Overview of the Whole Heart and Heart Chamber Segmentation Methods.

The methods described are classified based on the used segmentation approach into (1) edge-based segmentation methods, (2) model-fitting segmentation methods and (3) machine and deep learning segmentation methods and are further split based on the targeted cardiac structure. Edge-based methods are mostly developed as semi-automatic and allow end-user interaction, which provides physicians with extra control over the final segmentation. Model-fitting methods are very robust and resistant to the high variability in image contrast and overall image quality. Nevertheless, they are often time-consuming and require appropriate models with prior knowledge. While the emerging deep learning segmentation approaches provide unprecedented performance in some specific scenarios and under the appropriate training, their performance highly depends on the data quality and the amount and the accuracy of provided annotations.