title: Automatic lung tumor segmentation with leaks removal in follow-up CT studies.
abstract: OBJECTIVE
  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 % ([Formula: see text]), a significant improvement from the 30 % ([Formula: see text]) result of stand-alone level-set segmentation.
  
  
  CONCLUSIONS
  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.
authors.affiliation: The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Givat Ram Campus, 91904, Jerusalem, Israel, refael.vivanti@mail.huji.ac.il.
authors.forename: R
authors.initials: R
authors.lastname: Vivanti
authors.forename: L
authors.initials: L
authors.lastname: Joskowicz
authors.forename: O A
authors.initials: OA
authors.lastname: Karaaslan
authors.forename: J
authors.initials: J
authors.lastname: Sosna
mesh_terms: D000465:Algorithms; D001331:Automation; D018450:Disease Progression; D005500:Follow-Up Studies; D006801:Humans; D008137:Longitudinal Studies; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D015233:Models, Statistical; D016011:Normal Distribution; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*