6 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…
Performance Estimation for Supervised Medical Image Segmentation Models on Unlabeled Data Using UniverSeg
Jingchen Zou, Jianqiang Li, Gabriel Jimenez +5
The performance of medical image segmentation models is usually evaluated using metrics like the Dice score and Hausdorff distance, which compare predicted masks to ground truth an…
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…
Deep vessel segmentation with joint multi-prior encoding
Amine Sadikine, Bogdan Badic, Enzo Ferrante +4
The precise delineation of blood vessels in medical images is critical for many clinical applications, including pathology detection and surgical planning. However, fully-automated…