Overview of the Whole Heart and Heart Chamber Segmentation Methods

• Published in: Cardiovascular engineering and technology 2020-12, Vol.11 (6), p.725-747
• Main Authors: Habijan, Marija, Babin, Danilo, Galić, Irena, Leventić, Hrvoje, Romić, Krešimir, Velicki, Lazar, Pižurica, Aleksandra
• Format: Article
• Language: English
• Subjects: Biomedical Engineering and Bioengineering; Biomedicine; Cardiology; Engineering; Original Article
• ISSN: 1869-408X; 1869-4098
• DOI: 10.1007/s13239-020-00494-8

Background Preservation and improvement of heart and vessel health is the primary motivation behind cardiovascular disease (CVD) research. Development of advanced imaging techniques can improve our understanding of disease physiology and serve as a monitor for disease progression. Various image processing approaches have been proposed to extract parameters of cardiac shape and function from different cardiac imaging modalities with an overall intention of providing full cardiac analysis. Due to differences in image modalities, the selection of an appropriate segmentation algorithm may be a challenging task. Purpose This paper presents a comprehensive and critical overview of research on the whole heart, bi-ventricles and left atrium segmentation methods from computed tomography (CT), magnetic resonance (MRI) and echocardiography (echo) imaging. The paper aims to: (1) summarize the considerable challenges of cardiac image segmentation, (2) provide the comparison of the segmentation methods, (3) classify significant contributions in the field and (4) critically review approaches in terms of their performance and accuracy. Conclusion 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.