Heartbeat measurement is important in assesssing cardiac function because variations in heart rhythm can be the cause as well as an effect of hidden pathological heart conditions. Zebrafish (Danio rerio) has emerged as one of the most useful model organisms for cardiac research. Indeed, the zebrafish heart is easily accessible for optical analyses without conducting invasive procedures and shows anatomical similarity to the human heart. In this study, we present a non-invasive, simple, cost-effective process to quantify the heartbeat in embryonic zebrafish. To achieve reproducibility, high throughput and flexibility (i.e., adaptability to any existing confocal microscope system and with a user-friendly interface that can be easily used by researchers), we implemented this method within a software program. We show here that this platform, called ZebraBeat, can successfully detect heart rate variations in embryonic zebrafish at various developmental stages, and it can record cardiac rate fluctuations induced by factors such as temperature and genetic- and chemical-induced alterations. Applications of this methodology may include the screening of chemical libraries affecting heart rhythm and the identification of heart rhythm variations in mutants from large-scale forward genetic screens.

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Imaging of the left ventricle using cine short-axis MRI sequences, considered as an important tool that used for evaluating cardiac function by calculating different cardiac parameters. The manual segmentation of the left ventricle in all image sequences takes a lot of time, and therefore the automatic segmentation of the left ventricle is main step in cardiac function evaluation. In this paper, we proposed an automatic method for segmenting the left ventricle in cardiac MRI images. We applied pixel classification method by using number of features and KNN classifier for segmenting the left ventricle Cavity, and from its output we can get the endocardial contour. Then, we transformed image pixels from Cartesian to polar coordinates for segmenting the epicardial contour. This method was tested on large number of images, and we achieved good results reached to 95.61% sensitivity, and 98.9% specificity for endocardium segmentation, and 93.32% sensitivity, and 98.49% specificity for epicardium segmentation. The results of the proposed method show the availability for fast and reliable segmentation of the left ventricle.

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