A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images

  • Authors:

  • Soumya Ghose;Arnau Oliver;Robert Martí;Xavier Lladó;Joan C. Vilanova;Jordi Freixenet;Jhimli Mitra;DéSiré Sidibé;Fabrice Meriaudeau

  • Affiliations:

  • Computer Vision and Robotics Group, University of Girona, Campus Montilivi, Edifici P-IV, 17071 Girona, Spain and Laboratoire Le2I - UMR CNRS 6306, Université de Bourgogne, 12 Rue de la Fonde ...;Computer Vision and Robotics Group, University of Girona, Campus Montilivi, Edifici P-IV, 17071 Girona, Spain;Computer Vision and Robotics Group, University of Girona, Campus Montilivi, Edifici P-IV, 17071 Girona, Spain;Computer Vision and Robotics Group, University of Girona, Campus Montilivi, Edifici P-IV, 17071 Girona, Spain;Girona Magnetic Resonance Center, Joan Maragall 26, 17002 Girona, Spain;Computer Vision and Robotics Group, University of Girona, Campus Montilivi, Edifici P-IV, 17071 Girona, Spain;Computer Vision and Robotics Group, University of Girona, Campus Montilivi, Edifici P-IV, 17071 Girona, Spain and Laboratoire Le2I - UMR CNRS 6306, Université de Bourgogne, 12 Rue de la Fonde ...;Laboratoire Le2I - UMR CNRS 6306, Université de Bourgogne, 12 Rue de la Fonderie, 71200 Le Creusot, France;Laboratoire Le2I - UMR CNRS 6306, Université de Bourgogne, 12 Rue de la Fonderie, 71200 Le Creusot, France

  • Venue:
  • Computer Methods and Programs in Biomedicine
  • Year:
  • 2012

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Abstract

Prostate segmentation is a challenging task, and the challenges significantly differ from one imaging modality to another. Low contrast, speckle, micro-calcifications and imaging artifacts like shadow poses serious challenges to accurate prostate segmentation in transrectal ultrasound (TRUS) images. However in magnetic resonance (MR) images, superior soft tissue contrast highlights large variability in shape, size and texture information inside the prostate. In contrast poor soft tissue contrast between prostate and surrounding tissues in computed tomography (CT) images pose a challenge in accurate prostate segmentation. This article reviews the methods developed for prostate gland segmentation TRUS, MR and CT images, the three primary imaging modalities that aids prostate cancer diagnosis and treatment. The objective of this work is to study the key similarities and differences among the different methods, highlighting their strengths and weaknesses in order to assist in the choice of an appropriate segmentation methodology. We define a new taxonomy for prostate segmentation strategies that allows first to group the algorithms and then to point out the main advantages and drawbacks of each strategy. We provide a comprehensive description of the existing methods in all TRUS, MR and CT modalities, highlighting their key-points and features. Finally, a discussion on choosing the most appropriate segmentation strategy for a given imaging modality is provided. A quantitative comparison of the results as reported in literature is also presented.