17 citations · 19 across the 7 of their papers we have counts for
7 papers
Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges
Lasse Hansen, Wiebke Heyer, Christoph Großbröhmer +51
Medical image registration is critical for clinical applications, and fair benchmarking of different methods is essential for monitoring ongoing progress in the field. To date, the…
The Brain Resection Multimodal Image Registration (ReMIND2Reg) 2025 Challenge
Reuben Dorent, Laura Rigolo, Colin P. Galvin +8
Accurate intraoperative image guidance is critical for achieving maximal safe resection in brain tumor surgery, yet neuronavigation systems based on preoperative MRI lose accuracy…
Unified Cross-Modal Medical Image Synthesis with Hierarchical Mixture of Product-of-Experts
Reuben Dorent, Nazim Haouchine, Alexandra Golby +3
We propose a deep mixture of multimodal hierarchical variational auto-encoders called MMHVAE that synthesizes missing images from observed images in different modalities. MMHVAE's…
Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound
Hassan Rasheed, Reuben Dorent, Maximilian Fehrentz +6
We propose in this paper a texture-invariant 2D keypoints descriptor specifically designed for matching preoperative Magnetic Resonance (MR) images with intraoperative Ultrasound (…
Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound
Reuben Dorent, Erickson Torio, Nazim Haouchine +5
Intraoperative ultrasound (iUS) imaging has the potential to improve surgical outcomes in brain surgery. However, its interpretation is challenging, even for expert neurosurgeons.…
Learning Expected Appearances for Intraoperative Registration during Neurosurgery
Nazim Haouchine, Reuben Dorent, Parikshit Juvekar +5
We present a novel method for intraoperative patient-to-image registration by learning Expected Appearances. Our method uses preoperative imaging to synthesize patient-specific exp…
Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations
Reuben Dorent, Nazim Haouchine, Fryderyk Kögl +9
We introduce MHVAE, a deep hierarchical variational auto-encoder (VAE) that synthesizes missing images from various modalities. Extending multi-modal VAEs with a hierarchical laten…