65 citations · 123 across the 9 of their papers we have counts for
8 papers · 1 filter
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…
Deep Biomechanically-Guided Interpolation for Keypoint-Based Brain Shift Registration
Tiago Assis, Ines P. Machado, Benjamin Zwick +2
Accurate compensation of brain shift is critical for maintaining the reliability of neuronavigation during neurosurgery. While keypoint-based registration methods offer robustness…
A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections
Fernando Pérez-García, Reuben Dorent, Michele Rizzi +9
Accurate segmentation of brain resection cavities (RCs) aids in postoperative analysis and determining follow-up treatment. Convolutional neural networks (CNNs) are the state-of-th…
Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets
Reuben Dorent, Thomas Booth, Wenqi Li +5
Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. Established tissue segmentation approaches have, however, not been de…
Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI
Hari McGrath, Peichao Li, Reuben Dorent +6
Management of vestibular schwannoma (VS) is based on tumour size as observed on T1 MRI scans with contrast agent injection. Current clinical practice is to measure the diameter of…
Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation
Reuben Dorent, Samuel Joutard, Marc Modat +2
We propose a new deep learning method for tumour segmentation when dealing with missing imaging modalities. Instead of producing one network for each possible subset of observed mo…