 56-4997

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  Matthäus Bruckner
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  Jimmy Chen

  Focus

  Surgical Process Modelling for Patients with Mitral Regurgitation

  Email
  
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  Arman Ghanaat

  Focus

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

  Email
  
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  Julian Kuhm

  Focus

  Cardiac Motion Vector Field Visualization and Clustering

  Email
  
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  Marcello Mächerle

  Focus

  Investigation of hemodynamics in type B aortic dissections using patient-specific 3D-printed and perfused aortic models

  Email
  
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  Lukas Mohl

  Focus

  Development of patient-specific aortic models for hemodynamic training

  Email
  
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  Christina Wang

  Focus

  Surgical Training of Mitral Valve Reconstruction on Patient-Specific Simulators for Quantification of the Learning Progress

  Email
  

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  Marie Kapusta

  Bachelor's Thesis

  Focus

  Mitral Valve Modeling, Medical Image Processing

  Email
  
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  Halvar Kelm, B.Sc.

  Student Assistant

  Focus

  Deep Learning and Computer Vision

  Email
  
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  Elise Kudlich, B.Sc.

  Master's Thesis

  Focus

  Tissue Mechanical Characterisation

  Email
  
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  Amon Schneider, B.Sc.

  Master's Thesis

Abdullah Kaplican
Alexander Rogausch
Andela Grizelj
Antonia Stern
Florian Ritzmann
Ibrahim Mansaray
Jean-Luc Busch
Jonathan Kloss
Julian Brand
Lalith Sharan
Maren Leidner
Maximilian Hehl
Max Wernz
Meret Anne Siemer
Michael Salzgeber
Mohamad Omran Scharaf
Moritz Bednorz
Robert Kreher
Sebastian Eble
Simon Sauerzapf
Ulrike Schnaithmann

Palm, V., Thangamani, S., Budai, B. K., Skornitzke, S., Eckl, K., Tong, E., Sedaghat, S., Heußel, C. P., von Stackelberg, O., Engelhardt, S., Kopytova, T., Norajitra, T., Maier-Hein, K. H., Kauczor, H.-U., Wielpütz, M. O.
AI-based CT assessment of 3117 vertebrae reveals significant sex-specific vertebral height differences
In: Scientific Reports, Vol. 15, Nr. 1 (2025)
PDF  | BibTeX

