Random walk-based automated segmentation for the prognosis of malignant pleural mesothelioma

In this paper we apply the random walk-based segmentation method to mesothelioma CT image datasets, aiming to establish an automatic segmentation routine that can provide volumetric assessments for monitoring progression of the disease and its treatments. We have validated the applicability of this method to our image data through a series of experimental trials, and demonstrated the superior performance and benefits of random walk compared to other segmentation algorithms such as level sets.