Sandy Engelhardt

VLDB 2026 Research / reviewers in the wild / expert

dblp:45/7703 · DBLP profile ↗

← Back

12ranked-venue papers

3as first author

6since 2021 · last 2026

0000-0001-8816-7654ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 9 · 2 first-author · 5 since 2021Graphics, computer vision, multimedia, augmented reality and games · 7 · 3 first-author · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author

Year	Publication	Venue	Position
2026	Deformable image registration for self-supervised cardiac phase detection in cardiac magnetic resonance images of patients with various diseases abstract Cardiovascular magnetic resonance (CMR) is widely used to assess cardiac function, but individual cardiac cycles complicate automatic temporal comparison and sub-phase analysis. Accurate cardiac keyframe detection can eliminate this problem. However, automatic methods solely derive end-systole (ES) and end-diastole (ED) frames from left ventricular volume curves, which do not provide a deeper insight into myocardial motion. We propose a self-supervised deep learning method detecting five keyframes in short-axis (SAX) and four-chamber (4CH) cine CMR. Initially, dense deformable registration fields are derived from CMR to compute a 1D motion descriptor encoding global cardiac contraction and relaxation patterns. Keyframes are derived from these characteristic curves with a set of rules. The method was independently evaluated for both views using four databases encompassing multiple centre, vendor and disease. M&Ms-2 (n=360) was used for training and evaluation; M&Ms (n=345) and ACDC (n=100) for repeatability control. Generalisability to patients with rare congenital heart defects was tested using the German Competence Network (GCN) database. A disease-stratified analysis confirmed stable performance across cardiomyopathies and congenital abnormalities. Our method improved detection accuracy by 49%/59% for SAX and 31%/39% for 4CH in ED/ES over the volume-based approach, with mean cyclic frame difference (cFD) below 1.3 and 1.2 frames for SAX and 4CH respectively. Our framework enables temporally aligned inter- and intra-patient analysis of cardiac dynamics, irrespective of cycle or phase lengths for aligned strain analysis or temporal normalisation. Code and annotations are available at: https://github.com/Cardio-AI/cmr-multi-view-phase-detection.git. Sarah Kaye Mueller, Sven Koehler, Jonathan Kiekenap, Gerald Greil, Tarique Hussain, Samir Sarikouch, Florian André, Norbert Frey, Sandy Engelhardt	Medical Image Anal.	9
2024	FUNAvg: Federated Uncertainty Weighted Averaging for Datasets with Diverse Labels Malte Tölle, Fernando Navarro, Sebastian Eble, Ivo Wolf, Bjoern Menze, Sandy Engelhardt	MICCAI (10)	6
2023	Why is the Winner the Best? abstract International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Tizabi, Fabian Isensee, Tim Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David Gage Ellis, Sandy Engelhardt, Melanie Ganz-Benjaminsen, Noha M. Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias P. Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, A. Emre Kavur, Oldrich Kodym, Michal Kozubek 0001, Jianning Li 0002, Hongwei Li 0004, Jun Ma 0016, Carlos Martín-Isla, Bjoern Menze, J. Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patino, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin M. H. van Wijnen, Martin Wagner 0001, D. Wei, Amine Yamlahi, Moi Hoon Yap, C. Yuan, Maximilian Zenk, A. Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, J. Cho, C. Choi, Qi Dou 0001, Ivan Ezhov, Christoph M. Friedrich, C. Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García-Faura, Maria Grammatikopoulou, S. Hong, Mostafa Jahanifar, I. Jang, Abdolrahim Kadkhodamohammadi, I. Kang, Florian Kofler, S. Kondo, Hugo J. Kuijf, M. Luu, Tomaz Martincic, Pedro Morais, Mohamed A. Naser, Bruno Oliveira 0002, David Owen 0001, S. Pang, Szymon Plotka, Élodie Puybareau, Nasir M. Rajpoot, K. Ryu, Numan Saeed, Adam J. Shephard, Dejan Stepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Hélène Urien, João L. Vilaça, Kareem A. Wahid, Benedikt Wiestler, Marek Wodzinski, F. Xia, J. Xie, Z. Xiong, Sen Yang 0006, Klaus H. Maier-Hein, Paul F. Jaeger, Annette Kopp-Schneider, Lena Maier-Hein	CVPR	24
