Dipl.-Ing. Christina Gsaxner, BSc

News:

Looking for a challenge? Join the new Miccai 2020 Cranial Implant Design Challenge organized by our group. The training data set is now available!

https://autoimplant.grand-challenge.org/

Christina Gsaxner is currently working as a project assistant at the Institute of Computer Graphics and Vision at the TU Graz and in the Division of Oral- and Maxillofacial Surgery at the Medical University of Graz. She obtained her Master's degree in Biomedical Engineering with a focus on medical imaging from TU Graz. At the moment, Christina is working on the enFaced project, a interdisciplinary research project between computer science and medicine, which aims to integrate virtual and augmented reality into the field of head and neck surgery, under the supervision of Prof. Dieter Schmalstieg and Dr. Jan Egger. She is part of the Computer Algorithms for Medicine Laboratory

Christina's research interests lie in Medical Augmented Reality, Computer-assisted Interventions, Registration & Tracking in AR and Medical Image Analysis.

Publications

Dieter Schmalstieg, Jan Egger, Christina Schwarz-Gsaxner and Antonio Pepe Augmented Reality for Head and Neck Carcinoma Imaging: Description and Feasibility of an Instant Calibration, Markerless Approach Show publication in PURE
Jan Egger, Christina Schwarz-Gsaxner and Antonio Pepe Synthetic Skull Bone Defects for automatic Patient-specific Craniofacial Implant Design Show publication in PURE
Jan Egger, Christina Schwarz-Gsaxner and Antonio Pepe An Online Platform for Automatic Skull Defect Restoration and Cranial Implant Design SPIE Medical Imaging Conference 2021 Show publication in PURE
Jan Egger, Christina Schwarz-Gsaxner and Antonio Pepe Detection, segmentation, simulation and visualization of aortic dissections Show publication in PURE
Dieter Schmalstieg, Jan Egger, Christina Schwarz-Gsaxner and Antonio Pepe Semi-supervised Virtual Regression of Aortic Dissections Using 3D Generative Inpainting Show publication in PURE
Jan Egger, Christina Schwarz-Gsaxner and Antonio Pepe A baseline approach for autoimplant: the MICCAI 2020 cranial implant design challenge Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures 75-84 Show publication in PURE
Philipp Fleck, Clemens Arth, Jan Egger, Christina Schwarz-Gsaxner and Antonio Pepe Single-Shot Deep Volumetric Regression for Mobile Medical Augmented Reality Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures 64-74 Show publication in PURE
Jan Egger, Christina Schwarz-Gsaxner and Antonio Pepe Deep Learning - A first Meta-Survey of selected Reviews across Scientific Disciplines and their Research Impact Show publication in PURE
Jan Egger, Christina Schwarz-Gsaxner and Antonio Pepe Medical Deep Learning - A systematic Meta-Review Show publication in PURE
Jan Egger, Christina Schwarz-Gsaxner and Antonio Pepe A comprehensive Workflow and Framework for immersive Virtual Endoscopy of dissected Aortae from CTA Data Show publication in PURE
Dieter Schmalstieg, Jan Egger, Christina Schwarz-Gsaxner and Antonio Pepe Towards the Automatization of Cranial Implant Design in Cranioplasty: Structured description of the challenge design Show publication in PURE
Jan Egger, Christina Schwarz-Gsaxner and Antonio Pepe IRIS: interactive real-time feedback image segmentation with deep learning SPIE Medical Imaging Show publication in PURE
Jan Egger and Christina Schwarz-Gsaxner Facial model collection for medical augmented reality in oncologic cranio-maxillofacial surgery Show publication in PURE
Dieter Schmalstieg, Jan Egger, Christina Schwarz-Gsaxner and Antonio Pepe Markerless Image-to-Face Registration for Untethered Augmented Reality in Head and Neck Surgery International Conference on Medical Image Computing and Computer-Assisted Intervention 236-244 Show publication in PURE
Jan Egger, Christina Schwarz-Gsaxner, Antonio Pepe and Peter Mohr-Ziak A Marker-Less Registration Approach for Mixed Reality–Aided Maxillofacial Surgery: a Pilot Evaluation Show publication in PURE
Dieter Schmalstieg, Jan Egger, Christina Schwarz-Gsaxner and Antonio Pepe Pattern Recognition and Mixed Reality for Computer-Aided Maxillofacial Surgery and Oncological Assessment BMEiCON 2018 - 11th Biomedical Engineering International Conference Show publication in PURE
Jan Egger, Christina Schwarz-Gsaxner, Peter Roth, Antonio Pepe and Lydia Alice Lindner PET-Train: Automatic Ground Truth Generation from PET Acquisitions for Urinary Bladder Segmentation in CT Images using Deep Learning Show publication in PURE
Jan Egger, Christina Schwarz-Gsaxner and Peter Roth Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data Show publication in PURE
Jan Egger, Christina Schwarz-Gsaxner and Peter Roth Learning from the Truth: Fully Automatic Ground Truth Generation for Training of Medical Deep Learning Networks Proceedings of the Joint ARW & OAGM Workshop 2019 173-174 Show publication in PURE
Jan Egger, Christina Schwarz-Gsaxner, Dominik Narnhofer and Lydia Alice Lindner Using Synthetic Training Data for Deep Learning-Based GBM Segmentation 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 6724-6729 Show publication in PURE
Jan Egger and Christina Schwarz-Gsaxner A Review on Multiplatform Evaluations of Semi-Automatic Open-Source Based Image Segmentation for Cranio-Maxillofacial Surgery Show publication in PURE
Jan Egger, Christina Schwarz-Gsaxner and Antonio Pepe Depth-Awareness in a System for Mixed-Reality Aided Surgical Procedures Intelligent Computing Methodologies - 15th International Conference, ICIC 2019, Proceedings 716-726 Show publication in PURE
Dieter Schmalstieg, Jan Egger, Christina Schwarz-Gsaxner and Lydia Alice Lindner Fully Convolutional Mandible Segmentation on a valid Ground-Truth Dataset Proceedings IEEE Engineering in Medicine and Biology Conference (EMBC) Show publication in PURE
Dieter Schmalstieg, Jan Egger, Christina Schwarz-Gsaxner and Lydia Alice Lindner Lower jawbone data generation for deep learning tools under MeVisLab Show publication in PURE
Dieter Schmalstieg, Jan Egger, Christina Schwarz-Gsaxner and Lydia Alice Lindner TuMore: generation of synthetic brain tumor MRI data for deep learning based segmentation approaches Show publication in PURE
Dieter Schmalstieg, Jan Egger, Christina Schwarz-Gsaxner and Lydia Alice Lindner Exploit 18F-FDG enhanced urinary bladder in PET data for deep learning ground truth generation in CT scans Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging Show publication in PURE
Christina Schwarz-Gsaxner Automatic urinary bladder segmentation in CT images using deep learning Show publication in PURE
Contact
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Institute of Computer Graphics and Vision
Inffeldgasse 16/II
8010 Graz

christina.gsaxner@icg.tugraz.at