Mueller, S. K., Kiekenap, J., Koehler, S., Andre, F., Frey, N., Greil, G., Hussain, T., Wolf, I., Engelhardt, S.
An Automatic Self-supervised Phase-Based Approach to Aligned Long-Axis Strain Measurements in Four Chamber Cardiovascular Magnetic Resonance Imaging
In: Functional Imaging and Modeling of the Heart (FIMH 2025)
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Svennberg, E., Han, J. K., Caiani, E. G., Engelhardt, S., Ernst, S., Friedman, P., Garcia, R., Ghanbari, H., Hindricks, G., Man, S. H., Millet, J., Narayan, S. M., Ng, G. A., Noseworthy, P. A., Tjong, F. V. Y., Ramírez, J., Singh, J. P., Trayanova, N., Duncker, D., … Zorzi, A.
State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology
In: EP Europace, Vol. 27, Nr. 5 (2025)
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Koehler, S., Kuhm, J., Huffaker, T., Young, D., Tandon, A., André, F., Frey, N., Greil, G., Hussain, T., Engelhardt, S.
Deep Learning–based Aligned Strain from Cine Cardiac MRI for Detection of Fibrotic Myocardial Tissue in Patients with Duchenne Muscular Dystrophy
In: Radiology: Artificial Intelligence, Volume 7, Number 3 (2025)
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Seyfarth, M., Dar, S.U.H., Ayx, I., Fink, M.A., Schoenberg, S.O., Kauczor, H.-U., Engelhardt, S.
MedLoRD: A Medical Low-Resource Diffusion Model for High-Resolution 3D CT Image Synthesis
In: Simulation and Synthesis in Medical Imaging  workshop (SASHIMI) at MICCAI 2025 (accepted)
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Svennberg, E., Han, J.K., Caiani, E.G., Engelhardt, S., Ernst, S., Friedman, P., Garcia, R., Ghanbari, H., Hindricks, G., Man, S.H., Millet, J., Narayan, S.M., Ng, G.A., Noseworthy, P.A., Tjong, F.V.Y., Ramírez, J., Singh, J.P., Trayanova, N., Duncker, D
State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025. A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group in e-Cardiology
In: EP Europace (2025)
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Tölle, M., Garthe, P., Scherer, C., Seliger, J.M., Leha, A., Krüger, N., Simm, S., Martin, S., Eble, S., Kelm, H., Bednorz, M., André, F., Bannas, P., Diller, G., Frey, N., Groß, S., Hennemuth, A., Kaderali, L., Meyer, A., Nagel, E., Orwat, S., Seiffert, M., Friede, T., Seidler, T., Engelhardt, S.
Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imaging
In: npj digital medicine (2025)
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Tölle, M., Burger, L., Kelm, H., André, F., Bannas, P., Diller, G., Frey, N., Garthe, P., Groß, S., Hennemuth, A., Kaderali, L., Krüger, N., Leha, A., Martin, S., Meyer, A., Nagel, E., Orwat, S., Scherer, C., Seiffert, M., Seliger, J.M., Simm, S., Friede, T., Seidler, T., Engelhardt, S
Multi-modal dataset creation for federated learning with DICOM-structured reports
In: International Journal of Computer Assisted Radiology and Surgery (2025)
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Tölle, M., Scharaf, M., Fischer, S., Reich, C., Zeid, S., Dieterich, C., Meder, B., Frey, N., Wild, P., Engelhardt, S.
Arbitrary Data as Images: Fusion of Patient Data Across Modalities and Irregular Intervals with Vision Transformers
In: arXiv (2025)
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Karl, R., Leister, R., Stroh, L., Mereles, D., Eden, M., Neff, L., Raffaele, D. S., Romano, G., Kriegseis, J., Karck, M., Lichtenstern, C., Frey, N., Frohnapfel, B., Stroh, A., Engelhardt, S.
Investigating the Shortcomings of the Flow Convergence Method for Quantification of Mitral Regurgitation in a Pulsatile In-Vitro Environment and with Computational Fluid Dynamics
In: Cardiovascular Engineering and Technology (2025)
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Woźnicki, P., Laqua, C., Fiku, I., Hekalo, A., Truhn, D., Engelhardt, S., Kather, J., Foersch, S., D’Antonoli, T.A., Pinto dos Santos, D., Baeßler, B., Laqua, F.C.
Automatic structuring of radiology reports with on-premise open-source large language models
In: European Radiology (2024)
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Grizelj, A., Sharan, L., Karck, M., Simone, R.D., Romano, G., Engelhardt, S.
On-demand mitral valve morphometrics during surgical repair
In: Current Directions in Biomedical Engineering, Volume 10 (2024)
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Engelhardt, S.
Why thorough open data descriptions matters more than ever in the age of AI: opportunities for cardiovascular research
In: European Heart Journal - Digital Health (2024)
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Engelhardt, S., Dar, S.U.H., Sharan, L., André, F., Nagel, E., Thomas, S.
Artificial intelligence in cardiovascular imaging and intervention (2024)
In: Herz
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Seyfarth, M., Dar, S.U.H., Engelhardt, S.
Latent Pollution Model: The Hidden Carbon Footprint in 3D Image Synthesis
In: Simulation and Synthesis in Medical Imaging  workshop (SASHIMI) at MICCAI 2024
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Kahmann, J., Nörenberg, D., Papavassiliu, T., Dar, S.U.H., Engelhardt, S., Schoenberg, S.O., Froelich, M.F., Ayx, I.
Combined conventional factors and the radiomics signature of coronary plaque texture could improve cardiac risk prediction
In: Insights into Imaging, Volume 15 (2024)
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Baeßler, B., Engelhardt, S., Hekalo, A., Hennemuth, A., Hüllebrand, M., Laube, A., Scherer, C., Tölle, M., Wech, T.
Perfect Match: Radiomics and Artificial Intelligence in Cardiac Imaging
In: Circulation: Cardiovascular Imaging, Volume 17, Issue 6 (2024)
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Tölle, M., Navarro, F., Eble, S., Wolf, I., Menze, B., Engelhardt, S.
FUNAvg: Federated Uncertainty Weighted Averaging for Distributed Datasets with Diverse Labels
In: MICCAI 2024
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Mohl, L., Karl, R., Hagedorn, M.N., Runz, A., Skornitzke, S., Toelle, M., Bergt, C.S., Hatzl, J., Uhl, C., Böckler, D., Meisenbacher, K., Engelhardt, S.
Simulation of thoracic endovascular aortic repair in a perfused patient-specific model of type B aortic dissection
In: International Journal of Computer Assisted Radiology and Surgery (2024)
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Lehmann, D.H., Gomes, B., Vetter, N., Braun, O., Amr, A., Hilbel, T., Müller, J., Köthe, U., Reich, C., Kayvanpour,E., Sedaghat-Hamedani, F., Meder, M., Haas, J., Ashley, E., Rottbauer, W., Felbel, D., Bekeredjian, R., Mahrholdt, H., Keller, A., Ong, P., Seitz, A., Hund, H., Geis, N., André, F., Engelhardt, S., Katus, H.A., Frey, N., Heuveline, V., Meder, B.
Prediction of diagnosis and diastolic filling pressure by AI-enhanced cardiac MRI: a modelling study of hospital data
In: The Lancet Digital Health, Volume 6, Issue 6 (2024)
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Dar, S.U.H., Seyfarth, M., Kahmann, J., Ayx, I., Papavassiliu, T., Schoenberg, S.O., Frey, N., Baeßler, B., Foersch, S., Truhn, D., Kather, J.N., Engelhardt, S.
Unconditional Latent Diffusion Models Memorize Patient Imaging Data: Implications for Openly Sharing Synthetic Data
In: arXiv (2024)
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Koehler, S., Kuhm, J., Huffaker, T., Young, D., Tandon, A., Andre, F., Frey, N., Greil, G., Hussain, T., Engelhardt, S.
Artificial Intelligence to derive aligned strain in cine CMR to detect patients with myocardial fibrosis: an open and scrutinizable approach
Under Review (2024)
Preprint | BibTeX

