RT Journal Article
T1 Unsupervised clustering based coronary artery segmentation
A1 Serrano Antón, Belén
A1 Insúa Villa, Manuel
A1 Pendón-Minguillón, Santiago
A1 Paramés Estévez, Santiago
A1 Otero Cacho, Alberto
A1 López Otero, Diego
A1 Díaz Fernández, Brais
A1 Bastos-Fernández, María
A1 González Juanatey, José Ramón
A1 Pérez Muñuzuri, Alberto
AB 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 (3Axis), and a perpendicular version (Perp), which uses the cross-section of each vessel. The methodology was tested on two patient groups: a test set of 10 patients and an additional set of 22 patients with clinically diagnosed lesions. The 3Axis method achieved a Dice score of 0.88 in the test set and 0.83 in the lesion set, while the Perp method obtained Dice scores of 0.81 in the test set and 0.82 in the lesion set, decreasing to 0.79 and 0.80 in the lesion region, respectively. These results are competitive with current state-of-the-art methods. Conclusions: This clustering-based segmentation approach offers a robust framework that can be easily integrated into clinical workflows, improving both accuracy and efficiency in coronary artery analysis. Additionally, the ability to visualize clusters and graphs from any cross-section enhances the method’s explainability, providing clinicians with deeper insights into vascular structures. The study demonstrates the potential of clustering algorithms for improving segmentation performance in coronary artery imaging.
PB BioMed Central
YR 2025
FD 2025-03-07
LK https://hdl.handle.net/10347/46984
UL https://hdl.handle.net/10347/46984
LA eng
NO Serrano-Antón, B., Insúa Villa, M., Pendón-Minguillón, S., Paramés-Estévez, S., Otero-Cacho, A., López-Otero, D., Díaz-Fernández, B., Bastos-Fernández, M., González-Juanatey, J. R., & P. Muñuzuri, A. (2025). Unsupervised clustering based coronary artery segmentation. BioData Mining, 18(1). https://doi.org/10.1186/S13040-025-00435-Y
NO This research has been supported by the Xunta de Galicia (Grant No. 2021-PG036-1), the Spanish Ministerio de Ciencia e Innovación (Grants No. PID2022-138322OB-I00, PID2022-141626NB-I00, MCIN/AEI/https://doi.org/10.13039/501100011033), European Union Next Generation EU/PRTR (Grant No: DIN2020-011068) and Interreg VI-A Spain - Portugal (Project 0330_NEW_HEART_1_E). All these programs are co-funded by ERDF (EU).
DS Minerva
RD 19 may 2026