Jianning Li 0002

EDBT 2026 Demo / reviewers in the wild / expert

dblp:41/2242-2 · DBLP profile ↗

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10ranked-venue papers

3as first author

9since 2021 · last 2025

0000-0002-3782-9547ORCID · conflict

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

Applied, interdisciplinary, general and emerging computing · 8 · 3 first-author · 7 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021

Year	Publication	Venue	Position
2025	Deep Medial Voxels: Learned Medial Axis Approximations for Anatomical Shape Modeling abstract Shape reconstruction from imaging volumes is a recurring need in medical image analysis. Common workflows start with a segmentation step, followed by careful post-processing and, finally, ad hoc meshing algorithms. As this sequence can be time-consuming, neural networks are trained to reconstruct shapes through template deformation. These networks deliver state-of-the-art results without manual intervention, but, so far, they have primarily been evaluated on anatomical shapes with little topological variety between individuals. In contrast, other works favor learning implicit shape models, which have multiple benefits for meshing and visualization. Our work follows this direction by introducing deep medial voxels, a semi-implicit representation that faithfully approximates the topological skeleton from imaging volumes and eventually leads to shape reconstruction via convolution surfaces. Our reconstruction technique shows potential for both visualization and computer simulations. Code available at https://github.com/apepe91/dmv. Antonio Pepe 0003, Richard Schussnig, Jianning Li 0002, Christina Schwarz-Gsaxner, Dieter Schmalstieg, Jan Egger	IEEE Trans. Medical Imaging	3
2024	GAN-based generation of realistic 3D volumetric data: A systematic review and taxonomy abstract With the massive proliferation of data-driven algorithms, such as deep learning-based approaches, the availability of high-quality data is of great interest. Volumetric data is very important in medicine, as it ranges from disease diagnoses to therapy monitoring. When the dataset is sufficient, models can be trained to help doctors with these tasks. Unfortunately, there are scenarios where large amounts of data is unavailable. For example, rare diseases and privacy issues can lead to restricted data availability. In non-medical fields, the high cost of obtaining enough high-quality data can also be a concern. A solution to these problems can be the generation of realistic synthetic data using Generative Adversarial Networks (GANs). The existence of these mechanisms is a good asset, especially in healthcare, as the data must be of good quality, realistic, and without privacy issues. Therefore, most of the publications on volumetric GANs are within the medical domain. In this review, we provide a summary of works that generate realistic volumetric synthetic data using GANs. We therefore outline GAN-based methods in these areas with common architectures, loss functions and evaluation metrics, including their advantages and disadvantages. We present a novel taxonomy, evaluations, challenges, and research opportunities to provide a holistic overview of the current state of volumetric GANs. Jianning Li 0002, Kelsey L. Pomykala, Jens Kleesiek, Victor Alves, Jan Egger	Medical Image Anal.	2
2024	Corrigendum to: GAN-based generation of realistic 3D volumetric data: A systematic review and taxonomy [Medical Image Analysis 93 (2024)] Jianning Li 0002, Kelsey L. Pomykala, Jens Kleesiek, Victor Alves, Jan Egger	Medical Image Anal.	2
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	40
2023	The HoloLens in medicine: A systematic review and taxonomy abstract The HoloLens (Microsoft Corp., Redmond, WA), a head-worn, optically see-through augmented reality (AR) display, is the main player in the recent boost in medical AR research. In this systematic review, we provide a comprehensive overview of the usage of the first-generation HoloLens within the medical domain, from its release in March 2016, until the year of 2021. We identified 217 relevant publications through a systematic search of the PubMed, Scopus, IEEE Xplore and SpringerLink databases. We propose a new taxonomy including use case, technical methodology for registration and tracking, data sources, visualization as well as validation and evaluation, and analyze the retrieved publications accordingly. We find that the bulk of research focuses on supporting physicians during interventions, where the HoloLens is promising for procedures usually performed without image guidance. However, the consensus is that accuracy and reliability are still too low to replace conventional guidance systems. Medical students are the second most common target group, where AR-enhanced medical simulators emerge as a promising technology. While concerns about human-computer interactions, usability and perception are frequently mentioned, hardly any concepts to overcome these issues have been proposed. Instead, registration and tracking lie at the core of most reviewed publications, nevertheless only few of them propose innovative concepts in this direction. Finally, we find that the validation of HoloLens applications suffers from a lack of standardized and rigorous evaluation protocols. We hope that this review can advance medical AR research by identifying gaps in the current literature, to pave the way for novel, innovative directions and translation into the medical routine. Christina Schwarz-Gsaxner, Jianning Li 0002, Antonio Pepe 0003, Jens Kleesiek, Dieter Schmalstieg, Jan Egger	Medical Image Anal.	2
2023	Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge Jianning Li 0002, David Gage Ellis, Oldrich Kodym, Laurèl Rauschenbach, Christoph Rieß, Ulrich Sure, Karsten H. Wrede, Carlos M. Alvarez, Marek Wodzinski, Mateusz Daniol, Daria Hemmerling, Hamza Mahdi, Allison Clement, Evan Kim, Zachary Fishman, Cari M. Whyne, James G. Mainprize, Michael R. Hardisty, Shashwat Pathak, Chitimireddy Sindhura, Rama Krishna Sai S. Gorthi, Degala Venkata Kiran, Subrahmanyam Gorthi, Artem Kroviakov, Antonio Pepe 0003, Christina Schwarz-Gsaxner, Adam Herout, Victor Alves, Michal Spanel, Michele R. Aizenberg, Jens Kleesiek, Jan Egger	Medical Image Anal.	1
2021	Inside-Out Instrument Tracking for Surgical Navigation in Augmented Reality abstract Surgical navigation requires tracking of instruments with respect to the patient. Conventionally, tracking is done with stationary cameras, and the navigation information is displayed on a stationary display. In contrast, an augmented reality (AR) headset can superimpose surgical navigation information directly in the surgeon’s view. However, AR needs to track the headset, the instruments and the patient, often by relying on stationary infrastructure. We show that 6DOF tracking can be obtained without any stationary, external system by purely utilizing the on-board stereo cameras of a HoloLens 2 to track the same retro-reflective marker spheres used by current optical navigation systems. Our implementation is based on two tracking pipelines complementing each other, one using conventional stereo vision techniques, the other relying on a single-constraint-at-a-time extended Kalman filter. In a technical evaluation of our tracking approach, we show that clinically relevant accuracy of 1.70 mm/1.11° and real-time performance is achievable. We further describe an example application of our system for untethered end-to-end surgical navigation. Christina Schwarz-Gsaxner, Jianning Li 0002, Antonio Pepe 0003, Dieter Schmalstieg, Jan Egger	VRST	2
2021	Automatic skull defect restoration and cranial implant generation for cranioplasty Jianning Li 0002, Gord von Campe, Antonio Pepe 0003, Christina Schwarz-Gsaxner, Enpeng Wang, Xiaojun Chen 0003, Ulrike Zefferer, Martin Tödtling, Marcell Krall, Hannes Deutschmann, Ute Schäfer, Dieter Schmalstieg, Jan Egger	Medical Image Anal.	1
2021	AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design abstract The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. The codes can be found at https://github.com/Jianningli/tmi. Jianning Li 0002, Pedro Pimentel, Angelika Szengel, Moritz Ehlke, Hans Lamecker, Stefan Zachow, Laura Jovani Estacio Cerquin, Christian Doenitz, Heiko Ramm, Xiaojun Chen 0003, Franco Matzkin, Virginia F. J. Newcombe, Enzo Ferrante, David Gage Ellis, Michele R. Aizenberg, Oldrich Kodym, Michal Spanel, Adam Herout, James G. Mainprize, Zachary Fishman, Michael R. Hardisty, Amirhossein Bayat, Suprosanna Shit, Bomin Wang, Zhi Liu 0004, Matthias Eder, Antonio Pepe 0003, Christina Schwarz-Gsaxner, Victor Alves, Ulrike Zefferer, Gord von Campe, Karin Pistracher, Ute Schäfer, Dieter Schmalstieg, Bjoern Menze, Ben Glocker, Jan Egger	IEEE Trans. Medical Imaging	1
2020	Detection, segmentation, simulation and visualization of aortic dissections: A review Antonio Pepe 0003, Jianning Li 0002, Malte Rolf-Pissarczyk, Christina Schwarz-Gsaxner, Xiaojun Chen 0003, Gerhard A. Holzapfel, Jan Egger	Medical Image Anal.	2