Automatic measurement of the sinus of Valsalva by image analysis. (September 2017)

• Record Type: Journal Article
• Authors: Mairesse, Fabrice; Blanchard, Cédric; Boucher, Arnaud; Sliwa, Tadeusz; Lalande, Alain; Voisin, Yvon
• Is Part Of: Computer methods and programs in biomedicine. Volume 148(2017)
• Page Start: 123
• Page End: 135
• Publication Date: 2017-09
• Subjects: Valsalva sinuses -- Image analysis -- Computed tomography -- Magnetic resonance imaging; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.06.014
• Languages: English
• ISSNs: 0169-2607

Abstract

Abstract : Hihglights: Automatic localization of the Sinus of Valsalva from cine-MRI and CT examinations. Definition of the Aurora transform, a morphological tool, to extract star domains. Automatic extraction of the Sinus of Valsalva using the Aurora transform. Relevant points are detected and measurements are deduced. Sinuses are morphologically classified and edges are fitted by ellipses. Abstract: Background and Objectives: Despite the importance of the morphology of the sinus of Valsalva in the behavior of heart valves and the proper irrigation of coronary arteries, the study of these sinuses from medical imaging is still limited to manual radii measurements. This paper aims to present an automatic method to measure the sinuses of Valsalva on medical images, more specifically on cine MRI and Xray CT. Methods: This paper introduces an enhanced method to automatically localize and extract each sinus of Valsalva edge and its relevant points. Compared to classical active contours, this new image approach enhances the edge extraction of the Sinus of Valsalva. Our process not only allows image segmentation but also a complex study of the considered region including morphological classification, metrological characterization, valve tracking and 2D modeling. Results: The method was successfully used on single or multiplane cine MRI and aortic CT angiographies. The localization is robust and the proposed edge extractor is more efficient than the state-of-the-art methods (average success rate for MRI examinations=84% ± 24%, average success rate for CT examinations=89% ± 11%). Moreover, deduced measurements are close to manual ones. Conclusions: The software produces accurate measurements of the sinuses of Valsalva. The robustness and the reproducibility of results will help for a better understanding of sinus of Valsalva pathologies and constitutes a first step to the design of complex prostheses adapted to each patient.