Fiche publication
Date publication
avril 2025
Journal
Medical & biological engineering & computing
Auteurs
Membres identifiés du Cancéropôle Est :
Pr DE MATHELIN Michel
Tous les auteurs :
Farola Barata B, Liao G, Borghesan G, McCutcheon K, Bennett J, Rosa B, de Mathelin M, Nageotte F, Gora MJ, Vander Sloten J, Vander Poorten E, Dall'Alba D
Lien Pubmed
Résumé
Ultrasound (US) imaging enables the evaluation of vascular structures in real time, and it can provide morphological and pathological information during US-guided procedures. Automatic prediction of vascular structure boundaries can help clinicians in locating and measuring target structures more accurately and efficiently. Most existing US segmentation methods use per-pixel classification or regression, which require post-processing to obtain contour coordinates. In this work, we present ACE-Net, a novel approach that directly predicts the contour coordinates for every scanning line (A-line) in US images. ACE-Net combines two main modules: a boundary regression module that predicts the upper and lower coordinates of the target area for each A-line, and an A-line classification module that determines whether an A-line belongs to the target area or not. We evaluated our method on three clinical US datasets using, among others, dice similarity coefficient (DSC) and inference time as performance metrics. Our method outperformed state-of-the-art segmentation methods in inference time while achieving superior or comparable performance in DSC. ACE-Net is publicly available at https://github.com/bfarolabarata/ace-net .
Mots clés
Computer aided intervention, Convolutional neural network, Coordinates regression, Deep learning, Image contour segmentation, Ultrasound imaging
Référence
Med Biol Eng Comput. 2025 04 2;:
