Automatic Liver Segmentation Method Featuring a Novel Filter for Multiphase Multidetector-Row Helical Computed Tomography

Abstract

To introduce an automatic liver segmentation method that includes a novel filter for multiphase multidetector-row helical computed tomography. We acquired 3-phase multidetector-row computed tomographic scans that included unenhanced, arterial, and portal phases. The liver was segmented using our novel adaptive linear prediction filter designed to reduce the difference between filter input and output values in the liver region and to increase these values outside the liver region. The segmentation algorithm produced a mean dice similarity coefficient (DSC) value of 91.4%. The application of our adaptive linear prediction filter was effective in automatically extracting liver regions.