Optimized aortic root segmentation during transcatheter aortic valve implantation.

ABSTRACT: Transcatheter aortic valve implantation (TAVI) is a highly effective treatment for patients with severe aortic stenosis. Accurate valve positioning is critical for successful TAVI, and highly accurate real-time visualization-with minimal use of contrast-is especially important for patients with chronic kidney disease. Under fluoroscopic conditions, which often suffer from low contrast, high noise and artifacts, automatic segmentation of anatomical structures using convolutional neural networks (CNNs) can significantly improve the accuracy of valve positioning. This paper presents a comparative analysis of various CNN architectures for automatic aortic root segmentation on angiographic images, with the aim of optimizing the TAVI process. The experimental evaluation included models such as F

SUBMITTER: Laptev NV 

PROVIDER: S-EPMC12657348 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature