Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors.

ABSTRACT: This paper addresses the automated segmentation of multiple organs in upper abdominal computed tomography (CT) data. The aim of our study is to develop methods to effectively construct the conditional priors and use their prediction power for more accurate segmentation as well as easy adaptation to various imaging conditions in CT images, as observed in clinical practice. We propose a general framework of multi-organ segmentation which effectively incorporates interrelations among multiple organs and easily adapts to various imaging conditions without the need for supervised intensity information. The features of the framework are as follows: (1) A method for modeling conditional shape and location (shape-location) priors, which we call prediction-based priors, is developed to derive accur

SUBMITTER: Okada T 

PROVIDER: S-EPMC4679509 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature