Subject-Specific prior shape knowledge in feature-oriented probability maps for fully automatized liver segmentation in MR volume data

Abstract

Liver segmentation and volumetry in native MR-volume data is an important topic in epidemiological research. Manual liver segmentation is extremely time-consuming and often infeasible requiring automatized methods. Automatic liver segmentation is challenging because of the large variability in liver shape and appearance and the low contrast to neighboring organs. We present a fully automatized liver segmentation framework that uses a sequence of modules based on individualized model knowledge on liver appearance and shape. Liver probability maps are computed that incorporate organ-specific features like MR-intensity distributions, inner-organ MR-differences and liver positions. Probability map generation differentiates automatically between fatty and non-fatty livers. Moreover, we improve an existing technique for prior shape level set segmentation to delineate the liver in tissue-specific liver probability maps and to recognize cystic hepatic tissue. Dice coefficients of 0.937 and low volumetric errors on 35 test data sets confirm the robust segmentation quality of the framework.