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

R. Vivanti*, L. Joskowicz, O. A. Karaaslan, J. Sosna

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


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.

Original languageAmerican English
Pages (from-to)1505-1514
Number of pages10
JournalInternational journal of computer assisted radiology and surgery
Issue number9
StatePublished - 13 Sep 2015

Bibliographical note

Publisher Copyright:
© 2015, CARS.


  • Follow-up CT scans
  • Longitudinal studies
  • Lung tumor
  • Tumor segmentation


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