71 citations · 129 across the 12 of their papers we have counts for
12 papers
Self-Supervised Temporal Regularization for Landmark-Based Cardiac Segmentation with Automatic AHA Regional Mapping
David Montalvo-García, Nicolás Gaggion, María 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…
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…
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…
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…
Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI
Nicolás Gaggion, Benjamin A. Matheson, Yan Xia +6
Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function. Essential to this endeavour are anatomical 3D surface and volume…
Unsupervised bias discovery in medical image segmentation
Nicolás Gaggion, Rodrigo Echeveste, Lucas Mansilla +2
It has recently been shown that deep learning models for anatomical segmentation in medical images can exhibit biases against certain sub-populations defined in terms of protected…
CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images
Nicolás Gaggion, Candelaria Mosquera, Lucas Mansilla +4
The development of successful artificial intelligence models for chest X-ray analysis relies on large, diverse datasets with high-quality annotations. While several databases of ch…
Multi-center anatomical segmentation with heterogeneous labels via landmark-based models
Nicolás Gaggion, Maria Vakalopoulou, Diego H. Milone +1
Learning anatomical segmentation from heterogeneous labels in multi-center datasets is a common situation encountered in clinical scenarios, where certain anatomical structures are…
Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis
Nicolás Gaggion, Lucas Mansilla, Candelaria Mosquera +2
Anatomical segmentation is a fundamental task in medical image computing, generally tackled with fully convolutional neural networks which produce dense segmentation masks. These m…
Hybrid graph convolutional neural networks for landmark-based anatomical segmentation
Nicolás Gaggion, Lucas Mansilla, Diego Milone +1
In this work we address the problem of landmark-based segmentation for anatomical structures. We propose HybridGNet, an encoder-decoder neural architecture which combines standard…