Abstract

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% (std = 5.6), a significant improvement from the 30% (std = 13.3) result of stand-alone level-set segmentation.

Conclusion The key advantage of our method is that it autoR. Vivanti (B) · L. Joskowicz The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Givat Ram Campus, 91904 Jerusalem, Israel e-mail: refael.vivanti@mail.huji.ac.il O. A. Karaaslan · J. Sosna Department of Radiology, Hadassah Hebrew University Medical Center, Jerusalem, Israel matically 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.

Automatic lung tumor segmentation with leaks removal in follow-up CT studies
Refael Vivanti
Refael Vivanti
School of Engineering and Computer Science, The Hebrew University of Jerusalem, Israel
Leo Joskowicz
Leo Joskowicz
School of Computer Science and Engineering, Computer-Aided Surgery and Medical Image Processing Laboratory, The Hebrew University of Jerusalem, Givat Ram Campus, Jerusalem 91904, Israel
Tel : +972-2-658-6299, Fax : +972-2-658-6459

The Edmond and Lily Safra Center for Brain Research (ELSC)
Onur A. Karaaslan
Onur A. Karaaslan
Department of Radiology Hadassah, Hebrew University Medical Center
Jacob Sosna
Jacob Sosna
Department of Radiology, School of Medicine, Hadassah Hebrew University Medical Center, Jerusalem, Israel
Telephone: +972-2-6777111