Unsupervised clustering based coronary artery segmentation.

ABSTRACT:

Background

The acquisition of 3D geometries of coronary arteries from computed tomography coronary angiography (CTCA) is crucial for clinicians, enabling visualization of lesions and supporting decision-making processes. Manual segmentation of coronary arteries is time-consuming and prone to errors. There is growing interest in automatic segmentation algorithms, particularly those based on neural networks, which require large datasets and significant computational resources for training. This paper proposes an automatic segmentation methodology based on clustering algorithms and a graph structure, which integrates data from both the clustering process and the original images.

Results

The study compares two approaches: a 2.5D version using axial, sagittal, and coronal slices

SUBMITTER: Serrano-Anton B 

PROVIDER: S-EPMC11887207 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature