A conditional statistical shape model with integrated error estimation of the conditions; application to liver segmentation in non-contrast CT images.

This paper presents a novel conditional statistical shape model in which the condition can be relaxed instead of being treated as a hard constraint. The major contribution of this paper is the integration of an error model that estimates the reliability of the observed conditional features and subsequently relaxes the conditional statistical shape model accordingly. A three-step pipeline consisting of (1) conditional feature extraction from a maximum a posteriori estimation, (2) shape prior estimation through the novel level set based conditional statistical shape model with integrated error model and (3) subsequent graph cuts segmentation based on the estimated shape prior is applied to automatic liver segmentation from non-contrast abdominal CT volumes.

Statistical shape model of a liver for autopsy imaging.

Purpose: Modeling the postmortem liver for autopsy imaging is a challenging problem owing to the variation in organ deformation found in cadavers and limited availability of postmortem liver CT scans. An algorithm was developed to construct a statistical shape model (SSM) for the adult postmortem liver in autopsy imaging.

Methods: First, we investigated the relationship between SSMs obtained from in vivo liver CT scans and those from postmortem cases.