Dr Steffen Bollmann

UQ Postdoct Research Fellow

Centre for Advanced Imaging

Overview

Qualifications

  • PhD, Swiss Federal Institute of Technology, Zurich

Publications

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Supervision

  • Doctor Philosophy

  • Doctor Philosophy

  • Doctor Philosophy

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Available Projects

  • Convolutional neural networks are particulary well suited to solve a variety of inverse problems in medical imaging. This project is a great chance to get involved in the field of medical image processing using deep learning techniques from image reconstruction, registration to segmentation. Prior knowledge in Python, Tensorflow/Keras, Pytorch, and Linux shell scripting are recommended.

  • This project utilizes our minimum deformation modeling pipeline (https://github.com/CAIsr/volgenmodel-nipype) to build multi-modal models of the human brain (https://www.sciencedirect.com/science/article/pii/S1046202315000110).

View all Available Projects

Publications

Journal Article

Conference Publication

Other Outputs

  • Bollmann, Steffen, Janke, Andrew, Marstaller, Lars, Reutens, David, O'Brien, Kieran and Barth, Markus (2017): GRE and QSM average 7T model. The University of Queensland. Dataset. doi:10.14264/uql.2017.178

  • Bollmann, Steffen, Janke, Andrew, Marstaller, Lars, Reutens, David, O'Brien, Kieran and Barth, Markus (2017): MP2RAGE T1-weighted average 7T model. The University of Queensland. Dataset. doi:10.14264/uql.2017.266

  • Bollmann, Steffen, Janke, Andrew, Marstaller, Lars, Reutens, David, O'Brien, Kieran and Barth, Markus (2017): Turbo Spin Echo average 7T model. The University of Queensland. Dataset. doi:10.14264/uql.2017.267

PhD and MPhil Supervision

Current Supervision

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • Convolutional neural networks are particulary well suited to solve a variety of inverse problems in medical imaging. This project is a great chance to get involved in the field of medical image processing using deep learning techniques from image reconstruction, registration to segmentation. Prior knowledge in Python, Tensorflow/Keras, Pytorch, and Linux shell scripting are recommended.

  • This project utilizes our minimum deformation modeling pipeline (https://github.com/CAIsr/volgenmodel-nipype) to build multi-modal models of the human brain (https://www.sciencedirect.com/science/article/pii/S1046202315000110).