Interactive Prostate Shape Reconstruction from 3D TRUS Images - PubMed (original) (raw)

Interactive Prostate Shape Reconstruction from 3D TRUS Images

Tomotake Furuhata et al. J Comput Des Eng. 2014 Oct.

Abstract

This paper presents a two-step, semi-automated method for reconstructing a three-dimensional (3D) shape of the prostate from a 3D transrectal ultrasound (TRUS) image. While the method has been developed for prostate ultrasound imaging, it can potentially be applicable to any other organ of the body and other imaging modalities. The proposed method takes as input a 3D TRUS image and generates a watertight 3D surface model of the prostate. In the first step, the system lets the user visualize and navigate through the input volumetric image by displaying cross sectional views oriented in arbitrary directions. The user then draws partial/full contours on selected cross sectional views. In the second step, the method automatically generates a watertight 3D surface of the prostate by fitting a deformable spherical template to the set of user-specified contours. Since the method allows the user to select the best cross-sectional directions and draw only clearly recognizable partial or full contours, the user can avoid time-consuming and inaccurate guesswork on where prostate contours are located. By avoiding the usage of noisy, incomprehensible portions of the TRUS image, the proposed method yields more accurate prostate shapes than conventional methods that demand complete cross-sectional contours selected manually, or automatically using an image processing tool. Our experiments confirmed that a 3D watertight surface of the prostate can be generated within five minutes even from a volumetric image with a high level of speckles and shadow noises.

Keywords: Image Processing; Prostate; Shape Reconstruction; TRUS; Ultrasound.

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Figures

Figure 1

Figure 1

Typical TRUS images – part of an image is often not comprehensible due to speckle and shadow noises.

Figure 2

Figure 2

A prostate 3D geometry reconstructed from a set of manually selected full contours is not smooth and may not be watertight.

Figure 3

Figure 3

Proposed two-step approach to generating the 3D surface model of a prostate from a 3D TRUS image

Figure 4

Figure 4

Developed GUI for Step 1 cross-sectional image navigation, adjustment of intensity and contrast, and specification of partial/full contours.

Figure 5

Figure 5

Template polygonal models of the prostate

Figure 6

Figure 6

Error convergence with tri-mesh and quad-mesh templates. A quad-mesh template yields 30–40% smaller error compared with a tri-mesh template having a similar number of mesh elements.

Figure 7

Figure 7

3D Error comparison with various smoothing schemes.

Figure 8

Figure 8

V-Spring uses a spring force that acts in the normal direction to minimize curvature variation.

Figure 9

Figure 9

V-Spring uses a spring force that acts in the tangential direction to optimize vertex distribution.

Figure 10

Figure 10

V-Spring uses spring forces for fitting, or constraining, the final surface to approximate the set of partial contours.

Figure 11

Figure 11

Manually specified contours deviate approximately 1 mm on average with a maximum deviation of 2.5–3.0 mm, if the user is asked to plot a complete contour.

Figure 12

Figure 12

Error convergence with different types of input contours.

Figure 13

Figure 13

Shape convergence with multiple, randomly selected partial contours using Scheme 3

Figure 14

Figure 14

Case Study 3: the entire shape reconstruction process yields a smooth, realistic, and water-tight 3D shape of the prostate.

Figure 15

Figure 15

Examples of 3D prostate shapes created by the proposed shape reconstruction method.

Figure 16

Figure 16

The error distances map and histogram of a process to the other process for each case

Figure 16

Figure 16

The error distances map and histogram of a process to the other process for each case

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