Detail

Keywords

Artificial Intelligence; Image Processing, Computer-Assisted; Machine Learning; Multimodal Imaging

Research group(s)

• CD lab­o­ra­tory for math­e­mat­i­cal mod­el­ling and sim­u­la­tion of next-generation med­ical ul­tra­sound de­vices
  Members:
  Michael Figl
  Christina Haberl
  Johannes Tischer
  Lukas Zalka
• Computational Imaging Research Lab
  Head: Georg Langs
  Research Area: Research at CIR is roughly grouped around 3 research lines: Machine Learning & Neuroimaging, Computer Aided Diagnosis and Quantification, Computer Vision and Pattern Recognition
  Members:
  Christoph Fürböck
  Franz Kainberger
  Georg Langs
  Roxane Licandro
  Philipp Seeböck
  Johannes Tischer
  Nicolas Weilguny
  Paul Weiser
• Early Life Image Analysis (ELIA)
  Head: Roxane Licandro
  Research Area: ELIA focuses on developing novel technologies to represent, analyse and understand perinatal imaging data from the fetal period until 18 years. Methodologies are tailored to age related dynamics and specifically address developmental changes and interactions with pathologic progression patterns.
  Members:
  Roxane Licandro
  Johannes Tischer
  Nicolas Weilguny

Research interests

My research focuses on multimodal medical image analysis of the brain, encompassing both healthy and pathological development. I am particularly interested in multimodal representation learning, image fusion, image registration, spatio-temporal modeling, and predictive modeling. My work emphasizes integrating complementary information from ultrasound and MRI images to provide a comprehensive understanding of brain development across different stages.

Selected publications

1. Tischer, J. et al. (2025) ‘Conditional Fetal Brain Atlas Learning for Automatic Tissue Segmentation’, Perinatal, Preterm and Paediatric Image Analysis, pp. 36–47. Available at: https://doi.org/10.1007/978-3-032-05997-0_4.
2. Tischer, J., Szeles, J.C. and Kaniusas, E. (2025) ‘Personalized auricular vagus nerve stimulation: beat-to-beat deceleration dominates in systole-gated stimulation during inspiration - a pilot study’, Frontiers in Physiology, 15. Available at: https://doi.org/10.3389/fphys.2024.1495868.
3. Mandl, S. et al. (2026) ‘Predictive value of foetal superior temporal sulcus asymmetry for neonatal speech discrimination’, Brain Communications, 8(1). Available at: https://doi.org/10.1093/braincomms/fcag048.
4. Ciceri, T. et al. (2025) ‘FetGEs: A Deep Learning Approach for Fetal MRI Ganglionic Eminence Segmentation’, Perinatal, Preterm and Paediatric Image Analysis, pp. 93–104. Available at: https://doi.org/10.1007/978-3-032-05997-0_9.
5. Shi, H. et al. (2024) ‘Revisiting the Pathophysiology of Intracranial Hemorrhage in Fetuses with Chiari II Malformation: Novel Imaging Biomarkers of Disease Severity?’, American Journal of Neuroradiology, 45(10), pp. 1562–1569. Available at: https://doi.org/10.3174/ajnr.a8331.