Abstract
ABSTRACTAlthough the zebrafish embryo is a powerful animal model of human heart failure, the methods routinely employed to monitor cardiac function produce rough approximations that are susceptible to bias and inaccuracies. We developed and validated a deep learning-based image-analysis platform for automated extraction of volumetric parameters of cardiac function from dynamic light-sheet fluorescence microscopy (LSFM) images of embryonic zebrafish hearts. This platform, the Cardiac Functional Imaging Network (CFIN), automatically delivers rapid and accurate assessments of cardiac performance with greater sensitivity than current approaches.This article has an associated First Person interview with the first author of the paper.
Highlights
Despite the advantages of utilizing zebrafish embryos for modeling human heart failure (Bakkers, 2011; Becker et al, 2012; Chen et al, 1996; Liu et al, 2014; Shih et al, 2015; Stainier et al, 1996), the metrics employed to monitor cardiac function remain limited and unrefined
To efficiently process the vast amount of data generated by light-sheet fluorescence microscopy (LSFM), we utilized a deep learning convolutional neural network to automatically identify and annotate the atrial and/or ventricular boundaries in each frame of 2D LSFM movies
This manually annotated dataset amounted to 13% of the total images collected for each heart (≈2400 images total from four hearts)
Summary
Despite the advantages of utilizing zebrafish embryos for modeling human heart failure (Bakkers, 2011; Becker et al, 2012; Chen et al, 1996; Liu et al, 2014; Shih et al, 2015; Stainier et al, 1996), the metrics employed to monitor cardiac function remain limited and unrefined. The most common metric, ‘fractional shortening’ (FS), is a proxy for volumetric output calculated from the cross-sectional diameters of the ventricle during diastole (chamber relaxation) and systole (chamber contraction), which are measured from twodimensional (2D) images (Hoage et al, 2012; Yalcin et al, 2017). Despite its ease of use, FS suffers from inconsistencies caused by variable imaging angles, and subjective identification of systole, diastole and cross-sectional diameter. The accuracy of this method relies upon the assumption that chamber diameter is uniformly correlated with chamber volume, which is not always the case. Regional abnormalities in wall movement can over- or underestimate functional deficits depending on where the abnormalities reside relative to the diameters being measured.
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