Abstract
An excessive deposition of collagen matrix in the myocardium has been clearly identified as an indication of the progression towards heart failure. Nevertheless, few studies have been performed for its quantification and most of them use 2D histological images, thus losing valuable encoded 3D information. In this study, several biopsies of areas of the left ventricle from age-matched spontaneously hypertensive rats and Wistar Kyoto rats were imaged using synchrotron radiation-based X-ray phase contrast imaging. Then, an optimized deep neural network was used for automatic image segmentation in order to assess collagen fraction differences between models as well as its age dependency. The results show a general increase in the collagen percentage in the hypertensive model and for older rats. Such tendency is comparable with the reports found in the literature. Therefore, this proof of concept shows that synchrotron imaging in combination with deep neural networks is a powerful tool for the investigation and quantification of cardiac microstructures.
H. Dejea, C. Tanner, E. Konukoglu and A. Bonnin—These authors contributed equally to this work.
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Acknowledgments
We acknowledge Monica Zamora, Fatima Crispi and Eduard Guasch for animal handling, and Xavier Buyse Sánchez for his support in the labelling task. In addition, we acknowledge the Paul Scherrer Institut, Villigen, Switzerland for provision of synchrotron radiation beamtime at the beamline TOMCAT (X02DA) of the Swiss Light Source. This project was supported by the grant #2017-303 of the Strategic Focal Area “Personalized Health and Related Technologies (PHRT)”, and the grant C17-04 of the Strategic Focal Area “Swiss Data Science Center (SDSC)” of the ETH Domain.
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Dejea, H. et al. (2019). Synchrotron X-Ray Phase Contrast Imaging and Deep Neural Networks for Cardiac Collagen Quantification in Hypertensive Rat Model. In: Coudière, Y., Ozenne, V., Vigmond, E., Zemzemi, N. (eds) Functional Imaging and Modeling of the Heart. FIMH 2019. Lecture Notes in Computer Science(), vol 11504. Springer, Cham. https://doi.org/10.1007/978-3-030-21949-9_21
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