A LOW-INTERACTION AUTOMATIC 3D LIVER SEGMENTATION METHOD FOR SELECTIVE INTERNAL RADIATION THERAPY

Three-Dimensional (3-D) imaging is vital in computer-assisted surgical planning including minimal invasive surgery, targeted drug delivery, and tumor resection. Selective Internal Radiation Therapy (SIRT) is a liver directed radiation therapy for the treatment of liver cancer. Accurate calculation of anatomical liver and tumor volumes are essential for the determination of the tumor to normal liver ratio and for the calculation of the dose of Y-90 microspheres that will result in high concentration of the radiation in the tumor region as compared to nearby healthy tissue. This study introduces a parallel-aware semi-automatic liver segmentation approach for accurate calculation of the anatomic liver volumes. The novelty of this algorithm is in using a single slice in the initialization process to segment the entire liver in 3D. The algorithm relies on a localized contouring algorithm in combination with a modified k-means approach as the main preprocessing steps. A singular contribution of the algorithm is in devising initialization masks for the contouring algorithm that allowed for minimal human intervention. Intensity based region growing along with a new volume of interest (VOI) based correction technique are combined to achieve the single slice initialization. The performance of the algorithm is assessed experimentally using 20 liver Computed Tomography (CT) scans. Results show an average accuracy of 96.76% for volumetric calculation and an average Dice coefficient of 0.92. Results of the parallel computing process, using a single workstation, showed a 78% gain. Also, statistical analysis carried out showed that user initialization has no significant effect on the results.