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News

September 23, 2024: Read an interview with Jun. Prof. Dr. Sandy Engelhardt on our novel federated learning approach for TAVI prosthesis planning and outcome prediction in the latest edition of the E-HEALTH-COM journal.

September 19, 2024: Jun. Prof. Dr. Sandy Engelhardt will give a talk on the applications of generative AI in the cardiovascular medicine at the DGA 2024 conference in Leipzig today, 19th September, at 2:45pm.

September 13, 2024: Congratulations to Andela Grizelj for receiving the 3rd prize in the Best Paper Award for the paper "On-demand mitral valve morphometrics during surgical repair" at CURAC 2024 in Leipzig. 

June 18, 2024: Congratulations to Malte Tölle and co-authors for the recently accepted MICCAI 2024 paper "FUNAvg: Federated Uncertainty Weighted Averaging for Distributed Datasets with Diverse Labels".

March 27, 2024: Jun. Prof. Dr. Sandy Engelhardt will give a talk on the role of artificial intelligence in cardiology at the 90th annual conference of DGK within the scope of eCardiology in Mannheim on Friday, 5th April, at 08:45am.

March 8, 2024: We are proud to present two works at the BVM 2024 in Erlangen:

  • Malte Tölle with “Towards Unified Multi-Modal Dataset Creation for Deep Learning Utilizing Structured Reports” on March 11th from 1pm to 3pm at the Postersession 1-3
  • Salman Ul Hassan Dar with “Effect of Training Epoch Number on Patient Data Memorization in Unconditional Latent Diffusion Models” on March 12th at 3:30pm during the session “Machine learning and artificial intelligence”

February 16, 2024: Jun. Prof. Dr. Sandy Engelhardt will give a talk at the DGTHG annual meeting on AI in cardiac surgery in Hamburg on Monday, 19th February, in the session starting at 2pm.

December 15, 2023: Jun. Prof. Sandy Engelhardt is the speaker of a novel consortium MultidimensionAI that aims to address major challenges in AI for developing personalized treatment strategies for patients with heart failure. The project is funded by the Carl Zeiss foundation with a total budget of €5 million.

November 7, 2023: Jun. Prof. Dr. Sandy Engelhardt had the honor to participate in expert discussions with the Federal President of Germany Frank-Walter Steinmeier as part of a team of leading experts in the fields of healthcare, artificial intelligence, and information technology.

October 8, 2023: We have co-organized the 3rd edition of the Deep Generative Models workshop (DGM4MICCAI) at MICCAI 2023.

September 15, 2023: Congratulations to Malte Tölle for winning the “Informatics for Life Poster Award 2023” for his poster presentation "Privacy Preserving Quantification of Heart Geometry from Partially Labelled Data Silos with Federated Learning" at the Informatics for Life Symposium "HEALTHY AI - promises and limits".

June 21, 2023: Congratulations to Georgii Kostiuchik for winning the "NDI Best Long Abstract Presentation Award" with his abstract about "Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splits" at the IPCAI 2023 in Munich.

June 15, 2023: Meet us at the IPCAI 2023 in Munich:

  • Lalith Sharan with "Comparative evaluation of three commercially available markerless depth sensors for close-range use in surgical simulation" on Tuesday June 20th at 9am at the short talk session.
  • Georgii Kostiuchik with "Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splits" on Wednesday June 21st at 2pm at the long abstract presentation.
  • Lukas Mohl with "3d-printed Patient-specific Aortic Dissection Model for Training of endovascular interventions" on Thursday June 22nd at 8:50am at the Car poster session 2.

April 29, 2023: Listen to Jun. Prof. Dr. Sandy Engelhardt in the Official EACTS Podcast: A Cut Above: Cardiothoracic insights from EACTS discussing about Artificial Intelligence (AI) in cardiothoracic surgery.

April 14, 2023: We are proud to present to the annual meeting 2023 of the DGK one talk,
"Medical 3D Printing für die Interventionsplanung" by our PhD student Roger Karl @eCardiology Friday 14.04.2023 at 12pm, and three abstracts,
"Quantification of the Learning Progress in Minimally Invasive Mitral Valve Repaier on a Patient-Specific Simulator" by our MD student Christina Wang, @ Posterbereich 11/2 on Saturday 15.04.2023 at 10am,
"The Unmet Potential of Digital Workflow Analysis for Interventional Cardiology and Cardiac Surgery" by our MD student Jimmy Chen, @ Posterbereich 11/5 on Saturday 15.04.2023 at 10am,
"Automatic classification of Agatston score from Photon Counting CT data based on myocardial radiomics analysis" by our student Maren Leidner, @ Posterbereich 17/5 on Saturday 15.04.2023 at 10am.

