Principal curve based semi-automatic segmentation of organs in 3D-CT

Radiation therapy plays an important and effective role in the treatment of cancer. A main goal in radiation therapy is to deliver high radiation doses to the perceived tumors while minimizing radiation to surrounding normal tissues. Manual delineation of tumors and organs-at-risk (OARs) on three-dimensional computed tomography (3D-CT) is both a time-consuming and labor intensive task, and there maybe variability between manual delineations by different radiation oncologists. In this paper, we present a semi-supervised method to segment the contours of organs represented by piecewise linear segments connected with a small number of points given the user's input in one or more slices as an approximate initialization. This method detects ridge samples from the kernel interpolation of the edge map and approximates the shape of organs using piecewise linear segments among those sample points based on the principal curve score. Results are provided in two 3D-CT scans. Evaluation of the efficacy of our semiautomatic segmentation method is based on the overlapping ratio between the manually delineated contours and the semiautomatic segmented contours represented by a small number of points. The preserved points can be as low as 10 percent of the initial manual points, and the Dice Coefficients are approximately 0.93 for lung segmentation.