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. 2021 Mar 4;11:5241. doi: 10.1038/s41598-021-84812-7

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Light sheet imaging and automated analysis of myocardial infarction. (a) Representative examples of light sheet imaged hearts. 3D overview image is shown on the left. Second panel from left—digital 2D section is taken from 3D reconstructed heart (long axis horizontal plane). Third panel from left demonstrates deep learning-based segmentation of the left ventricle (LV) chamber (in yellow). (b) Diastolic LV chamber volume quantified by deep-learning based analysis. (c) LV apex volume (quantified below plane fitted through the center of deep learning segmented chamber). (d) Light sheet quantification of LV chamber volume in diastole correlates with echocardiography-based LV end-diastolic volume (r = 0.73, p < 0.0001). Data is presented as mean ± s.e.m., n = 8–10. One-way ANOVA with Tukey’s post hoc test. Significance: **p < 0.01, ***p < 0.001. Scale bars: 1 mm. LAD: left anterior descending artery ligation.