3D modeling of detected pleural thickenings through thin plate spline interpolation

Abstract

Pleuramesothelioma is a malignant tumor. A computer-assisted diagnosis system shall provide physicians with the assessment of the detected pleural thickening from the 3D CT data. This paper describes a new, improved method to assess the size of detected thickening using both the 2D and 3D thin plate spline. First, a coordinate transformation was applied. Next, set of landmarks was selected as input for the thin plate spline. The final step is the numerical area integration. The results show that the calculated area was more accurate than the former pixel counting technique, and promise further automatic development. KEY WORDSThin plate spline interpolation, 3D modeling, pleural thickenings, pleural mesothelioma, 3D CT data

Automatic 3D modeling of pleural thickening through thin plate spline interpolation

“…2.1 Detection of Pleura I Thickening The pleural thickenings are detected from pleural contours out of the 3D CT data using a high precision segmentation technique, followed by comparing the pleural contour with its convex hull as well as medical criteria according to tissue properties of CT data [3]. The 3D connected neighborhood labeling defines the group of each pleural thickening by connecting the belonging 2D findings of pleural thickening on each layer together.…”

“…To create this model, the pleural contour is refined by a more sophisticated interpolation replacing the linear interpolation originating from the convex hull calculation at those locations where pleural thickenings were detected. The interpolation is based on a thin plate spline model [4][5] [6], which is reported to be suitable for the deformation of biological tissues [3] [7].…”

“…1), the aim of this work is to develop a highly accurate automatic volume calculation using the chain code concavity analysis followed by the 3D thin plate spline interpolation [3]. The steps of our method are as following.…”

“…By translation and rotation of the coordinate system with automatically determined orthogonal matrix by a set of landmark points contained in each group of pleural thickening(Fig. 2), a new coordinate system leads to a better curve fitting for both surfaces and volume determination[3]…”

3D Surface-based Detection of Pleural Thickenings

“…For exact measurements of volume and thickness, the thickenings are classified as pleural plaques or as diffuse pleural thickenings. A thin plate spline interpolation [4] is applied to reconstruct the correct thickening ex-pansion, depending on the classification. To perform the follow-up assessment a rigid registration [5] between the image data of two different points in time is performed.…”