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Interactive prostate shape reconstruction from 3D TRUS images

  • Furuhata, Tomotake (Department of Mechanical Engineering, Carnegie Mellon University) ;
  • Song, Inho (Department of Mechanical Engineering, Carnegie Mellon University) ;
  • Zhang, Hong (Department of Mechanical Engineering, Carnegie Mellon University) ;
  • Rabin, Yoed (Department of Mechanical Engineering, Carnegie Mellon University) ;
  • Shimada, Kenji (Department of Mechanical Engineering, Carnegie Mellon University)
  • Received : 2014.06.22
  • Accepted : 2014.09.11
  • Published : 2014.10.01

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

Acknowledgement

Supported by : National Cancer Institute

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