3 papers
eess.IV2025
Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate
Leonardo Geronzi, Antonio Martinez, Michel Rochette +13
Objective: ascending aortic aneurysm growth prediction is still challenging in clinics. In this study, we evaluate and compare the ability of local and global shape features to pre…
eess.IV2024
Deep Supervision by Gaussian Pseudo-label-based Morphological Attention for Abdominal Aorta Segmentation in Non-Contrast CTs
Qixiang Ma, Antoine Lucas, Adrien Kaladji +1
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 agen…
eess.IV2024
Beyond Strong labels: Weakly-supervised Learning Based on Gaussian Pseudo Labels for The Segmentation of Ellipse-like Vascular Structures in Non-contrast CTs
Qixiang Ma, Antoine Łucas, Huazhong Shu +2
Deep-learning-based automated segmentation of vascular structures in preoperative CT scans contributes to computer-assisted diagnosis and intervention procedure in vascular disease…