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. 2008 Jun;35(6):2321-30.
doi: 10.1118/1.2900111.

Semiautomated three-dimensional segmentation software to quantify carpal bone volume changes on wrist CT scans for arthritis assessment

J Duryea  1 , M Magalnick, S Alli, L Yao, M Wilson, R Goldbach-Mansky
Affiliations

Semiautomated three-dimensional segmentation software to quantify carpal bone volume changes on wrist CT scans for arthritis assessment

J Duryea et al. Med Phys. 2008 Jun.

Abstract

Rapid progression of joint destruction is an indication of poor prognosis in patients with rheumatoid arthritis. Computed tomography (CT) has the potential to serve as a gold standard for joint imaging since it provides high resolution three-dimensional (3D) images of bone structure. The authors have developed a method to quantify erosion volume changes on wrist CT scans. In this article they present a description and validation of the methodology using multiple scans of a hand phantom and five human subjects. An anthropomorphic hand phantom was imaged with a clinical CT scanner at three different orientations separated by a 30-deg angle. A reader used the semiautomated software tool to segment the individual carpal bones of each CT scan. Reproducibility was measured as the root-mean-square standard deviation (RMMSD) and coefficient of variation (CoV) between multiple measurements of the carpal volumes. Longitudinal erosion progression was studied by inserting simulated erosions in a paired second scan. The change in simulated erosion size was calculated by performing 3D image registration and measuring the volume difference between scans in a region adjacent to the simulated erosion. The RMSSD for the total carpal volumes was 21.0 mm3 (CoV = 1.3%) for the phantom, and 44.1 mm3 (CoV = 3.0%) for the in vivo subjects. Using 3D registration and local volume difference calculations, the RMMSD was 1.0-3.0 mm3 The reader time was approximately 5 min per carpal bone. There was excellent agreement between the measured and simulated erosion volumes. The effect of a poorly measured volume for a single erosion is mitigated by the large number of subjects that would comprise a clinical study and that there will be many erosions measured per patient. CT promises to be a quantifiable tool to measure erosion volumes and may serve as a gold standard that can be used in the validation of other modalities such as magnetic resonance imaging.

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Figures

Figure 1
Figure 1
Wrist radiograph demonstrating the projected overlapping bone structure. The figure also identifies seven of the eight carpal bones. The pisiform (not identified on the figure) is a small round-shaped bone obscured on the image by the triquetral.
Figure 2
Figure 2
Segmented slice of a hamate bone that illustrates the hybrid reader-software edge detection technique. In (a) the edge tracking software has detected a bone margin within the true boundaries of the carpal. The arrow indicated the true bone margin. In (b) the reader manually edits the contour (indicated by arrow). In (c) the automated active contour routine refines the manual edits.
Figure 3
Figure 3
Example of semiautomated segmentation for the first slice of a carpal. In (a) the reader entered a seed point and (b) initiated an edge tracking routine. In (c) the reader made a small edit to guide the edge tracking back to the correct bone margin. In (d) the software closed the contour and performed an active contour refinement step described below. (e) shows the subsequent slice with the segmentation from the previous slice overlaid. (f) is the final segmentation after the active contour step is applied.
Figure 4
Figure 4
Illustration of the active contour model algorithm.
Figure 5
Figure 5
Example of gradient threshold refinement step on a scaphoid carpal. In (a) the carpal margins are sufficiently oblique that the adjacent segmentation fails to provide a useful starting point and the segmentation fails. In (b) the gradient threshold refinement step is applied. In (c) the standard active contour algorithm is employed to refine the contour and produce the final result.
Figure 6
Figure 6
Illustration of refinement procedure. (a) and (b) show the biniarized Bbaseline and Bfollow-up with the simulated erosion. In (c) the images are spatially registered. (d), (e), and (f) show Bloss (dark voxels), Bgain (light voxels) and both overlaid. In (g) the results of the binary erosion step is shown.
Figure 7
Figure 7
(a) Graph of VEr vs VGS for Δθ=15 deg. VGS is the gold standard erosion volume that is known exactly from the algorithm that produced the simulated erosion in the follow-up erosion VEr is the erosion calculated by the method after 3D registration of Bbaseline and Bfollow-up. (b) Graph of VEr vs VGS for Δθ=30 deg.
Figure 8
Figure 8
Graph of the RMSSD vs Nbin.
Figure 9
Figure 9
Graph of the RMSSD vs REr (Nbin=13).
Figure 10
Figure 10
(a) Graph of the RMSSD vs REr for erosions created with a radius less than 0.6 mm (Nbin=13). (b) Graph of the RMSSD vs REr for erosions created with a radius less than 0.8 mm (Nbin=13).
Figure 11
Figure 11
3D surface plot of the RMSSD vs REr and Nbin.
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