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.

Volumetric analysis of MRI data monitoring the treatment of polycystic kidney disease in a mouse model

“…The imaging of mouse polycystic kidneys has been performed with ultrasound [7], X-ray micro-CT [8,9], as well as MRI [2,5,10,11]. MRI allows the repeated and highresolution imaging of the kidneys without the use of ionizing radiation.…”

“…The analysis of CT and MRI images of mouse polycystic kidneys has been performed with extensive manual intervention [2,5], which is cumbersome, costly, and potentially biased due to operator interaction. Some of the preclinical imaging studies for this condition have been accompanied with methods for automated data analysis [8][9][10][11][12]. They often involve manual input during a training phase for several typical data sets [8][9][10][11].…”

Analysis of MR Images of Mice in Preclinical Treatment Monitoring of Polycystic Kidney Disease

“…The imaging of mouse kidneys has been performed in vivo primarily with X-ray micro-CT [2] and MRI [3,4,5]. Typically, the analysis of the images has been performed with extensive manual annotation [4,5].…”

“…There have been attempts to restrict the manual interaction to the initialization of an automated segmentation. In CT images axial contours of kidneys were represented with a deformable model [2]. In MRI the largest coronal contours of kidneys were represented with splines or as the shortest path between annotated points [3].…”

Construction of a 3D Volumetric Probabilistic Model of the Mouse Kidney from MRI

“…Toward this goal, here we present the result of constructing a probabilistic volumetric model which is the first key component of our strategy for the segmentation process. A general drawback of the model constructing methods proposed so far [2,5] is the correspondence problem where the definition of one-to-one mapping across data are needed. To solve this problem, we apply the extended robust point matching algorithm (RPM) [3] which can automatically compute the correspondences.…”