Segmentation of the sternum from low-dose chest CT images

Segmentation of the sternum in medical images is of clinical significance as it frequently serves as a stable reference to image registration and segmentation of other organs in the chest region. In this paper we present a fully automated algorithm to segment the sternum in low-dose chest CT images (LDCT). The proposed algorithm first locates an axial seed slice and then segments the sternum cross section on the seed slice by matching a rectangle model. Furthermore, it tracks and segments the complete sternum in the cranial and caudal direction respectively through sequential axial slices starting from the seed slice. The cross section on each axial slice is segmented using score functions that are designed to have local maxima at the boundaries of the sternum. Finally, the sternal angle is localized. The algorithm is designed to be specifically robust with respect to cartilage calcifications and to accommodate the high noise levels encountered with LDCT images. Segmentation of 351 cases from public datasets was evaluated visually with only 1 failing to produce a usable segmentation. 87.2% of the 351 images have good segmentation and 12.5% have acceptable segmentation. The sternal body segmentation and the localization of the sternal angle and the vertical extents of the sternum were also evaluated quantitatively for 25 good cases and 25 acceptable cases. The overall weighted mean DC of 0.897 and weighted mean distance error of 2.88 mm demonstrate that the algorithm achieves encouraging performance in both segmenting the sternal body and localizing the sternal angle.