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Recent Trends of Left and Right Ventricle Segmentation in Cardiac MRI Using Deep Learning

Clinical indications of heart disease are shown from left ventricle (LV) or right ventricle (RV) volume measurements of cardiac MRI images. LV and RV segmentation of cardiac MRI images can detect and measure image volume. Public dataset MICCAI, ACDC, Kaggle, and SCD provide data on MRI images of cardiac that have been widely used by researchers. The deep learning method approach can optimally solve problems in analyzing heart disease from cardiac MRI images. The aim of this paper is to determine the availability of public datasets that are appropriate for the research objectives. It can support the optimization of the segmentation method for LV and RV images of cardiac as the contribution of this paper. The results of the study are that the public dataset (MICCAI, ACDC, Kaggle, and SCD) provides sufficient data for the identification, classification, and measurement of LV and RV volumes. Furthermore, a deep learning approach with convolutional neural networks can detect and classify heart diseases with high accuracy.