Dedicated breast CT (bCT) is an emerging technology that produces 3D images of the breast, thus allowing radiologists to detect and evaluate breast lesions in 3D. However, assessing potential cancers in the bCT volume can prove time consuming and difficult. Thus, we are developing automated 3D lesion segmentation methods to aid in the interpretation of bCT images. Based on previous studies using a 3D radial-gradient index (RGI) method , we are investigating whether 3D active contour segmentation can be applied in 3D to capture additional details of the lesion margin.
Our data set includes 40 contract-enhanced bCT scans. Based on a radiologist-marked lesion center of each mass, an initial RGI contour is obtained that serves as the input to an active contour segmentation method. In this study, active contour level set segmentation, an iterative segmentation technique, is extended to 3D. Three stopping criteria are compared, based on 1) the change of volume (ΔV/V), 2) the mean value of the increased volume at each iteratin (dμ/dt), and 3) the changing rate of intensity inside and outside the lesion (Δvw).
Lesion segmentation was evaluated by determining the overlap ratio between computer-determined segmentations and manually-drawn lesion outlines. For a given lesion, the overlap ratio was averaged across coronal, sagittal, and axial planes. The average overlap ratios for the three stopping criteria were found to be 0.66 (ΔV/V), 0.68 (dμ/dt), 0.69 (Δvw).