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Thoracic Aorta Calcium Detection and Quantification Using Convolutional Neural Networks in a Large Cohort of Intermediate-Risk Patients

1
Instituto de Medicina Traslacional, Trasplante y Bioingeniería (IMeTTyB), Universidad Favaloro-CONICET, Solís 453, Buenos Aires CP 1078, Argentina
2
Cardiovascular Imaging Unit, Hôpital Européen Georges Pompidou, INSERM U970, 75015 Paris, France
*
Author to whom correspondence should be addressed.
Academic Editor: Li Yueh Hsu
Tomography 2021, 7(4), 636-649; https://doi.org/10.3390/tomography7040054
Received: 27 August 2021 / Revised: 16 October 2021 / Accepted: 19 October 2021 / Published: 28 October 2021
(This article belongs to the Section Cardiovascular Imaging)
Arterial calcification is an independent predictor of cardiovascular disease (CVD) events whereas thoracic aorta calcium (TAC) detection might anticipate extracoronary outcomes. In this work, we trained six convolutional neural networks (CNNs) to detect aortic calcifications and to automate the TAC score assessment in intermediate CVD risk patients. Cardiac computed tomography images from 1415 patients were analyzed together with their aortic geometry previously assessed. Orthogonal patches centered in each aortic candidate lesion were reconstructed and a dataset with 19,790 images (61% positives) was built. Three single-input 2D CNNs were trained using axial, coronal and sagittal patches together with two multi-input 2.5D CNNs combining the orthogonal patches and identifying their best regional combination (BRC) in terms of lesion location. Aortic calcifications were concentrated in the descending (66%) and aortic arch (26%) portions. The BRC of axial patches to detect ascending or aortic arch lesions and sagittal images for the descending portion had the best performance: 0.954 F1-Score, 98.4% sensitivity, 87% of the subjects correctly classified in their TAC category and an average false positive TAC score per patient of 30. A CNN that combined axial and sagittal patches depending on the candidate aortic location ensured an accurate TAC score prediction. View Full-Text
Keywords: convolutional neural network; artery calcium; thoracic aorta calcification convolutional neural network; artery calcium; thoracic aorta calcification
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MDPI and ACS Style

Guilenea, F.N.; Casciaro, M.E.; Pascaner, A.F.; Soulat, G.; Mousseaux, E.; Craiem, D. Thoracic Aorta Calcium Detection and Quantification Using Convolutional Neural Networks in a Large Cohort of Intermediate-Risk Patients. Tomography 2021, 7, 636-649. https://doi.org/10.3390/tomography7040054

AMA Style

Guilenea FN, Casciaro ME, Pascaner AF, Soulat G, Mousseaux E, Craiem D. Thoracic Aorta Calcium Detection and Quantification Using Convolutional Neural Networks in a Large Cohort of Intermediate-Risk Patients. Tomography. 2021; 7(4):636-649. https://doi.org/10.3390/tomography7040054

Chicago/Turabian Style

Guilenea, Federico N., Mariano E. Casciaro, Ariel F. Pascaner, Gilles Soulat, Elie Mousseaux, and Damian Craiem. 2021. "Thoracic Aorta Calcium Detection and Quantification Using Convolutional Neural Networks in a Large Cohort of Intermediate-Risk Patients" Tomography 7, no. 4: 636-649. https://doi.org/10.3390/tomography7040054

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