6 papers · 1 filter
Self-Supervised Temporal Regularization for Landmark-Based Cardiac Segmentation with Automatic AHA Regional Mapping
David Montalvo-GarcÃa, Nicolás Gaggion, MarÃa J. Ledesma-Carbayo +1
Graph-based cardiac segmentation with implicit anatomical correspondences provides topological guarantees and population-level analysis capabilities, but models trained on independ…
ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality
Matias Cosarinsky, Ramiro Billot, Lucas Mansilla +5
Assessing the quality of automatic image segmentation is crucial in clinical practice, but often very challenging due to the limited availability of ground truth annotations. Rever…
Mask-HybridGNet: Graph-based segmentation with emergent anatomical correspondence from pixel-level supervision
Nicolás Gaggion, Maria J. Ledesma-Carbayo, Stergios Christodoulidis +2
Graph-based medical image segmentation represents anatomical structures using boundary graphs, providing fixed-topology landmarks and inherent population-level correspondences. How…
CheXmask-U: Quantifying uncertainty in landmark-based anatomical segmentation for X-ray images
Matias Cosarinsky, Nicolas Gaggion, Rodrigo Echeveste +1
In this work, we study uncertainty estimation for anatomical landmark-based segmentation on chest X-rays. Inspired by hybrid neural network architectures that combine standard imag…
ChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping
Nicolás Gaggion, Noelia A. Boccardo, Rodrigo Bonazzola +17
Plant developmental plasticity, particularly in root system architecture, is fundamental to understanding adaptability and agricultural sustainability. ChronoRoot 2.0 builds upon e…
Fitting Skeletal Models via Graph-based Learning
Nicolás Gaggion, Enzo Ferrante, Beatriz Paniagua +1
Skeletonization is a popular shape analysis technique that models an object's interior as opposed to just its boundary. Fitting template-based skeletal models is a time-consuming p…