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

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 heavil...

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Vydáno v:Computers in biology and medicine Ročník 196; číslo Pt B; s. 110798
Hlavní autoři: Santos, Rui, Castro, Rui, Baeza, Rúben, Nunes, Fábio, Filipe, Vítor M., Renna, Francesco, Paredes, Hugo, Fontes-Carvalho, Ricardo, Pedrosa, João
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Ltd 01.09.2025
Témata:
3D CNN
Calcinosis - diagnostic imaging
Calcium scoring
Cardiac CT
Convolutional Neural Networks
Coronary Artery Disease - diagnostic imaging
Coronary calcification
Coronary Vessels - diagnostic imaging
Dilated convolution
Female
Humans
Imaging, Three-Dimensional - methods
Internal Medicine
Lightweight
Male
Middle Aged
Other
Tomography, X-Ray Computed
Vascular Calcification - diagnostic imaging
3D CNN
Coronary calcification
Dilated convolution
Cardiac CT
Lightweight
Calcium scoring
ISSN:0010-4825, 1879-0534, 1879-0534
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Shrnutí: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. •3D lightweight CNN with a domain knowledge-based starting point to accurately segment coronary calcifications.•Methodology that outperforms state-of-the-art architectures.•External validation dataset that demonstrates model’s generalization to unseen cases.
Bibliografie:ObjectType-Article-1
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content type line 23
ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2025.110798