1 citations · 1 across the 5 of their papers we have counts for
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SIAM: Head and Brain MRI Segmentation from Few High-Quality Templates via Synthetic Training
Romain Valabregue, Ines Khemir, Eric Badinet +3
Synthetic training has recently advanced brain MRI segmentation by enabling contrast-agnostic models trained entirely on generated data. However, most existing approaches rely on h…
A 3D Cross-modal Keypoint Descriptor for MR-US Matching and Registration
Daniil Morozov, Reuben Dorent, Nazim Haouchine
Intraoperative registration of real-time ultrasound (iUS) to preoperative Magnetic Resonance Imaging (MRI) remains an unsolved problem due to severe modality-specific differences i…
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…
Unsupervised anomaly detection using Bayesian flow networks: application to brain FDG PET in the context of Alzheimer's disease
Hugues Roy, Reuben Dorent, Ninon Burgos
Unsupervised anomaly detection (UAD) plays a crucial role in neuroimaging for identifying deviations from healthy subject data and thus facilitating the diagnosis of neurological d…
SegMatch: A semi-supervised learning method for surgical instrument segmentation
Meng Wei, Charlie Budd, Luis C. Garcia-Peraza-Herrera +3
Surgical instrument segmentation is recognised as a key enabler in providing advanced surgical assistance and improving computer-assisted interventions. In this work, we propose Se…