Toelle, M., Burger, L., Kelm, H., Engelhardt, S.
Towards Unified Multi-Modal Dataset Creation for Deep Learning Utilizing Structured Reports
In: Bildverarbeitung in der Medizin 2024 (BVM)
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Dar, S.U.H.,  Ayx, I., Kapusta, M., Papavassiliu, T., Schoenberg, S.O., Engelhardt, S.
Effect of Training Epoch Number on Patient Data Memorization in Unconditional Latent Diffusion Models
In: Bildverarbeitung in der Medizin 2024 (BVM)
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Kostiuchik, G., Sharan, L., Mayer, B., Wolf, I., Preim, B., Engelhardt, S.
Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splits
In: International Journal of Computer Assisted Radiology and Surgery (2024)
PDF | Preprint | Code | App | BibTeX

Karl, R., Romano, G., Marx, J., Eden, M., Schlegel, P., Stroh, L., Fischer, S., Hehl, M., Kühle, R., Mohl, L., Karck, M., Frey, N., De Simone, R., Engelhardt, S.
An ex-vivo and in-vitro dynamic simulator for surgical and transcatheter mitral valve interventions
In: International Journal of Computer Assisted Radiology and Surgery (2023)
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Engelhardt, S., Martin, S., Rodríguez Bolanos, C.R., Pappas, L., Koehler, S., Nagel, E.
Künstliche Intelligenz in der kardialen Bildgebung
In: Aktuelle Kardiologie, Volume 12, Number 6, November (2023)
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Wang, C., Karl, R., Sharan, L., Grizelj, A., Fischer, S., Karck, M., De Simone, R., Romano, G., Engelhardt, S.
Surgical Training of Minimally Invasive Mitral Valve Repair on a Patient-Specific Simulator Improves Surgical Skills
In: European Journal of Cardio-Thoracic Surgery (2023)
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Dar, S.U.H., Ghanaat, A., Kahmann, J., Ayx, I., Papavassiliu, T., Schoenberg, S.O., Engelhardt, S.
Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesis
In: Deep Generative Models workshop (DGM4MICCAI) at MICCAI 2023
Preprint | BibTeX

Khader, F., Müller-Franzes, G., Tayebi Arasteh, S., Han, T., Haarburger, C., Schulze-Hagen, M., Schad, P., Engelhardt, S., Baeßler, B., Foersch, S., Stegmaier, J., Kuhl, C., Nebelung, S., Kather, J.N., Truhn, D.
Denoising diffusion probabilistic models for 3D medical image generation
In: Scientific Reports, Volume 13, Issue 1 (2023)
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Burger, L., Sharan, L., Karl, R., Wang, C., Karck, M., De Simone, R., Wolf, I., Romano, G., Engelhardt, S.
Comparative evaluation of three commercially available markerless depth sensors for close-range use in surgical simulation
In: International Journal of Computer Assisted Radiology and Surgery (2023)
PDF | Code | BibTeX

Fischer, S., Romano, G., Sharan, L., Warnecke, G., Mereles, D., Karck, M., De Simone, R., Engelhardt, S.
Surgical Rehearsal for Mitral Valve Repair: Personalizing Surgical Simulation by 3D-Printing
In: The Annals of Thoracic Surgery, Volume 115, Issue 4, (2023)
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Mayer, S., Meuschke, M., Chen, J., Müller-Stich, B., Wagner, M., Preim, B., Engelhardt, S.
Interactive visual exploration of surgical process data
In: International Journal of Computer Assisted Radiology and Surgery (2022)
PDF | App | BibTeX | Video

