Unsupervised clustering based coronary artery segmentation.

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2025-03-07 DOI:10.1186/s13040-025-00435-y
Belén Serrano-Antón, Manuel Insúa Villa, Santiago Pendón-Minguillón, Santiago Paramés-Estévez, Alberto Otero-Cacho, Diego López-Otero, Brais Díaz-Fernández, María Bastos-Fernández, José R González-Juanatey, Alberto P Muñuzuri
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引用次数: 0

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 (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.

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Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
期刊最新文献
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