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Christina Gsaxner

Dipl.-Ing., BSc

20172021
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Research Output 2017 2019

  • 7 Conference contribution
  • 3 Paper
  • 3 Article
  • 1 Master's Thesis
2019

A Marker-Less Registration Approach for Mixed Reality–Aided Maxillofacial Surgery: a Pilot Evaluation

Pepe, A., Trotta, G. F., Mohr-Ziak, P., Gsaxner, C., Wallner, J., Bevilacqua, V. & Egger, J., 4 Sep 2019, In : Journal of digital imaging. 32, 6, p. 1008-1018 11 p.

Research output: Contribution to journalArticleResearchpeer-review

Oral Surgery
Surgery
Tissue
Helmet mounted displays
Operating rooms

A Review on Multiplatform Evaluations of Semi-Automatic Open-Source Based Image Segmentation for Cranio-Maxillofacial Surgery

Wallner, J., Schwaiger, M., Hochegger, K. M., Gsaxner, C., Zemann, W. & Egger, J., 2019, In : Computer methods and programs in biomedicine. 182, 23 p., 105102.

Research output: Contribution to journalArticleResearchpeer-review

Oral Surgery
Image segmentation
Software packages
Surgery
Licensure

Depth-Awareness in a System for Mixed-Reality Aided Surgical Procedures

Sylos Labini, M., Gsaxner, C., Pepe, A., Wallner, J., Egger, J. & Bevilacqua, V., 2019, Intelligent Computing Methodologies - 15th International Conference, ICIC 2019, Proceedings. Huang, Z-K., Hussain, A. & Huang, D-S. (eds.). Springer Verlag Heidelberg, p. 716-726 11 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11645 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Mixed Reality
Surgery
Operating rooms
Navigation
Sensing

Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data

Gsaxner, C., Roth, P. M., Egger, J. & Wallner, J., 2019, In : PLoS ONE. p. e0212550

Research output: Contribution to journalArticleResearchpeer-review

neural networks
Learning algorithms
Urinary Bladder
Positron emission tomography
learning

Learning from the Truth: Fully Automatic Ground Truth Generation for Training of Medical Deep Learning Networks

Gsaxner, C., Roth, P. M., Wallner, J. & Egger, J., 2019, Proceedings of the Joint ARW & OAGM Workshop 2019. Pichler, A., Roth, P. M., Slabatnig, R., Stübl, G. & Vincze, M. (eds.). Graz: Verlag der Technischen Universität Graz, p. 173-174

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Open Access
Image analysis
Positron emission tomography
Tomography
Neural networks
Supervised learning

Markerless Image-to-Face Registration for Untethered Augmented Reality in Head and Neck Surgery

Gsaxner, C., Pepe, A., Schmalstieg, D., Egger, J. & Wallner, J., 2019, International Conference on Medical Image Computing and Computer-Assisted Intervention. p. 236-244

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Neck
Head
Physicians
Head and Neck Neoplasms
Tomography

Pattern Recognition and Mixed Reality for Computer-Aided Maxillofacial Surgery and Oncological Assessment

Pepe, A., Trotta, G. F., Gsaxner, C., Schmalstieg, D., Wallner, J., Egger, J. & Bevilacqua, V., 2019, BMEiCON 2018 - 11th Biomedical Engineering International Conference. 8609921

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

surgery
pattern recognition
Surgery
Pattern recognition
earphones

PET-Train: Automatic Ground Truth Generation from PET Acquisitions for Urinary Bladder Segmentation in CT Images using Deep Learning

Gsaxner, C., Pfarrkirchner, B., Lindner, L., Pepe, A., Roth, P. M., Wallner, J. & Egger, J., 2019.

Research output: Contribution to conferencePaperResearchpeer-review

Positron emission tomography
ground truth
bladder
learning
Tomography

Using Synthetic Training Data for Deep Learning-Based GBM Segmentation

Lindner, L., Narnhofer, D., Weber, M., Gsaxner, C., Egger, J. & Kolodziej, M., 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Institute of Electrical and Electronics Engineers, p. 6724-6729 6 p.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Medical image processing
Neural networks
Magnetic resonance
Image segmentation
Tumors
2018

Exploit 18F-FDG enhanced urinary bladder in PET data for deep learning ground truth generation in CT scans

Gsaxner, C., Pfarrkirchner, B., Lindner, L., Jakse, N., Wallner, J., Schmalstieg, D. & Egger, J., 12 Mar 2018, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging. SPIE Press, SPIE - The International Society for Optical Engineering

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Fully Convolutional Mandible Segmentation on a valid Ground-Truth Dataset

Egger, J., Pfarrkirchner, B., Gsaxner, C., Lindner, L., Schmalstieg, D. & Wallner, J., 2018, Proceedings IEEE Engineering in Medicine and Biology Conference (EMBC).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Lower jawbone data generation for deep learning tools under MeVisLab

Pfarrkirchner, B., Gsaxner, C., Lindner, L., Jakse, N., Wallner, J., Schmalstieg, D. & Egger, J., 2018.

Research output: Contribution to conferencePaperResearchpeer-review

TuMore: generation of synthetic brain tumor MRI data for deep learning based segmentation approaches

Lindner, L., Pfarrkirchner, B., Gsaxner, C., Schmalstieg, D. & Egger, J., 2018.

Research output: Contribution to conferencePaperResearchpeer-review

Open Access
2017

Automatic urinary bladder segmentation in CT images using deep learning

Gsaxner, C., 2017

Research output: ThesisMaster's ThesisResearch