3.1k citations · 15.9k across the 136 of their papers we have counts for
12 papers · 1 filter
Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning
Guotai Wang, Wenqi Li, Maria A. Zuluaga +8
Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate…
An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation
Lorenz Berger, Eoin Hyde, M. Jorge Cardoso +1
Deep convolutional neural networks (CNNs) have shown excellent performance in object recognition tasks and dense classification problems such as semantic segmentation. However, tra…
Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks
Guotai Wang, Wenqi Li, Sebastien Ourselin +1
A cascade of fully convolutional neural networks is proposed to segment multi-modal Magnetic Resonance (MR) images with brain tumor into background and three hierarchical regions:…
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…
Model based learning for accelerated, limited-view 3D photoacoustic tomography
Andreas Hauptmann, Felix Lucka, Marta Betcke +6
Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed-up. In this work we…
Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
Carole H Sudre, Wenqi Li, Tom Vercauteren +2
Deep-learning has proved in recent years to be a powerful tool for image analysis and is now widely used to segment both 2D and 3D medical images. Deep-learning segmentation framew…
On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task
Wenqi Li, Guotai Wang, Lucas Fidon +3
Deep convolutional neural networks are powerful tools for learning visual representations from images. However, designing efficient deep architectures to analyse volumetric medical…
ToolNet: Holistically-Nested Real-Time Segmentation of Robotic Surgical Tools
Luis C. Garcia-Peraza-Herrera, Wenqi Li, Lucas Fidon +8
Real-time tool segmentation from endoscopic videos is an essential part of many computer-assisted robotic surgical systems and of critical importance in robotic surgical data scien…
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Guotai Wang, Maria A. Zuluaga, Wenqi Li +8
Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art…
Scalable multimodal convolutional networks for brain tumour segmentation
Lucas Fidon, Wenqi Li, Luis C. Garcia-Peraza-Herrera +4
Brain tumour segmentation plays a key role in computer-assisted surgery. Deep neural networks have increased the accuracy of automatic segmentation significantly, however these mod…
Fast PET reconstruction using Multi-scale Fully Convolutional Neural Networks
Jieqing Jiao, Sebastien Ourselin
Reconstruction of PET images is an ill-posed inverse problem and often requires iterative algorithms to achieve good image quality for reliable clinical use in practice, at huge co…
Disease Progression Modeling and Prediction through Random Effect Gaussian Processes and Time Transformation
Marco Lorenzi, Maurizio Filippone, Daniel C. Alexander +1
The development of statistical approaches for the joint modelling of the temporal changes of imaging, biochemical, and clinical biomarkers is of paramount importance for improving…