Abstract
We present a novel automatic algorithm for lung tumors segmentation in follow-up CT studies. The inputs are a baseline CT scan and a delineation of the tumors in it; the output is the tumor delineations in the follow-up scan. The algorithm consists of four steps: (1) deformable registration of the baseline and follow-up scans; (2) segmentation of the tumors in the follow-up scan; (3) geometry-based segmentation leaks correction; and (4) tumor boundary regularization. The key advantage of our method is that it automatically builds a patient-specific prior that increases segmentation accuracy and robustness and reduces observer variability. Our experimental results on 80 pairs of CT scans from 40 patients with ground-truth segmentations by a radiologist yield an average overlap error of 14.5 % (std = 5.6), a significant improvement from the 30 % (std = 13.3) result of stand-alone fast marching segmentation.
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Vivanti, R., Karaaslan, O.A., Joskowicz, L., Sosna, J. (2014). Automatic Lung Tumor Segmentation with Leaks Removal in Follow-up CT Studies. In: Linguraru, M., et al. Clinical Image-Based Procedures. Translational Research in Medical Imaging. CLIP 2014. Lecture Notes in Computer Science(), vol 8680. Springer, Cham. https://doi.org/10.1007/978-3-319-13909-8_12
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DOI: https://doi.org/10.1007/978-3-319-13909-8_12
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