A new deformable model for analysis of X-ray CT images in preclinical studies of mice for polycystic kidney disease.

Abstract

This paper describes the application of a new probabilistic shape and appearance model (PSAM) algorithm to the task of detecting polycystic kidney disease (PKD) in X-ray computed tomography images of laboratory mice. The genetically engineered PKD mouse is a valuable animal model that can be used to develop new treatments for kidney-related problems in humans. PSAM is a statistical-based deformable model that improves upon existing point distribution models for boundary-based object segmentation. This new deformable model algorithm finds the optimal boundary position using an objective function that has several unique characteristics. Most importantly, the objective function includes both global shape and local gray-level characteristics, so optimization occurs with respect to both pieces of information simultaneously. PSAM is employed to segment the mouse kidneys and then texture measurements are applied within kidney boundaries to detect PKD. The challenges associated with the segmentation non-rigid organs along with the availability of a priori information led to the choice of a trainable, deformable model for this application. In 103 kidney images that were analyzed as part of a preclinical animal study, the mouse kidneys and spine were segmented with an average error of 2.4 pixels per boundary point. In all 103 cases, the kidneys were successfully segmented at a level where PKD could be detected using mean-of-local-variance texture measurements within the located boundary.