2022	Posterior temperature optimized Bayesian models for inverse problems in medical imaging Max-Heinrich Laves, Malte Tölle, Alexander Schlaefer, Sandy Engelhardt	Medical Image Anal.	4
2022	Mutually Improved Endoscopic Image Synthesis and Landmark Detection in Unpaired Image-to-Image Translation abstract The CycleGAN framework allows for unsupervised image-to-image translation of unpaired data. In a scenario of surgical training on a physical surgical simulator, this method can be used to transform endoscopic images of phantoms into images which more closely resemble the intra-operative appearance of the same surgical target structure. This can be viewed as a novel augmented reality approach, which we coinedHyperrealismin previous work. In this use case, it is of paramount importance to display objects like needles, sutures or instruments consistent in both domains while altering the style to a more tissue-like appearance. Segmentation of these objects would allow for a direct transfer, however, contouring of these, partly tiny and thin foreground objects is cumbersome and perhaps inaccurate. Instead, we propose to use landmark detection on the points when sutures pass into the tissue. This objective is directly incorporated into a CycleGAN framework by treating the performance of pre-trained detector models as an additional optimization goal. We show that a task defined on these sparse landmark labels improves consistency of synthesis by the generator network in both domains. Comparing a baseline CycleGAN architecture to our proposed extension (DetCycleGAN), mean precision (PPV) improved by$+61.32$, mean sensitivity (TPR) by$+37.91$, and mean$F_1$score by$+0.4743$. Furthermore, it could be shown that by dataset fusion, generated intra-operative images can be leveraged as additional training data for the detection network itself. Lalith Sharan, Gabriele Romano, Sven Koehler, Halvar Kelm, Matthias Karck, Raffaele De Simone, Sandy Engelhardt	IEEE J. Biomed. Health Informatics	7
2021	Unsupervised Domain Adaptation From Axial to Short-Axis Multi-Slice Cardiac MR Images by Incorporating Pretrained Task Networks abstract Anisotropic multi-slice Cardiac Magnetic Resonance (CMR) Images are conventionally acquired in patient-specific short-axis (SAX) orientation. In specific cardiovascular diseases that affect right ventricular (RV) morphology, acquisitions in standard axial (AX) orientation are preferred by some investigators, due to potential superiority in RV volume measurement for treatment planning. Unfortunately, due to the rare occurrence of these diseases, data in this domain is scarce. Recent research in deep learning-based methods mainly focused on SAX CMR images and they had proven to be very successful. In this work, we show that there is a considerable domain shift between AX and SAX images, and therefore, direct application of existing models yield sub-optimal results on AX samples. We propose a novel unsupervised domain adaptation approach, which uses task-related probabilities in an attention mechanism. Beyond that, cycle consistency is imposed on the learned patient-individual 3D rigid transformation to improve stability when automatically re-sampling the AX images to SAX orientations. The network was trained on 122 registered 3D AX-SAX CMR volume pairs from a multi-centric patient cohort. A mean 3D Dice of 0.86 ± 0.06 for the left ventricle, 0.65 ± 0.08 for the myocardium, and 0.77 ± 0.10 for the right ventricle could be achieved. This is an improvement of 25% in Dice for RV in comparison to direct application on axial slices. To conclude, our pre-trained task module has neither seen CMR images nor labels from the target domain, but is able to segment them after the domain gap is reduced. Code: https://github.com/Cardio-AI/3d-mri-domain-adaptation. Sven Koehler, Tarique Hussain, Zach Blair, Tyler Huffaker, Florian Ritzmann, Animesh Tandon, Thomas Pickardt, Samir Sarikouch, Heiner Latus, Gerald Greil, Ivo Wolf, Sandy Engelhardt	IEEE Trans. Medical Imaging	12
