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 substructures 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 卤 0.023 and a mean surface distance error of 1.17 卤 1.39 mm for the automatic whole heart segmentation of seven substructures.
(8.0+极速模式)