Cardiac computed tomography (CT) is widely used in clinics for diagnosing heart diseases and assessing functionality of the heart. It is therefore desirable to achieve fully automatic whole heart segmentation for the
clinical applications, since manual work can be labor-intensive and subject to bias. However, automating this
segmentation is challenging due to the large shape variability of the heart and the poor contrast between sub-
structures such as those in the right ventricle and right atrium region in CT angiography images. In this work,
we develop a fully automatic whole heart segmentation framework for CT volumes. This framework is based on
image registration and atlas propagation techniques. Also, we investigate and compare the segmentation performance using single and multiple atlas propagation and segmentation strategies. In multiple atlas segmentation,
a ranking-and-selection scheme is used to identify the best atlas(es) from an atlas pool for an unseen image. The
segmentation methods are evaluated using fifteen clinical data. The results show that the proposed multiple
atlas segmentation method can achieve a mean Dice score of 0:889±0:023 and a mean surface distance error of
1:17±1:39 mm for the automatic whole heart segmentation of seven substructures.
Cardiac functional indices, such as ejection fraction and regional wall motion/ thickening, are commonly used for
assessing the contractility and functionality of the heart in clinical practice. An important step for computer-aided
determination of functional indices is the automated segmentation of the heart from computed tomography angiography (CTA) and the partitioning of the left ventricle into 16 segments. We develop a fully automatic scheme which not only segments the whole heart from cardiac CTA images, but also partitions the left ventricle, including the blood pool and myocardium, into 16 segments of bull’s eye plot. The segmentation is based on image registration and atlas propagation techniques, whereas the bull’s eye plot is first obtained through atlas propagation and then further improved to correct inconsistency across different subjects, uneven size for each segment and “zig-zag” edges between them. In this preliminary study, a cohort of ten clinical CTA data was employed to compute and evaluate the regional functional indices as well as the global indices.