Özet:

Abstract Deep learning is now the most common technique for segmenting cardiac images, having recently eclipsed all previous approaches. In this research, we demonstrate a complete application of deep learning for cardiac image segmentation. This usage covers a broad variety of popular imaging modalities as well as the primary anatomical components that are significant (ventricles, atria, and arteries). In addition, we provide an overview of open-source software repositories as well as cardiac imaging datasets in order to encourage research that can be reproduced. Finally, we emphasize the limitations and restrictions of current deep learning-based approaches (a lack of labels, domain-general designs, and a lack of interpretability), and we offer future research pathways to address these issues.

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