Jianning Li
01 / Overview

Medical AI Researcher

My research focuses on computer vision, deep generative modeling, and geometric representation learning applied to medical image analysis and computer-assisted interventions. I develop computational frameworks for 3D anatomical shape reconstruction, statistical shape modeling, and interpretable clinical decision support, with primary applications in craniofacial, dental, and neurosurgical image analysis. My methodological work emphasizes computational efficiency, uncertainty estimation, and open-source benchmark development for clinical translation.

Medical Image Analysis Computer Vision Deep Generative Models Interpretable AI Disentangled Representation Learning
Google Scholar
Google Scholar Publications & Citations
GitHub
GitHub Open Source Code
📌

Open to collaborations on papers & grants, research discussions, and Bachelor/Master thesis supervision.

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Paper Demo GitHub
02 / Biography

Background & Education

Jianning Li is a Researcher at Zuse Institute Berlin and a Lecturer at Free University of Berlin and Charité – Universitätsmedizin Berlin. He earned his Ph.D. and M.S. in Computer Science (both with distinction) and his B.S. in Biomedical Engineering (with distinction).

His research primary centers on 3D medical shape reconstruction, deep generative modeling, Bayesian neural networks for uncertainty quantification, and sparse convolutional architectures. He has led international benchmark challenges in cranial implant design (AutoImplant @ MICCAI) and co-organized workshops on medical shape analysis (ShapeMI, MAD).

In addition to computational research, he actively contributes to academic service as a peer reviewer for journals and conferences including Medical Image Analysis (MedIA), IEEE Transactions on Medical Imaging (TMI), and MICCAI, while supervising student research projects bridging computer science and clinical medicine.

Education

Ph.D. Computer Science With Distinction
M.S. Computer Science With Distinction
B.S. Biomedical Engineering With Distinction
Download CV
03 / News

Latest Updates

Open
2025

Multiple Master's thesis topics available in medical image analysis and deep learning. Check the prospective students page for details.

Closed
2024

We were looking for a research student assistant. This position is now closed.

Conference
Oct 2023

Attended MICCAI 2023 in Vancouver, Canada (Oct 8–15).

Collaboration
Ongoing

Actively seeking collaborators for joint papers and grant proposals in AI-powered clinical imaging. Reach out to discuss!

04 / Selected Projects

Research Projects

01

MONAI Skull Reconstruction

Open-source skull reconstruction pipeline built on MONAI. Provides a reproducible, community-friendly framework for cranial implant design.

MONAIOpen Source3D Deep Learning
View Project →
02

Sparse CNN for Shape Completion & Super-resolution

Sparse convolutional neural networks for high-resolution skull shape completion and shape super-resolution from low-resolution inputs.

Sparse CNNShape CompletionSuper-resolution
View Project →
03

Registration-based Shape Completion

A registration-driven approach to skull shape completion, leveraging image registration to complete missing anatomical regions.

RegistrationShape CompletionSkull
View Project →
04

Coarse-to-Fine Shape Completion

A coarse-to-fine (C2F) framework for high-resolution skull shape completion, enabling detailed implant design from CT scans.

C2FHigh-resolutionImplant Design
View Project →
05

Cloud Deployment for Shape Completion

Deploying deep learning skull shape completion models to the cloud — a practical example of clinical AI deployment.

CloudDeploymentDeep Learning
View Project →
06

Patch-wise Skull Reconstruction

Patch-based approach to skull reconstruction, enabling scalable processing of large volumetric CT data for cranioplasty planning.

Patch-wiseSkullReconstruction
View Project →
05 / Publications

Selected Publications

✨ 2x CVPR  ·  4x MedIA  ·  1x TMI  ·  1x Cover  ·  1x Highlight

2026

Comparative Analysis of 3D Root Canal Structures using Micro-CT

2026 Oral Health Research Congress, Continental European Division (CED-IADR), Lisbon, Portugal, August 2026

Li, J., Bitter, K., AL, M.A., Nguyen, A.D., Shemesh, H., Zaslansky, P. and Zachow, S.