January 25, 2023: Our journal paper Surgical Rehearsal for Mitral Valve Repair: Personalizing Surgical Simulation by 3D-Printing from our MD student Samantha Fischer was published in The Annals of Thoracic Surgery! Congratulations!

November 19, 2022: Congratulations to Lalith Sharan for winning the I4L Best Poster Award 2022!

September 29, 2022: Congratulations to Julian Kuhm for winning the Sven-Effert-Posterpreis at Herztage DGK 2022 in Bonn.

September 15, 2022: We are proud to present three abstracts at Herztage DGK 2022 in Bonn:
Kuhm et al. (Thursday, 29th of September 2022, 2:00 to 3:30 pm) - "Automatic prediction of akinetic myocardial segments from gadolinium-free cardiac magnetic resonance images in duchenne muscular dystrophy" (nominated for the Sven Effert Poster Award)
Ghanaat et al (Thursday, 29th of September 2022, 2:00 to 3:30 pm) - "Active Learning to efficiently improve Deep Learning Segmentation in Cardiac CT Angiography"
Marx et al. (Friday, 30th September 2022, 3:30 to 5:00 pm)- "Unrolled mitral valve visualization and quantification to assess geometric  modification after surgical repair and catheter-based interventional procedures"

September 14, 2022: We are going to present three papers at this years MICCAI Workshops in Singapore:
STACOM Workshop (Sunday, 18.09.2022, 12:10 pm - GMT+8)  - Koehler et al. "Self-supervised motion descriptor for cardiac phase detection in 4D CMR based on discrete vector field estimations"
AE-CAI Workshop (Sunday, 18.09.2022, 16:10 GMT+8) - Sharan et al. "mvHOTA: A multi-view higher order tracking accuracy metric to measure temporal and spatial associations in multi-point tracking"
DeCaF Workshop (Thursday, 22.09.2022, 8:50 GMT+8) - Toelle et al. "Content-Aware Differential Privacy with Conditional Invertible Neural Networks"

July 13, 2022 Congratulations to Malte Tölle for winning the Young-DZHK-Retreat @Mannheim/Heidelberg 2022 Poster Award

June 28, 2022: Congratulations to Sven Köhler for winning the BVM 2022 Poster Award with his work on "Comparison of evaluation metrics for landmark detection in CMR images"

May the 4th be with you: Join Jun. Prof. Dr. Sandy Engelhardt for her talk Mitral Valve Repair 4.0: AI, 3D-Printing and Augmented Reality at the King's College London 10th of May 1pm London time

May the 4th be with you: Jun. Prof. Dr. Sandy Engelhardt will give a speech about Technology for Efficient Analysis (AI) in Congenital Heart Disease at Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting in London, UK May 9th 10.15am London time

April 19, 2022: AICM organized parts of the eCardiology workshops @ DKGJahrestagung

April 15, 2022 - The AdaptOR MICCAI challenge 2022 is now open!

Feb 7, 2022: We will co-organize the 2nd edition of the DGM4MICCAI 2022 Workshop! See you in Singapore!

Feb 7, 2022: Our journal paper Posterior temperature optimized Bayesian models for inverse problems in medical imaging  from our PhD student Malte Tölle was accepted in Elsevier Medical Image Analysis! Congratulations!

Dec 06, 2021 - The Miccai 2021 "AdaptOR Challenge: Deep Generative Model Challenge for Domain Adaption in Surgery" is re-opened and datasets are released under CC BY-NC-SA Licence.

November 13, 2021: We congratulate our PhD students Lalith Sharan and Roger Karl for winning the INFORMATICS4LIFE Poster Award 2021.

November 11,2021: Our talk from the Visual Intelligence Seminar Norway is online now.

October 10,2021: Register for the ELLIS Life Annual Symposium 2021 on Oct 29, 2021, organized
by the ELLIS Unit Heidelberg. We will give a talk on our latest work on AI & Medical Image Processing.

August 31, 2021 Our journal paper "Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation" from our PhD student Lalith Sharan was accepted in IEEE JBHI! Congratulations!

