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

Other authors

Ministerio de Ciencia e Innovación (Espanya)

Publication date

info:eu-repo/date/embargoEnd/2026-01-01

info:eu-repo/date/embargoEnd/2026-01-01

2012-10



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


This research has been funded by VALTEC 08-1-0039 of Generalitat de Catalunya, Spain and Conseil Regional de Bourgogne, France. The research is partially funded by Spanish Science and Innovation grant no. TIN2011-23704

Document Type

Article


Published version

Language

English

Publisher

Elsevier

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