Design of a benchmark dataset, similarity metrics, and tools for liver segmentation

Reliable segmentation of the liver has been acknowledged as a significant step in several computational and diagnostic processes. While several methods have been designed for liver segmentation, comparative analysis of reported methods is limited by the unavailability of annotated datasets of the abdominal area. Currently available generic data-sets constitute a small sample set, and most academic work utilizes closed datasets. We have collected a dataset containing abdominal CT scans of 50 patients, with coordinates for the liver boundary. The dataset will be publicly distributed free of cost with software to provide similarity metrics, and a liver segmentation technique that uses Markov Random Fields and Active Contours. In this paper we discuss our data collection methodology, implementation of similarity metrics, and the liver segmentation algorithm.