Segmentation of coronary calcifications with a domain knowledge-based lightweight 3D convolutional neural network

• Type: Article in International Scientific Journal
• Date: 2025

Authors

Santos, R (Author) Other; Castro, R (Author) Other; Baeza, R (Author) Other; Nunes, F (Author) Other; Filipe, VM (Author) Other; Renna, F (Author) FCUP; Paredes, H (Author) Other; Carvalho, RF (Author) Other; Pedrosa, J (Author) Other

Journal

Title: Computers in Biology and Medicine

Vol. 196

ISSN: 0010-4825

Publisher: Elsevier

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: P-019-XCS

DOI: 10.1016/j.compbiomed.2025.110798

Abstract (EN): Cardiovascular diseases are the leading cause of death in the world, with coronary artery disease being the most prevalent. Coronary artery calcifications are critical biomarkers for cardiovascular disease, and their quantification via non-contrast computed tomography is a widely accepted and heavily employed technique for risk assessment. Manual segmentation of these calcifications is a time-consuming task, subject to variability. State-of-the-art methods often employ convolutional neural networks for an automated approach. However, there is a lack of studies that perform these segmentations with 3D architectures that can gather important and necessary anatomical context to distinguish the different coronary arteries. This paper proposes a novel and automated approach that uses a lightweight three-dimensional convolutional neural network to perform efficient and accurate segmentations and calcium scoring. Results show that this method achieves Dice score coefficients of 0.93 ± 0.02, 0.93 ± 0.03, 0.84 ± 0.02, 0.63 ± 0.06 and 0.89 ± 0.03 for the foreground, left anterior descending artery (LAD), left circumflex artery (LCX), left main artery (LM) and right coronary artery (RCA) calcifications, respectively, outperforming other state-of-the-art architectures. An external cohort validation also showed the generalization of this method's performance and how it can be applied in different clinical scenarios. In conclusion, the proposed lightweight 3D convolutional neural network demonstrates high efficiency and accuracy, outperforming state-of-the-art methods and showcasing robust generalization potential.

Language: English

Type (Professor's evaluation): Scientific