Automatic ROI identification for fast liver tumor segmentation using graph-cuts

The key challenge in tumor segmentation is to determine their exact location and volume. Difficulties arise because of low intensity boundaries, varying shapes and sizes. Furthermore, tumors can be located everywhere in the liver. Interactive segmentation methods seem to be the most appropriate in terms of reliability and robustness. In this work, we use a graph-cut based method to interactively segment tumors. However, complexity of the underlying graphs is enormous for clinical 3D datasets. We propose a method to identify automatically a region of interest using a coarse resolution image, which is then used to construct a reduced graph for final segmentation in the original image in full resolution. We compared our results to ground truth segmentations done by experts. Our results suggest that accuracy is comparable to other approaches. The average overlap was 80%, the average surface distance 0.73 mm and the average maximum surface distance 5.31 mm.