Abstract Website
2026

Uncertainty estimation and probabilistic skull shape reconstruction using Bayesian neural networks.

Scientific Reports, Volume 16, Article 16383, May 2026

Li, J., Sengupta, A. and Zachow, S.

Paper Project Code
2026

Globally Optimal Pose from Orthographic Silhouettes.

CVPR 2026CVPR

Sengupta, A., Kuş, D., Li, J., Zachow, S.

Paper Website
2025

Computational Insights into Root Canal Treatment: A Survey of Selected Methods in Imaging, Segmentation, Morphological Analysis, and Clinical Management.

Dentistry Journal, Volume 13, Issue 12 — Featured Cover PaperCover

Li, J., Bitter, K., et al.

Paper Cover
2025

MedShapeNet – a large-scale dataset of 3D medical shapes for computer vision.

Biomedical Engineering / Biomedizinische Technik, Volume 70, Issue 1Highlight

Li, J., Zhou, Z., Yang, J., Pepe, A., Gsaxner, C., Luijten, G., et al.

Paper
2023

Why is the winner the best?

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19955–19966CVPR

Eisenmann, M., Reinke, A., …, Li, J., …, et al.

Paper
2024

How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation.

BraTS 2023 — First Place1st Place

Ferreira, A., Solak, N., Li, J., Dammann, P., Kleesiek, J., Alves, V. and Egger, J.

Paper
2023

The HoloLens in medicine: A systematic review and taxonomy.

Medical Image Analysis, Volume 85, p. 102757MedIA

Gsaxner, C., Li, J., Pepe, A., et al.

Paper
2023

Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge.

Medical Image Analysis, Volume 88, August 2023, 102865MedIA

Li, J., Ellis, D.G., Kodym, O., Rauschenbach, L., Rieß, et al.

Paper
2023

Anatomy Completor: A Multi-class Completion Framework for 3D Anatomy Reconstruction.

International Workshop on Shape in Medical Imaging (ShapeMI 2023), pp. 1–14. Springer.

Li, J., Pepe, A., Luijten, G., Schwarz-Gsaxner, C., et al.

Paper
2023

Sparse convolutional neural network for high-resolution skull shape completion and shape super-resolution.

Scientific Reports

Li, J., Gsaxner, C., Pepe, A., et al.

Paper Demo
2022

Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder.

Medical Applications with Disentanglements (MAD 2022). Springer, Cham.

Li, J., Fragemann, J., et al.

Paper arXiv Code
2021

AutoImplant 2020 — First MICCAI Challenge on Automatic Cranial Implant Design.

IEEE Transactions on Medical Imaging (TMI), DOI: 10.1109/TMI.2021.3077047TMI

Li, J., Pimentel, P., Szengel, A., Ehlke, M., Lamecker, H., et al.

Paper Code
2021

Automatic Skull Defect Restoration and Cranial Implant Generation for Cranioplasty.

Medical Image Analysis (MedIA), 2021MedIA

Li, J., von Campe, G., Pepe, A., Gsaxner, C., et al.

Paper Code
2021

Inside-Out Instrument Tracking for Surgical Navigation in Augmented Reality.

27th ACM Symposium on Virtual Reality Software and Technology, pp. 1–11

Gsaxner, C., Li, J., Pepe, A., Schmalstieg, D. and Egger, J.

Paper
2021

An Online Platform for Automatic Skull Defect Restoration and Cranial Implant Design.

Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE

Li, J., Pepe, A., Gsaxner, C. and Egger, J.

Paper Project
2020

A Baseline Approach for AutoImplant: The MICCAI 2020 Cranial Implant Design Challenge.

Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures, pp. 75–84. Springer, Cham.

Li, J., Pepe, A., Gsaxner, C., von Campe, G. and Egger, J.

Paper Project
2020

Detection, segmentation, simulation and visualization of aortic dissections: A review.

Medical Image Analysis (MedIA), 65, p. 101773MedIA

Pepe, A.*, Li, J.* (*Equal contribution), Rolf-Pissarczyk, M., Gsaxner, C., Chen, X., Holzapfel, G.A. and Egger, J.

Paper
2020

Medical image segmentation in oral-maxillofacial surgery.

Computer-Aided Oral and Maxillofacial Surgery, pp. 1–27

Li, J., Erdt, M., Janoos, F., Chang, T.C. and Egger, J.

Paper
View all publications on Google Scholar
06 / Books & Proceedings

Books & Proceedings

Shape in Medical Imaging

International Workshop, ShapeMI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023.

Editor / Organizer View on Springer →

Medical Applications with Disentanglements

First MICCAI Workshop, MAD 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022.

Editor / Organizer View on Springer →

Towards the Automatization of Cranial Implant Design in Cranioplasty II

Second Challenge, AutoImplant 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021.

Editor / Challenge Lead View on Springer →

Towards the Automatization of Cranial Implant Design in Cranioplasty I

First Challenge, AutoImplant 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020.

Editor / Challenge Lead View on Springer →
07 / Talks

Presentations & Talks

2023ShapeMI @ MICCAI 2023

Anatomy Completor: A Multi-class Completion Framework for 3D Anatomy Reconstruction

Oral Presentation

2023CCVL Lab @ Johns Hopkins University

MedShapeNet: A Large Scale Dataset of 3D Medical Shapes for Computer Vision

Invited Talk

20232nd UA-Ruhr Biomedical Image Analysis Graduate Seminar

Disentanglement in Neuroimage Analysis

Oral Presentation

2023EMBC — Coronary Artery Segmentation Workshop

MedShapeNet: A Large Scale Dataset of 3D Medical Shapes

Invited Talk

2022MAD Workshop @ MICCAI 2022

VAE-based Skull Shape Completion

Poster Presentation

2022IKIM Talk

Medical Shape Completion

Invited Talk

2022Wahlfach Klinik — Digitale Medizin und Künstliche Intelligenz

Medical 3D Printing

Lecture

2022PhD Seminar

Deep Learning for Cranial Implant Design: Insights from two MICCAI Challenges

PhD Seminar

2022PhD Seminar

Sparse Convolutional Neural Networks for Medical Image Analysis

PhD Seminar

2021AutoImplant Challenge @ MICCAI 2021

Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation

Oral Presentation

2020AutoImplant Challenge @ MICCAI 2020

A Baseline Approach for AutoImplant: the MICCAI 2020 Cranial Implant Design Challenge

Conference Presentation

ongoing—

Deep Learning for Cranial Implant Design (Updated)

Slides

08 / Professional Service

Academic Service

Challenge Lead Organizer

Workshop / Tutorial Co-organizer

Program Committee

Reviewer

  • MICCAI — Medical Image Computing & Computer Assisted Intervention
  • Medical Image Analysis (MedIA)
  • IEEE Transactions on Medical Imaging
  • IEEE ISBI — International Symposium on Biomedical Imaging
  • Computers in Biology and Medicine

Editorial

09 / Open-source software

Open-source software

01

Private Local Dental AI Assistant

A comprehensive, private, desktop AI application run locally for dental professionals.

DentistryOpen SourceAI AssistantLLMs
View Source Codes →
10 / Teaching

Teaching

🎓

Teaching Portfolio

Explore my complete academic teaching history, including upcoming and past lectures, specialized seminars, and pedagogical details.

View Teaching Portfolio →
🔬

Thesis Supervision

Offering Bachelor's and Master's thesis topics in:

  • Medical image segmentation
  • Interpretable AI in radiology
  • 3D reconstruction from scans
  • Generative models for augmentation
✉️

Prospective Students

Interested in a thesis or research collaboration? Reach out with your CV and a brief description of your interests.

jianningli.me@gmail.com