July 8, 2021: Our paper "A Mean-Field Variational Inference Approach to Deep Image Prior for Inverse Problems in Medical Imaging" from our PhD student Malte Tölle was accepted and presented at MIDL2021!

July 2, 2021: Lalith Sharan, our PhD student presented a poster on Quantitative Endoscopic Image Analysis at the MIDL Doctoral Symposium, 2021. 

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!

Github Repositories of AICM

Head

Jun. Prof. Dr. Sandy Engelhardt
Focus

Medical Image Processing, Deep Learning, Computer-assisted Surgery


+49 6221 56-37173

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.

Projects

Team

Head of research group

Postdoc

Scientific Staff

Employees

  • Lukas Burger, M.Sc.
    Focus

    Depth sensing with infrared stereo and structured light approaches


  • Sarah Kaye Müller, M.Sc.
    Focus

    Manufacturing Pipeline - Patient-Specific Mitral Valve Models


  • Marvin Seyfarth, B.Sc.
    Focus

    Medical Image Synthesis, Deep Learning, Statistical Physics


Medical students

  • Matthäus Bruckner
  • Jimmy Chen
    Focus

    Surgical Process Modelling for Patients with Mitral Regurgitation


  • Arman Ghanaat
    Focus

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


  • Julian Kuhm
    Focus

    Cardiac Motion Vector Field Visualization and Clustering


  • Marcello Mächerle
    Focus

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


  • Lukas Mohl
    Focus

    Development of patient-specific aortic models for hemodynamic training


  • Christina Wang
    Focus

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


Student members

  • Sebastian Eble, B.Sc.

    Master's Thesis

    Focus

    Computer Vision and Deep Learning


  • Andela Grizelj

    Bachelor's Thesis

    Focus

    Augmented Reality, Computer Vision, 3D Visualisation


  • Halvar Kelm, B.Sc.

    Student Assistant

    Focus

    Deep Learning and Computer Vision


  • Mohamad Omran Scharaf, B.Sc.

    Master's Thesis

    Focus

    Multi-Modal Deep-Learning


  • Max Wernz

    Bachelor's Thesis

    Focus

    Medical Image Synthesis, Synthetic CCTA Generation, Coronary Artery Segmentation


Abdullah Kaplican
Alexander Rogausch
Antonia Stern
Florian Ritzmann
Ibrahim Mansaray
Jean-Luc Busch
Jonathan Kloss
Julian Brand
Maren Leidner
Marie Kapusta
Maximilian Hehl
Meret Anne Siemer
Michael Salzgeber
Moritz Bednorz
Robert Kreher
Simon Sauerzapf
Ulrike Schnaithmann

Recent publications

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
PDF | BibTeX

Engelhardt, S., Dar, S.U.H., Sharan, L., André, F., Nagel, E., Thomas, S.
Artificial intelligence in cardiovascular imaging and intervention
In: Herz
PDF | BibTeX

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
PDF | BibTeX

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.
Federated Foundation Model for Cardiac CT Imaging
In: arXiv
PDF | BibTeX

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: arXiv
PDF | BibTeX

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
PDF | BibTeX

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
PDF | BibTeX

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 (to appear)
PDF | BibTeX

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)
PDF | BibTeX

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)
PDF | BibTeX

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: arXiv
Preprint | BibTeX

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
Preprint | BibTeX

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
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)
PDF | Code | BibTeX

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)
PDF | BibTeX

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)
PDF | BibTeX

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
PDF | BibTeX

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)
PDF | BibTeX

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, May 2023
PDF | Code | BibTeX

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, April 2023
PDF | BibTeX

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
PDF | BibTeX

Eisenmann, M., et al.
Biomedical image analysis competitions: The state of current participation practice
In: arXiv
Preprint | BibTeX

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
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
PDF | Preprint | Code | BibTeX

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
PDF | BibTeX

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
PDF | Code | BibTeX

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
PDF | Preprint | BibTeX

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
PDF | Preprint | Code | BibTeX

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
PDF | BibTeX

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
PDF | Code | BibTeX

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
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 | 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
PDF | Preprint | BibTeX

Reinke, A., et al.
Common Limitations of Image Processing Metrics: A Picture Story
In: arXiv
PDF | BibTeX

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
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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 - (3rd Place CURAC Best Poster Award)
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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
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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
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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
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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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