Sharan, L., Romano G., Kelm, H., Karck, M., De Simone, R., Engelhardt, S.
mvHOTA: A multi-view higher order tracking accuracy metric to measure spatial and temporal associations in multi-point detection
In: AE-CAI | CARE | OR 2.0 JOINT MICCAI WORKSHOP 2022 at MICCAI 2022
PDF | Preprint | Code | Poster | BibTeX

Eisenmann, M., et al.
Why is the winner the best?
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2023
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Eisenmann, M., et al.
Biomedical image analysis competitions: The state of current participation practice
In: arXiv (2023)
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Khader, F., Mueller-Franzes, G., Arasteh, S.T., Han, T., Haarburger, C., Schulze-Hagen, M., Schad, P., Engelhardt, S., Baessler, B., Foersch, S., Stegmaier, J., Kuhl, C., Nebelung, S., Kather, J.N., Truhn, D.
Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
In: arXiv (2023)
Preprint | Code | BibTeX

Koehler, S., Hussain, T.,Hussain, H., Young, D., Sarikouch, S., Pickhardt, T., Greil, G., Engelhardt, S
Self-supervised motion descriptor for cardiac phase detection in 4D CMR based on discrete vector field estimations
In: 13th edition of Statistical Atlases and Computational Modeling of the Heart (STACOM) Workshop at MICCAI22
PDF | Preprint | Code | BibTeX 

Toelle, M., Koethe, U., André, F., Meder, B., Engelhardt, S.
Content-Aware Differential Privacy with Conditional Invertible Neural Networks
In: 3rd Workshop on Distributed, Collaborative, and Federated Learning (DeCaF) at MICCAI22
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Mira, A., Lamata, P., Pushparajah, K., Abraham, G., Mauger, C. A., McCulloch, A. D., Omens, J., Bissell, M. M., Blair, Z., Huffaker, T., Tandon, A., Engelhardt, S., Koehler, S., Pickardt, T., Beerbaum, P., Sarikouch, S., Latus, H., Greil, G., Young, A., Hussain, T.
Le Coeur en Sabot: Shape Associations with Adverse Events in Repaired Tetralogy of
Fallot.
In: Journal of Cardiovascular Magnetic Resonance 2022
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Laves, M. H., Toelle, M., Schlaefer, A., Engelhardt, S.
Posterior temperature optimized Bayesian models for inverse problems in medical imaging
In: Medical Image Analysis Volume 78 May 2022
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Koehler, S., Sharan, L., Kuhm, J., Ghanaat, A., Gordejeva, J., Simon, N. K., Grell, N. M., André, F., Engelhardt, S.
Comparison of Evaluation Metrics for Landmark Detection in CMR Images
In: Bildverarbeitung in der Medizin (BVM), Informatik aktuell. Springer Vieweg, Wiesbaden 2022
PDF | Preprint | Code | BibTeX

Burger, L., Sharan, L., Fischer, S., Brand, J., Hehl, M., Romano, G., Karck, M., De Simone, R., Wolf, I., Engelhardt, S.
Comparison of Depth Estimation Setups from Stereo Endoscopy and Optical Tracking for Point Measurements
In: Bildverarbeitung in der Medizin (BVM), Informatik aktuell. Springer Vieweg, Wiesbaden 2022
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Laves, M.H., Toelle, M., Schlaefer, A., Engelhardt, S.
Posterior Temperature Optimization in Variational Inference for Inverse Problems.
In: Bayesian Deep Learning Workshop (NeurIPS) 2021
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Sharan, L., Romano, G., Brand, J., Kelm, H., Karck, M., De Simone, R., Engelhardt, S.
Point detection through multi-instance deep heatmap regression for sutures in endoscopy
In: International Journal of Computer Assisted Radiology and Surgery 2021
PDF | Preprint | Code | BibTeX 

Cordes, J., Enzlein, T., Marsching, C., Hinze, M., Engelhardt, S., Hopf, C., Wolf, I.
M²aia—Interactive, fast, and memory-efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data
In: GigaScience, Volume 10, Issue 7, July 2021
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Toelle, M., Laves, M., Schlaefer, A.
A Mean-Field Variational Inference Approach to Deep Image Prior for Inverse Problems in Medical Imaging
In: MIDL 2021
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Sharan, L., Romano, G., Koehler, S., Kelm, H., Karck, M., De Simone, R., Engelhardt, S.
Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation
In: IEEE JBHI 2021
PDF | Preprint | Code | BibTeX

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
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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 | Preprint | Code |  BibTeX

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
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Reinke, A., et al.
Common Limitations of Image Processing Metrics: A Picture Story
In: arXiv
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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
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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
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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
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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
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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 - (3^rd Place CURAC Best Poster Award)
PDF | BibTeX | Video

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)
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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)
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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 | Preprint | Code | Dataset | BibTeX

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
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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
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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
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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
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