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Notch signaling is an evolutionarily conserved mechanoresponsive pathway that decides cell fate during developmental process. However, it is not clearly known whether cardiac contraction mediates Notch signaling during the development of the heart. In this study, we have developed a method of segmenting cardiomyocyte nuclei as markers to quantify the contractility of the heart on the basis of an improved Watershed segmentation technique using the Hessian Blob Algorithm. This method provides robust performance for a 4-D (3-D + time) region of interest (ROI) that we are interested in analyzing. Furthermore, we have overcome poor contrast and lack of detail due to refractive index mismatch between different media present in the light propagation path while taking images from LSFM. After image acquisition, we performed pre-processing using a dehazed PSF for deconvolution as the dehazing operation restores the light intensity of corrected PSF. To make the entire process automated, we used a pre-defined feature matching algorithm, whereby the user simply has to crop out a reference ROI, after which the algorithm will automatically segment the ROI from successive frames based on the reference frame. By the aforementioned processes, we have imaged tg(cmlc2:gfp-nuc) zebrafish to visualize and track nuclei of individual cardiomyocytes to quantify the contractility of the heart. To understand the contractility-mediated Notch signaling in cardiac development, tg(tp-1:gfp) has been used with pharmacologic treatments of isoproterenol and metoprolol to increase and decrease cardiac contractility respectively. Our image processing pipeline provided accurate quantification of understanding the heart development between Notch signaling and contractility.
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