Abdominal multi-organ localization on contrast-enhanced CT based on maximum a posteriori probability and minimum volume overlap
Multi-organ localization is required for many automated abdominal organ analysis tasks. We recently developed an automated organ localization method, which used an MAP framework, and applied it to non-contrast CT images. This method failed to localize smaller organs such as kidneys in some image data because it did not respect the spatial relationship among multiple organs. To address the problem, we extend the framework by modeling the interorgan spatial relations using a minimum volume overlap constraint and incorporating it into the MAP framework. The method was validated on 17 contrast-enhanced CT images and identified correctly the liver, spleen, pancreas and kidneys in all data sets. The new method is more robust to organ pose variations, computationally fast, and improved significantly the localization of kidneys.