65 citations · 160 across the 35 of their papers we have counts for
7 papers · 1 filter
Shape-guided Gaussian Splatting for Sparse-View X-ray 3D Reconstruction
Pranav Poudel, Florence Dell'Aniello Picard, Nairouz Shehata +2
Sparse-view X-ray 3D reconstruction is essential for reducing radiation exposure, but recovering a density field from a handful of X-ray projections is severely ill-posed. Recently…
Longitudinal tracking of multiple sclerosis lesions in the spinal cord: A validation study
Pierre-Louis Benveniste, Julian McGinnis, Shannon Kolind +11
Longitudinal characterization of multiple sclerosis (MS) lesions remains constrained by the lack of frameworks capable of establishing consistent instance-level correspondences acr…
A Joint 2D-3D Statistical Shape Model for Orthopedic Reconstruction
Florence Dell'Aniello Picard, Pranav Poudel, Nairouz Shehata +2
Three-dimensional femoral reconstruction from radiographs supports surgical planning, implant sizing, and post-operative follow-up, but remains ill-posed as X-ray projections disca…
Spatiotemporal Distillation via Recurrent Bottlenecks for Aortic Tracking
Dexter Wen Jie Teo, Nairouz Shehata, Herve Lombaert
Cardiac cine-MRI serves as a direct visual indicator of cardiovascular hemodynamics by capturing the continuous wall motion of the aorta. Quantifying these dynamic structural chang…
Deep Spectral Models for Robust Dental Shape Generation
Tibor Kubík, François Guibault, Michal Španěl +1
Accurate modeling of dental crown morphology is fundamental for diagnosis, orthodontic planning, and computer-aided restoration design. However, datasets suitable for training such…
Exploring Entropy-based Active Learning for Fair Brain Segmentation
Ghazal Danaee, Mélanie Gaillochet, Christian Desrosiers +2
Active learning (AL) has emerged as a crucial strategy for reducing the prohibitive costs associated with medical image segmentation. However, standard uncertainty-based AL methods…
Anatomically-aware conformal prediction for medical image segmentation with random walks
Mélanie Gaillochet, Christian Desrosiers, Hervé Lombaert
The reliable deployment of deep learning in medical imaging requires uncertainty quantification that provides rigorous error guarantees while remaining anatomically meaningful. Con…