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
11 papers · 1 filter
Evaluation of Uncertainty-Aware Multi-Software Ensembles for Hippocampal Segmentation
Gabriel Oliveira-Stahl, Anna Schroder, James Moggridge +6
Accurate hippocampal segmentation can be a useful tool for diagnosing and monitoring neurological conditions such as Alzheimer's disease and epilepsy. While numerous automated segm…
Brain tumour segmentation with incomplete imaging data
James K Ruffle, Samia Mohinta, Robert J Gray +2
The complex heterogeneity of brain tumours is increasingly recognized to demand data of magnitudes and richness only fully-inclusive, large-scale collections drawn from routine cli…
Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures
Fernando Pérez-García, Catherine Scott, Rachel Sparks +2
Detailed analysis of seizure semiology, the symptoms and signs which occur during a seizure, is critical for management of epilepsy patients. Inter-rater reliability using qualitat…
Model-based multi-parameter mapping
Yael Balbastre, Mikael Brudfors, Michela Azzarito +3
Quantitative MR imaging is increasingly favoured for its richer information content and standardised measures. However, computing quantitative parameter maps, such as those encodin…
Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentation
Vien Ngoc Dang, Francesco Galati, Rosa Cortese +7
Deep learning techniques for 3D brain vessel image segmentation have not been as successful as in the segmentation of other organs and tissues. This can be explained by two factors…
Scribble-based Domain Adaptation via Co-segmentation
Reuben Dorent, Samuel Joutard, Jonathan Shapey +7
Although deep convolutional networks have reached state-of-the-art performance in many medical image segmentation tasks, they have typically demonstrated poor generalisation capabi…
A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning
Mikael Agn, Per Munck af Rosenschöld, Oula Puonti +7
In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method comb…
An Algorithm for Learning Shape and Appearance Models without Annotations
John Ashburner, Mikael Brudfors, Kevin Bronik +1
This paper presents a framework for automatically learning shape and appearance models for medical (and certain other) images. It is based on the idea that having a more accurate s…
Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions
Zach Eaton-Rosen, Felix Bragman, Sotirios Bisdas +2
Automated medical image segmentation, specifically using deep learning, has shown outstanding performance in semantic segmentation tasks. However, these methods rarely quantify the…
NiftyNet: a deep-learning platform for medical imaging
Eli Gibson, Wenqi Li, Carole Sudre +14
Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flex…
Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks
Lucas Fidon, Wenqi Li, Luis C. Garcia-Peraza-Herrera +4
The Dice score is widely used for binary segmentation due to its robustness to class imbalance. Soft generalisations of the Dice score allow it to be used as a loss function for tr…