2020	Temporal Views of Flattened Mitral Valve Geometries abstract The mitral valve, one of the four valves in the human heart, controls the bloodflow between the left atrium and ventricle and may suffer from various pathologies. Malfunctioning valves can be treated by reconstructive surgeries, which have to be carefully planned and evaluated. While current research focuses on the modeling and segmentation of the valve, we base our work on existing segmentations of patient-specific mitral valves, that are also time-resolved ( 3D+t) over the cardiac cycle. The interpretation of the data can be ambiguous, due to the complex surface of the valve and multiple time steps. We therefore propose a software prototype to analyze such 3D+t data, by extracting pathophysiological parameters and presenting them via dimensionally reduced visualizations. For this, we rely on an existing algorithm to unroll the convoluted valve surface towards a flattened 2D representation. In this paper, we show that the 3D+t data can be transferred to 3D or 2D representations in a way that allows the domain expert to faithfully grasp important aspects of the cardiac cycle. In this course, we not only consider common pathophysiological parameters, but also introduce new observations that are derived from landmarks within the segmentation model. Our analysis techniques were developed in collaboration with domain experts and a survey showed that the insights have the potential to support mitral valve diagnosis and the comparison of the pre- and post-operative condition of a patient. Pepe Eulzer, Sandy Engelhardt, Nils Lichtenberg, Raffaele De Simone, Kai Lawonn	IEEE Trans. Vis. Comput. Graph.	2
2019	Cross-Domain Conditional Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training Sandy Engelhardt, Lalith Sharan, Matthias Karck, Raffaele De Simone, Ivo Wolf	MICCAI (5)	1
2019	Generating Large Labeled Data Sets for Laparoscopic Image Processing Tasks Using Unpaired Image-to-Image Translation Micha Pfeiffer, Isabel Funke, Maria Robu, Sebastian Bodenstedt, Leon Strenger, Sandy Engelhardt, Tobias Roß, Matthew J. Clarkson, Kurinchi Gurusamy, Brian R. Davidson, Lena Maier-Hein, Carina Riediger, Thilo Welsch, Jürgen Weitz, Stefanie Speidel	MICCAI (5)	6
2018	Improving Surgical Training Phantoms by Hyperrealism: Deep Unpaired Image-to-Image Translation from Real Surgeries Sandy Engelhardt, Raffaele De Simone, Peter M. Full, Matthias Karck, Ivo Wolf	MICCAI (1)	1
2018	Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? abstract Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions. Olivier Bernard 0001, Alain Lalande, Clément Zotti, Frederic Cervenansky, Xin Yang 0009, Pheng-Ann Heng, Irem Cetin, Karim Lekadir, Oscar Camara 0001, Miguel Ángel González Ballester, Gerard Sanroma, Sandy Napel, Steffen E. Petersen, Georgios Tziritas, Ilias Grinias, Mahendra Khened, Alex Varghese, Ganapathy Krishnamurthi, Marc-Michel Rohé, Xavier Pennec, Maxime Sermesant, Fabian Isensee, Paul F. Jaeger, Klaus H. Maier-Hein, Peter M. Full, Ivo Wolf, Sandy Engelhardt, Christian F. Baumgartner, Lisa M. Koch, Jelmer M. Wolterink, Ivana Isgum, Yeonggul Jang, Yoonmi Hong, Jay Patravali, Shubham Jain 0006, Olivier Humbert, Pierre-Marc Jodoin	IEEE Trans. Medical Imaging	27
2009	MoleARlert - an augmented reality game based on Lemmings abstract In this poster, we present our outdoor AR game MoleARlert. The idea behind this research prototype was to exploit the enormous potential of AR in combination with classical game play based on the well-known game Lemmings by Psygnosis. Real players interact with virtual moles on a real playing field. The moles are guided through the hazardous environment to their final goal. The multiplayer game is observed through a stationary video see-through monitor. The team leader directs the real players around the field in order for them to steer the game with special markers and human gestures. Thus, the game requires a lot of action from players and is very entertaining. Sandy Engelhardt, Annabell Langs, Gerrit Lochmann, Irini Schmidt, Stefan Müller 0002	ISMAR	1