477 citations · 2.1k across the 35 of their papers we have counts for
16 papers · 1 filter
An Automated Framework for Large-Scale Graph-Based Cerebrovascular Analysis
Daniele Falcetta, Liane S. Canas, Lorenzo Suppa +5
We present CaravelMetrics, a computational framework for automated cerebrovascular analysis that models vessel morphology through skeletonization-derived graph representations. The…
ReCAP: Recursive Cross Attention Network for Pseudo-Label Generation in Robotic Surgical Skill Assessment
Julien Quarez, Marc Modat, Sebastien Ourselin +2
In surgical skill assessment, the Objective Structured Assessments of Technical Skills (OSATS) and Global Rating Scale (GRS) are well-established tools for evaluating surgeons duri…
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…
A multi-organ point cloud registration algorithm for abdominal CT registration
Samuel Joutard, Thomas Pheiffer, Chloe Audigier +6
Registering CT images of the chest is a crucial step for several tasks such as disease progression tracking or surgical planning. It is also a challenging step because of the heter…
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…
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…
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…
Permutohedral Attention Module for Efficient Non-Local Neural Networks
Samuel Joutard, Reuben Dorent, Amanda Isaac +3
Medical image processing tasks such as segmentation often require capturing non-local information. As organs, bones, and tissues share common characteristics such as intensity, sha…
Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling
Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai +4
Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. Existing DPM algorithms neglect temporal dependencies among measurements, make pa…
Grey matter sublayer thickness estimation in themouse cerebellum
Da Ma, Manuel J. Cardoso, Maria A. Zuluaga +7
The cerebellar grey matter morphology is an important feature to study neurodegenerative diseases such as Alzheimer's disease or Down's syndrome. Its volume or thickness is commonl…
PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation
Mauricio Orbes Arteaga, Lauge Sørensen, M. Jorge Cardoso +6
For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear sha…
Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs
Mauricio Orbes-Arteaga, M. Jorge Cardoso, Lauge Sørensen +4
Segmenting vascular pathologies such as white matter lesions in Brain magnetic resonance images (MRIs) require acquisition of multiple sequences such as T1-weighted (T1-w) --on whi…
Robust training of recurrent neural networks to handle missing data for disease progression modeling
Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai +4
Disease progression modeling (DPM) using longitudinal data is a challenging task in machine learning for healthcare that can provide clinicians with better tools for diagnosis and…
Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration
Yipeng Hu, Marc Modat, Eli Gibson +11
One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a…
Joint registration and synthesis using a probabilistic model for alignment of MRI and histological sections
Juan Eugenio Iglesias, Marc Modat, Loic Peter +6
Nonlinear registration of 2D histological sections with corresponding slices of MRI data is a critical step of 3D histology reconstruction. This task is difficult due to the large…
Label-driven weakly-supervised learning for multimodal deformable image registration
Yipeng Hu, Marc Modat, Eli Gibson +7
Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms. We propo…