Automated Estimation of In-plane Nodule Shape in Chest Tomosynthesis Images

Abstract

The purpose of this study was to develop an automated segmentation method for lung nodules in chest tomosynthesis images. A number of simulated nodules of different sizes and shapes were created and inserted in two different locations into clinical chest tomosynthesis projections. The tomosynthesis volumes were then reconstructed using standard cone beam filtered back projection, with 1 mm slice interval. For the in-plane segmentation, the central plane of each nodule was selected. The segmentation method was formulated as an optimization problem where the nodule boundary corresponds to the minimum of the cost function, which is found by dynamic programming. The cost function was composed of terms related to pixel intensities, edge strength, edge direction and a smoothness constraint. The segmentation results were evaluated using an overlap measure (Dice index) of nodule regions and a distance measure (Hausdorff distance) between true and segmented nodule. On clinical images, the nodule segmentation method achieved a mean Dice index of 0.96 ± 0.01, and a mean Hausdorff distance of 0.5 ± 0.2 mm for isolated nodules and for nodules close to other lung structures a mean Dice index of 0.95 ± 0.02 and a mean Hausdorff distance of 0.5 ± 0.2 mm. The method achieved an acceptable accuracy and may be useful for area estimation of lung nodules.