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Segmentation-Free Estimation of Aortic Diameters from MRI Using Deep Learning

dc.creatorAguerreberry, Axel
dc.creatorde la Rosa, Ezequiel
dc.creatorLalande, Alain
dc.creatorFernández, Elmer Andrés
dc.date2020-10-04
dc.date.accessioned2022-11-01T19:35:27Z
dc.date.available2022-11-01T19:35:27Z
dc.identifierhttp://pa.bibdigital.ucc.edu.ar/3330/1/DC_Agueberry_de%20la%20Rosa_Lalande.pdf
dc.identifier.urihttp://test.repositoriodigital.com:8080/handle/123456789/43870
dc.descriptionAccurate and reproducible measurements of the aortic diameters are crucial for the diagnosis of cardiovascular diseases and for therapeutic decision making. Currently, these measurements are manually performed by healthcare professionals, being time consuming, highly variable, and suffering from lack of reproducibility. In this work we propose a supervised deep-learning method for the direct estimation of aortic diameters. The approach is devised and tested over 100 magnetic resonance angiography scans without contrast agent. All data was expert-annotated at six aortic locations typically used in clinical practice. Our approach makes use of a 3D+2D convolutional neural network (CNN) that takes as input a 3D scan and outputs the aortic diameter at a given location. In a 5-fold cross-validation comparison against a fully 3D CNN and against a 3D multiresolution CNN, our approach was consistently superior in predicting the aortic diameters. Overall, the 3D+2D CNN achieved a mean absolute error between 2.2–2.4 mm depending on the considered aortic location. These errors are less than 1 mm higher than the inter-observer variability. Thus, suggesting that our method makes predictions almost reaching the expert’s performance. We conclude that the work allows to further explore automatic algorithms for direct estimation of anatomical structures without the necessity of a segmentation step. It also opens possibilities for the automation of cardiovascular measurements in clinical settings.
dc.descriptionFil: Aguerreberry, Axel. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Fisicas y Naturales; Argentina
dc.descriptionFil: de la Rosa, Ezequiel. Technical University of Munich. Department of Computer Science; Alemania
dc.descriptionFil: Lalande, Alain. University of Burgundy. ImViA Laboratory; Francia
dc.descriptionFil: Lalande, Alain. University Hospital of Dijon. Medical Imaging Department; Francia
dc.descriptionFil: Fernández, Elmer. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Fisicas y Naturales; Argentina
dc.descriptionFil: Fernández, Elmer. Universidad Católica de Córdoba. CIDIE-CONICET; Argentina
dc.formatapplication/pdf
dc.languagespa
dc.relationhttp://pa.bibdigital.ucc.edu.ar/3330/
dc.relationhttps://link.springer.com/chapter/10.1007/978-3-030-68107-4_17
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-030-68107-4_17
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.sourceAguerreberry, Axel, de la Rosa, Ezequiel, Lalande, Alain and Fernández, Elmer Andrés (2020) Segmentation-Free Estimation of Aortic Diameters from MRI Using Deep Learning. In: 11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020, Lima, Perú.
dc.subjectR Medicina (General)
dc.titleSegmentation-Free Estimation of Aortic Diameters from MRI Using Deep Learning
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:ar-repo/semantics/artículo


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