Automatic Whole Heart Segmentation in CT Images Based on Multi-atlas Image Registration

Abstract

Whole heart segmentation in CT images is a significant prerequisite for clinical diagnosis or treatment. In this work, we present a three-step multi-atlas-based method for obtaining a segmentation of the whole heart. In the first step, the region of the heart was detected by aligning the down-sampled patient CT with the low-resolution atlas images. The detected region of heart was used to crop the original patient image. In the second step, the registration between high-resolution atlas images and cropped original patient images was performed to obtain the precise segmentation of the heart. In the third step, the registration was performed again by minimizing the dissimilarity within the heart region. Finally, the labels of four cardiac chambers, aorta and pulmonary artery were generated according to the similarity between the deformed atlas images and the patient image. A leave-one-out experiment has been performed on the 20 training datasets of MM-WHS 2017 challenge. The average Dice coefficient between our segmentation results and the manual segmentation results is 0.9051. The mean and standard deviation of Dice coefficients of each structure (i.e. LV, RV, LA, RA, Myo, Ao, PA) are 0.9601 ± 0.0324, 0.9344 ± 0.0418, 0.9594 ± 0.0316, 0.8836 ± 0.0826, 0.8724 ± 0.0707, 0.9295 ± 0.0883, 0.7966 ± 0.1149 respectively.