Overview of the Whole Heart and Heart Chamber Segmentation Methods.

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.

Left atrial appendage segmentation from 3D CCTA images for occluder placement procedure.

BACKGROUND: Percutaneous left atrial appendage (LAA) closure (placement of an occluder to close the appendage) is a novel procedure for stroke prevention in patients suffering from atrial fibrillation. The closure procedure planning requires accurate LAA measurements which can be obtained from computed tomography (CT) images. METHOD: We propose a novel semi-automatic LAA segmentation method from 3D coronary CT angiography (CCTA) images. The method segments the LAA, proposes the location for the occluder placement (a delineation plane between the left atrium and LAA) and calculates measurements needed for closure procedure planning. The method requires only two inputs from the user: a threshold value and a single seed point inside the LAA. Proposed location of the delineation plane can be intuitively corrected if necessary. Measurements are calculated from the segmented LAA according to the final delineation plane. RESULTS: Performance of the proposed method is validated on 17 CCTA images, manually segmented by two medical doctors. We achieve the average dice coefficient overlap of 92.52% and 91.63% against the ground truth segmentations. The average dice coefficient overlap between the two ground truth segmentations is 92.66%. Our proposed LAA orifice localization is evaluated against the desired location of the LAA orifice determined by the expert. The average distance between our proposed location and the desired location is 2.51 mm. CONCLUSION: Segmentation results show high correspondence to the ground truth segmentations. The occluder placement method shows high accuracy which indicates potential in clinical procedure planning.