Deep Learning-Based Lumen and Vessel Segmentation of Intravascular Ultrasound Images in Coronary Artery Disease.

ABSTRACT:

Background and objectives

Intravascular ultrasound (IVUS) evaluation of coronary artery morphology is based on the lumen and vessel segmentation. This study aimed to develop an automatic segmentation algorithm and validate the performances for measuring quantitative IVUS parameters.

Methods

A total of 1,063 patients were randomly assigned, with a ratio of 4:1 to the training and test sets. The independent data set of 111 IVUS pullbacks was obtained to assess the vessel-level performance. The lumen and external elastic membrane (EEM) boundaries were labeled manually in every IVUS frame with a 0.2-mm interval. The Efficient-UNet was utilized for the automatic segmentation of IVUS images.

Results

At the frame-level, Efficient-UNet showed a high dice similarity coefficien

SUBMITTER: Jeong GJ 

PROVIDER: S-EPMC10784613 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature