Jianning Li

I am currently working as a project assistant at the institite of computer graphics and vision, Graz University of Technology. My research interests include computer vision, maching learning, medical image analysis and interpretable deep learning (in medicine).

Challenge News
Selected Publications

Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation. In Arxiv August 2021. [github]
Li, J., Pepe, A., Gsaxner, C., et al.

Selected Publications

AutoImplant 2020 -First MICCAI Challenge on Automatic Cranial Implant Design. In IEEE Transactions on Medical Imaging (TMI) 2021. DOI:10.1109/TMI.2021.3077047. [github][Bibtex]
Li, J., Pimentel, P., Szengel, A., Ehlke, M., Lamecker, H., et al.

A Baseline Approach for AutoImplant: The MICCAI 2020 Cranial Implant Design Challenge. In Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures, pp. 75-84. Springer, Cham, 2020. [Project Page]
Li, J., Pepe, A., Gsaxner, C., von Campe, G. and Egger, J.

An Online Platform for Automatic Skull Defect Restoration and Cranial Implant Design . In Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 11598, p. 115981Q. International Society for Optics and Photonics, 2021. [Project Page]
Li, J., Pepe, A., Gsaxner, C. and Egger, J.

Towards the Automatization of Cranial Implant Design in Cranioplasty (AutoImplant 2020 Proceedings)
First Challenge, AutoImplant 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings.
Li, J. and Egger, J(Eds.)

Medical image segmentation in oral-maxillofacial surgery (Book Chapter)
In Computer-Aided Oral and Maxillofacial Surgery. pp.1-27.
Li, J., Erdt, M., Janoos, F., Chang, T.C. and Egger, J.

Detection, segmentation, simulation and visualization of aortic dissections: A review. In Medical image analysis (MedIA), 65, p.101773.
Pepe, A., Li, J.(Joint 1st author), Rolf-Pissarczyk, M., Gsaxner, C., Chen, X., Holzapfel, G.A. and Egger, J

Automatic Skull Defect Restoration and Cranial Implant Generation for Cranioplasty. In Medical image analysis (MedIA) 2021. [Github]
Li, J., von Campe, G., Pepe, A., Gsaxner, C. et al.

Synthetic skull bone defects for automatic patient-specific craniofacial implant design.In Scientific Data 8, no. 1 (2021): 1-8. [ dataset], [Github]
Li, J., Gsaxner, C., Pepe, A., Morais, A., Alves, V., von Campe, G., Wallner, J. and Egger, J.

Dataset Descriptor for the AutoImplant Cranial Implant Design Challenge.In In Cranial Implant Design Challenge, pp. 10-15. Springer, Cham, 2020.
Li, J. and Egger, J.

SkullBreak / SkullFix – Dataset for automatic cranial implant design and a benchmark for volumetric shape learning tasks.In Data in Brief, 35, p.106902.
Kodym, O.,Li, J. (Joint 1st author), Pepe, A., Gsaxner, C., Chilamkurthy, S., Egger, J. and Španěl, M., 2021..

Structural Analysis of Complex Atrial Intramural Microstructure from A Multi-layer Model Based on Siamese Network. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 294-297). IEEE. (Interpretable AI in Medicine)
Li, J., Chen, R. and Wu, J.

Accepted Proposals
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