Automatic lung tumor segmentation with leaks removal in follow-up CT studies

Purpose: In modern oncology, disease progression and response to treatment are routinely evaluated with a series of volumetric scans. The number of tumors and their volume (mass) over time provides a quantitative measure for the evaluation. Thus, many of the scans are follow-up scans. We present a new, fully automatic algorithm for lung tumors segmentation in follow-up CT studies that takes advantage of the baseline delineation. Methods: The inputs are a baseline CT scan and a delineation of the tumors in it and a follow-up scan; the output is the tumor delineations in the follow-up CT scan; the output is the tumor delineations in the follow-up CT scan. The algorithm consists of four steps: (1) deformable registration of the baseline scan and tumor’s delineations to the follow-up CT scan; (2) segmentation of these tumors in the follow-up CT scan with the baseline CT and the tumor’s delineations as priors; (3) detection and correction of follow-up tumors segmentation leaks based on the geometry of both the foreground and the background; and (4) tumor boundary regularization to account for the partial volume effects. Results: Our experimental results on 80 pairs of CT scans from 40 patients with ground-truth segmentations by a radiologist yield an average DICE overlap error of 14.5 % ($$\hbox {std}=5.6$$std=5.6), a significant improvement from the 30 % ($$\hbox {std}=13.3$$std=13.3) result of stand-alone level-set segmentation. Conclusion: The key advantage of our method is that it automatically builds a patient-specific prior to the tumor. Using this prior in the segmentation process, we developed an algorithm that increases segmentation accuracy and robustness and reduces observer variability.