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

• Published in: Computer methods and programs in biomedicine Vol. 108; no. 1; pp. 262 - 287
• Main Authors: Ghose, Soumya, Oliver, Arnau, Martí, Robert, Lladó, Xavier, Vilanova, Joan C, Freixenet, Jordi, Mitra, Jhimli, Sidibé, Désiré, Meriaudeau, Fabrice
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ireland Ltd 01.10.2012; Elsevier
• Subjects: Acoustic signal processing; Acoustics; Algorithms; Animal tumors. Experimental tumors; Applied sciences; Artificial intelligence; Biological and medical sciences; Computer Science; Computer science; control theory; systems; CT images; Exact sciences and technology; Experimental genital and mammary tumors; Fundamental areas of phenomenology (including applications); Humans; Instruments, apparatus, components and techniques common to several branches of physics and astronomy; Internal Medicine; Magnetic Resonance Imaging - methods; Male; Medical Imaging; Medical sciences; MR images; Other; Pattern recognition. Digital image processing. Computational geometry; Physics; Prostate - anatomy & histology; Prostate - diagnostic imaging; Prostate - pathology; Prostate gland segmentation methods; Tomography, X-Ray Computed - methods; TRUS images; Tumors; Ultrasonography; X- and γ-ray instruments and techniques; TRUS images; CT images; MR images; Prostate gland segmentation methods; Optical interferometry; Segmentation; Taxonomy; Variability; Shadow; Artefact; Texture; Tissue; Ultrasonic imaging; Imaging; Morphological analysis; Diagnosis; Acoustic image; Speckle; Similarity solution; Male genital system; Nuclear magnetic resonance imaging; Rectal administration; Computerized tomography; Calcification; Medical imagery; Prostate; Strength
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2012.04.006

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.