Consistent segmentation of repeat CT scans for growth assessment in pulmonary nodules

Nodule growth is a key characteristic of malignancy. The measurement of nodule diameter on chest radiographs has been unsatisfactory due to insufficient accuracy and reproducibility. Additionally, the frequent use of high resolution CT scanners has increased the detection rate of very small nodules. On one hand, the small nodules present even greater diagnostic difficulties and, on the other hand, are more frequently benign, resulting in higher rates of unnecessary surgery. In this paper we present a 3-D algorithm to improve the consistency of nodule segmentation on multiple scans. The multi-criterion, multi-scan segmentation algorithm has been developed based on the fact that a typical small pulmonary nodule has distinct difference in density at the boundary and relatively compact shape, and that other tissues in the lung do not change in size over time. Our preliminary results with in-vivo nodules have shown the potential of applying this practical 3-D segmentation algorithm to clinical settings.