563 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University College LondonGB43 papers
- UCL Queen Square Institute of NeurologyGB15 papers
- King's College LondonGB11 papers
- Wellcome Centre for Human NeuroimagingGB8 papers
- Wellcome / EPSRC Centre for Interventional and Surgical SciencesGB8 papers
- Queen Mary University of LondonGB7 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- Sorbonne UniversitéFR4 papers
- Assistance Publique – Hôpitaux de ParisFR3 papers
- Harvard UniversityUS3 papers
- InsermFR3 papers
- Massachusetts General HospitalUS3 papers
4 papers · 1 filter
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…
Simulation of Brain Resection for Cavity Segmentation Using Self-Supervised and Semi-Supervised Learning
Fernando Pérez-García, Roman Rodionov, Ali Alim-Marvasti +3
Resective surgery may be curative for drug-resistant focal epilepsy, but only 40% to 70% of patients achieve seizure freedom after surgery. Retrospective quantitative analysis coul…
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…
Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss
Guotai Wang, Jonathan Shapey, Wenqi Li +7
Automatic segmentation of vestibular schwannoma (VS) tumors from magnetic resonance imaging (MRI) would facilitate efficient and accurate volume measurement to guide patient manage…