Brain Tumor Target Volume Segmentation: Local Region Based Approach

Abstract

In this paper, we comprehensively evaluated clinical application of local robust-region based algorithms to delineate the brain target volumes in radiation therapy treatment planning. Localized region based algorithms can optimize processing time of manual target tumor delineation and have perfect correlation with manual delineation defined by oncologist due to high deformability. Accordingly, they can receive much attention in radiation therapy treatment planning. Firstly, clinical target volumes (CTVs) of 135 slices in 18 patients were manually defined by two oncologists and the average of these contours considered as references in order to compare with semi-automatic results from different four algorithms. Then, four localized region based algorithms named Localizing Region Based Active Contour (LRBAC), Local Chan-Vese Model (LCV), Local Region Chan-Vese Model (LRCV) and Local Gaussian Distribution Fitting (LGDF) were applied to outline CTVs. Finally, comparisons between semiautomatic results and baselines were done according to three different metric criteria: Dice coefficient, Hausdorff distance, and mean absolute distance. Manual delineation processing times of target tumors were also performed. Our result showed that LCV has advantage over other algorithms in terms of the processing time and afterward LRCV is the second fastest method. LRBAC was the second slowest technique; however, we found that processing speed in LRBAC can be almost doubled by replacing the time-consuming re-initialization process with energy penalizing term. Accordingly, due to high accuracy performance of LRBAC algorithm, it can be concluded that the modified version of LRBAC has the best performance in brain target volumes in radiation therapy treatment planning among other localized algorithms in terms of speed and accuracy.