3.1k citations · 15.9k across the 136 of their papers we have counts for
61 papers · 1 filter
UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation
Jianghao Wu, Guotai Wang, Ran Gu +6
Domain Adaptation (DA) is important for deep learning-based medical image segmentation models to deal with testing images from a new target domain. As the source-domain data are us…
Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations
Reuben Dorent, Nazim Haouchine, Fryderyk Kögl +9
We introduce MHVAE, a deep hierarchical variational auto-encoder (VAE) that synthesizes missing images from various modalities. Extending multi-modal VAEs with a hierarchical laten…
Unsupervised 3D out-of-distribution detection with latent diffusion models
Mark S. Graham, Walter Hugo Lopez Pinaya, Paul Wright +8
Methods for out-of-distribution (OOD) detection that scale to 3D data are crucial components of any real-world clinical deep learning system. Classic denoising diffusion probabilis…
LoViT: Long Video Transformer for Surgical Phase Recognition
Yang Liu, Maxence Boels, Luis C. Garcia-Peraza-Herrera +4
Online surgical phase recognition plays a significant role towards building contextual tools that could quantify performance and oversee the execution of surgical workflows. Curren…
Rapid and robust endoscopic content area estimation: A lean GPU-based pipeline and curated benchmark dataset
Charlie Budd, Luis C. Garcia-Peraza-Herrera, Martin Huber +2
Endoscopic content area refers to the informative area enclosed by the dark, non-informative, border regions present in most endoscopic footage. The estimation of the content area…
Driving Points Prediction For Abdominal Probabilistic Registration
Samuel Joutard, Reuben Dorent, Sebastien Ourselin +2
Inter-patient abdominal registration has various applications, from pharmakinematic studies to anatomy modeling. Yet, it remains a challenging application due to the morphological…
Brain Lesion Synthesis via Progressive Adversarial Variational Auto-Encoder
Jiayu Huo, Vejay Vakharia, Chengyuan Wu +4
Laser interstitial thermal therapy (LITT) is a novel minimally invasive treatment that is used to ablate intracranial structures to treat mesial temporal lobe epilepsy (MTLE). Regi…
Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models
Walter H. L. Pinaya, Mark S. Graham, Robert Gray +12
Deep generative models have emerged as promising tools for detecting arbitrary anomalies in data, dispensing with the necessity for manual labelling. Recently, autoregressive trans…
Transformer-based out-of-distribution detection for clinically safe segmentation
Mark S Graham, Petru-Daniel Tudosiu, Paul Wright +9
In a clinical setting it is essential that deployed image processing systems are robust to the full range of inputs they might encounter and, in particular, do not make confidently…
Motion Correction and Volumetric Reconstruction for Fetal Functional Magnetic Resonance Imaging Data
Daniel Sobotka, Michael Ebner, Ernst Schwartz +8
Motion correction is an essential preprocessing step in functional Magnetic Resonance Imaging (fMRI) of the fetal brain with the aim to remove artifacts caused by fetal movement an…
Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models
Guilherme Pombo, Robert Gray, Jorge Cardoso +4
We describe Countersynth, a conditional generative model of diffeomorphic deformations that induce label-driven, biologically plausible changes in volumetric brain images. The mode…
Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI
Lucas Fidon, Michael Aertsen, Nada Mufti +13
The performance of deep neural networks typically increases with the number of training images. However, not all images have the same importance towards improved performance and ro…
Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation
Reuben Dorent, Samuel Joutard, Jonathan Shapey +4
We introduce , a weakly supervised 3D approach to train a deep image segmentation network using particularly weak train-time annotations: only 6 extreme clicks…
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…
MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning
Xiangde Luo, Guotai Wang, Tao Song +6
Segmentation of organs or lesions from medical images plays an essential role in many clinical applications such as diagnosis and treatment planning. Though Convolutional Neural Ne…
An MRF-UNet Product of Experts for Image Segmentation
Mikael Brudfors, Yaël Balbastre, John Ashburner +4
While convolutional neural networks (CNNs) trained by back-propagation have seen unprecedented success at semantic segmentation tasks, they are known to struggle on out-of-distribu…
Multi-Atlas Based Pathological Stratification of d-TGA Congenital Heart Disease
Maria A. Zuluaga, Alex F. Mendelson, M. Jorge Cardoso +2
One of the main sources of error in multi-atlas segmentation propagation approaches comes from the use of atlas databases that are morphologically dissimilar to the target image. I…
Image Compositing for Segmentation of Surgical Tools without Manual Annotations
Luis C. Garcia-Peraza-Herrera, Lucas Fidon, Claudia D'Ettorre +3
Producing manual, pixel-accurate, image segmentation labels is tedious and time-consuming. This is often a rate-limiting factor when large amounts of labeled images are required, s…
Active Annotation of Informative Overlapping Frames in Video Mosaicking Applications
Loic Peter, Marcel Tella-Amo, Dzhoshkun Ismail Shakir +4
Video mosaicking requires the registration of overlapping frames located at distant timepoints in the sequence to ensure global consistency of the reconstructed scene. However, ful…
Hierarchical brain parcellation with uncertainty
Mark S. Graham, Carole H. Sudre, Thomas Varsavsky +4
Many atlases used for brain parcellation are hierarchically organised, progressively dividing the brain into smaller sub-regions. However, state-of-the-art parcellation methods ten…
Real-Time Segmentation of Non-Rigid Surgical Tools based on Deep Learning and Tracking
Luis C. García-Peraza-Herrera, Wenqi Li, Caspar Gruijthuijsen +7
Real-time tool segmentation is an essential component in computer-assisted surgical systems. We propose a novel real-time automatic method based on Fully Convolutional Networks (FC…
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…
Deep Placental Vessel Segmentation for Fetoscopic Mosaicking
Sophia Bano, Francisco Vasconcelos, Luke M. Shepherd +6
During fetoscopic laser photocoagulation, a treatment for twin-to-twin transfusion syndrome (TTTS), the clinician first identifies abnormal placental vascular connections and laser…
Combining multimodal information for Metal Artefact Reduction: An unsupervised deep learning framework
Marta B. M. Ranzini, Irme Groothuis, Kerstin Kläser +5
Metal artefact reduction (MAR) techniques aim at removing metal-induced noise from clinical images. In Computed Tomography (CT), supervised deep learning approaches have been shown…