Literature DB >> 22739209

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

Soumya Ghose1, Arnau Oliver, Robert Martí, Xavier Lladó, Joan C Vilanova, Jordi Freixenet, Jhimli Mitra, Désiré Sidibé, Fabrice Meriaudeau.   

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.
Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

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Year:  2012        PMID: 22739209     DOI: 10.1016/j.cmpb.2012.04.006

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  27 in total

1.  Postediting prostate magnetic resonance imaging segmentation consistency and operator time using manual and computer-assisted segmentation: multiobserver study.

Authors:  Maysam Shahedi; Derek W Cool; Cesare Romagnoli; Glenn S Bauman; Matthew Bastian-Jordan; George Rodrigues; Belal Ahmad; Michael Lock; Aaron Fenster; Aaron D Ward
Journal:  J Med Imaging (Bellingham)       Date:  2016-11-07

2.  Validation of a method for retroperitoneal tumor segmentation.

Authors:  Cristina Suárez-Mejías; José A Pérez-Carrasco; Carmen Serrano; José L López-Guerra; Tomás Gómez-Cía; Carlos L Parra-Calderón; Begoña Acha
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-02-10       Impact factor: 2.924

3.  A Learning-Based CT Prostate Segmentation Method via Joint Transductive Feature Selection and Regression.

Authors:  Yinghuan Shi; Yaozong Gao; Shu Liao; Daoqiang Zhang; Yang Gao; Dinggang Shen
Journal:  Neurocomputing       Date:  2016-01-15       Impact factor: 5.719

4.  Segmentation of prostate from ultrasound images using level sets on active band and intensity variation across edges.

Authors:  Xu Li; Chunming Li; Andriy Fedorov; Tina Kapur; Xiaoping Yang
Journal:  Med Phys       Date:  2016-06       Impact factor: 4.071

5.  CT prostate segmentation based on synthetic MRI-aided deep attention fully convolution network.

Authors:  Yang Lei; Xue Dong; Zhen Tian; Yingzi Liu; Sibo Tian; Tonghe Wang; Xiaojun Jiang; Pretesh Patel; Ashesh B Jani; Hui Mao; Walter J Curran; Tian Liu; Xiaofeng Yang
Journal:  Med Phys       Date:  2019-12-03       Impact factor: 4.071

6.  A combined learning algorithm for prostate segmentation on 3D CT images.

Authors:  Ling Ma; Rongrong Guo; Guoyi Zhang; David M Schuster; Baowei Fei
Journal:  Med Phys       Date:  2017-09-22       Impact factor: 4.071

Review 7.  Clinical value of prostate segmentation and volume determination on MRI in benign prostatic hyperplasia.

Authors:  Brian Garvey; Barış Türkbey; Hong Truong; Marcelino Bernardo; Senthil Periaswamy; Peter L Choyke
Journal:  Diagn Interv Radiol       Date:  2014 May-Jun       Impact factor: 2.630

8.  Combining Population and Patient-Specific Characteristics for Prostate Segmentation on 3D CT Images.

Authors:  Ling Ma; Rongrong Guo; Zhiqiang Tian; Rajesh Venkataraman; Saradwata Sarkar; Xiabi Liu; Funmilayo Tade; David M Schuster; Baowei Fei
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2016-03-21

Review 9.  Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications.

Authors:  Lizhi Liu; Zhiqiang Tian; Zhenfeng Zhang; Baowei Fei
Journal:  Acad Radiol       Date:  2016-04-25       Impact factor: 3.173

10.  Sparse patch-based label propagation for accurate prostate localization in CT images.

Authors:  Shu Liao; Yaozong Gao; Jun Lian; Dinggang Shen
Journal:  IEEE Trans Med Imaging       Date:  2012-11-27       Impact factor: 10.048

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