Literature DB >> 27363993

Gland and Zonal Segmentation of Prostate on T2W MR Images.

O Chilali1,2, P Puech1,3, S Lakroum1, M Diaf2, S Mordon1, N Betrouni4.   

Abstract

For many years, prostate segmentation on MR images concerned only the extraction of the entire gland. Currently, in the focal treatment era, there is a continuously increasing need for the separation of the different parts of the organ. In this paper, we propose an automatic segmentation method based on the use of T2W images and atlas images to segment the prostate and to isolate the peripheral and transition zones. The algorithm consists of two stages. First, the target image is registered with each zonal atlas image then the segmentation is obtained by the application of an evidential C-Means clustering. The method was evaluated on a representative and multi-centric image base and yielded mean Dice accuracy values of 0.81, 0.70, and 0.62 for the prostate, the transition zone, and peripheral zone, respectively.

Keywords:  Atlas; MR T2W images; Prostate; Segmentation; Zones

Mesh:

Year:  2016        PMID: 27363993      PMCID: PMC5114230          DOI: 10.1007/s10278-016-9890-0

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  11 in total

1.  EVCLUS: evidential clustering of proximity data.

Authors:  Thierry Denoeux; Marie-Hélène Masson
Journal:  IEEE Trans Syst Man Cybern B Cybern       Date:  2004-02

2.  Zonal segmentation of prostate using multispectral magnetic resonance images.

Authors:  N Makni; A Iancu; O Colot; P Puech; S Mordon; N Betrouni
Journal:  Med Phys       Date:  2011-11       Impact factor: 4.071

3.  Simultaneous Segmentation of Prostatic Zones Using Active Appearance Models With Multiple Coupled Levelsets.

Authors:  Robert Toth; Justin Ribault; John Gentile; Dan Sperling; Anant Madabhushi
Journal:  Comput Vis Image Underst       Date:  2013-09-01       Impact factor: 3.876

4.  Combining a deformable model and a probabilistic framework for an automatic 3D segmentation of prostate on MRI.

Authors:  Nasr Makni; P Puech; R Lopes; A S Dewalle; O Colot; N Betrouni
Journal:  Int J Comput Assist Radiol Surg       Date:  2008-12-03       Impact factor: 2.924

5.  ProstAtlas: a digital morphologic atlas of the prostate.

Authors:  N Betrouni; A Iancu; P Puech; S Mordon; N Makni
Journal:  Eur J Radiol       Date:  2011-05-31       Impact factor: 3.528

6.  A pattern recognition approach to zonal segmentation of the prostate on MRI.

Authors:  Geert Litjens; Oscar Debats; Wendy van de Ven; Nico Karssemeijer; Henkjan Huisman
Journal:  Med Image Comput Comput Assist Interv       Date:  2012

7.  Efficient 3D multi-region prostate MRI segmentation using dual optimization.

Authors:  Wu Qiu; Jing Yuan; Eranga Ukwatta; Yue Sun; Martin Rajchl; Aaron Fenster
Journal:  Inf Process Med Imaging       Date:  2013

8.  Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge.

Authors:  Geert Litjens; Robert Toth; Wendy van de Ven; Caroline Hoeks; Sjoerd Kerkstra; Bram van Ginneken; Graham Vincent; Gwenael Guillard; Neil Birbeck; Jindang Zhang; Robin Strand; Filip Malmberg; Yangming Ou; Christos Davatzikos; Matthias Kirschner; Florian Jung; Jing Yuan; Wu Qiu; Qinquan Gao; Philip Eddie Edwards; Bianca Maan; Ferdinand van der Heijden; Soumya Ghose; Jhimli Mitra; Jason Dowling; Dean Barratt; Henkjan Huisman; Anant Madabhushi
Journal:  Med Image Anal       Date:  2013-12-25       Impact factor: 8.545

9.  A comparison of the biological features between prostate cancers arising in the transition and peripheral zones.

Authors:  Iori Sakai; Ken-Ichi Harada; Isao Hara; Hiroshi Eto; Hideaki Miyake
Journal:  BJU Int       Date:  2005-09       Impact factor: 5.588

10.  Peripheral zone prostate cancers: location and intraprostatic patterns of spread at histopathology.

Authors:  Jérémie Haffner; Eric Potiron; Sébastien Bouyé; Philippe Puech; Xavier Leroy; Laurent Lemaitre; Arnauld Villers
Journal:  Prostate       Date:  2009-02-15       Impact factor: 4.104

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  6 in total

1.  Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.

Authors:  Leon Lenchik; Laura Heacock; Ashley A Weaver; Robert D Boutin; Tessa S Cook; Jason Itri; Christopher G Filippi; Rao P Gullapalli; James Lee; Marianna Zagurovskaya; Tara Retson; Kendra Godwin; Joey Nicholson; Ponnada A Narayana
Journal:  Acad Radiol       Date:  2019-08-10       Impact factor: 3.173

2.  Segmentation of the Prostate Transition Zone and Peripheral Zone on MR Images with Deep Learning.

Authors:  Michelle Bardis; Roozbeh Houshyar; Chanon Chantaduly; Karen Tran-Harding; Alexander Ushinsky; Chantal Chahine; Mark Rupasinghe; Daniel Chow; Peter Chang
Journal:  Radiol Imaging Cancer       Date:  2021-05

3.  Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks.

Authors:  Ruida Cheng; Holger R Roth; Nathan Lay; Le Lu; Baris Turkbey; William Gandler; Evan S McCreedy; Tom Pohida; Peter A Pinto; Peter Choyke; Matthew J McAuliffe; Ronald M Summers
Journal:  J Med Imaging (Bellingham)       Date:  2017-08-21

Review 4.  Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities.

Authors:  Huanye Li; Chau Hung Lee; David Chia; Zhiping Lin; Weimin Huang; Cher Heng Tan
Journal:  Diagnostics (Basel)       Date:  2022-01-24

5.  Segmentation of the prostate, its zones, anterior fibromuscular stroma, and urethra on the MRIs and multimodality image fusion using U-Net model.

Authors:  Seyed Masoud Rezaeijo; Shabnam Jafarpoor Nesheli; Mehdi Fatan Serj; Mohammad Javad Tahmasebi Birgani
Journal:  Quant Imaging Med Surg       Date:  2022-10

6.  Automatic prostate and prostate zones segmentation of magnetic resonance images using DenseNet-like U-net.

Authors:  Nader Aldoj; Federico Biavati; Florian Michallek; Sebastian Stober; Marc Dewey
Journal:  Sci Rep       Date:  2020-08-31       Impact factor: 4.379

  6 in total

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