Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate

no code implementations • 4 Mar 2025 • Leonardo Geronzi, Antonio Martinez, Michel Rochette, Kexin Yan, Aline Bel-Brunon, Pascal Haigron, Pierre Escrig, Jacques Tomasi, Morgan Daniel, Alain Lalande, Siyu Lin, Diana Marcela Marin-Castrillon, Olivier Bouchot, Jean Porterie, Pier Paolo Valentini, Marco Evangelos Biancolini

In this study, we evaluate and compare the ability of local and global shape features to predict ascending aortic aneurysm growth.

Prediction

Beyond Strong labels: Weakly-supervised Learning Based on Gaussian Pseudo Labels for The Segmentation of Ellipse-like Vascular Structures in Non-contrast CTs

no code implementations • 5 Feb 2024 • Qixiang Ma, Antoine Łucas, Huazhong Shu, Adrien Kaladji, Pascal Haigron

On the local dataset, our weakly-supervised learning approach based on pseudo labels outperforms strong-label-based fully-supervised learning (1. 54\% of Dice score on average), reducing labeling time by around 82. 0\%.

Weakly-supervised Learning

Deep Supervision by Gaussian Pseudo-label-based Morphological Attention for Abdominal Aorta Segmentation in Non-Contrast CTs

no code implementations • 4 Feb 2024 • Qixiang Ma, Antoine Lucas, Adrien Kaladji, Pascal Haigron

The segmentation of the abdominal aorta in non-contrast CT images is a non-trivial task for computer-assisted endovascular navigation, particularly in scenarios where contrast agents are unsuitable.

Deep Learning Pseudo Label