Literature DB >> 21084216

Semi-automatic segmentation for prostate interventions.

S Sara Mahdavi1, Nick Chng, Ingrid Spadinger, William J Morris, Septimiu E Salcudean.   

Abstract

In this paper we report and characterize a semi-automatic prostate segmentation method for prostate brachytherapy. Based on anatomical evidence and requirements of the treatment procedure, a warped and tapered ellipsoid was found suitable as the a-priori 3D shape of the prostate. By transforming the acquired endorectal transverse images of the prostate into ellipses, the shape fitting problem was cast into a convex problem which can be solved efficiently. The average whole gland error between non-overlapping volumes created from manual and semi-automatic contours from 21 patients was 6.63 ± 0.9%. For use in brachytherapy treatment planning, the resulting contours were modified, if deemed necessary, by radiation oncologists prior to treatment. The average whole gland volume error between the volumes computed from semi-automatic contours and those computed from modified contours, from 40 patients, was 5.82 ± 4.15%. The amount of bias in the physicians' delineations when given an initial semi-automatic contour was measured by comparing the volume error between 10 prostate volumes computed from manual contours with those of modified contours. This error was found to be 7.25 ± 0.39% for the whole gland. Automatic contouring reduced subjectivity, as evidenced by a decrease in segmentation inter- and intra-observer variability from 4.65% and 5.95% for manual segmentation to 3.04% and 3.48% for semi-automatic segmentation, respectively. We characterized the performance of the method relative to the reference obtained from manual segmentation by using a novel approach that divides the prostate region into nine sectors. We analyzed each sector independently as the requirements for segmentation accuracy depend on which region of the prostate is considered. The measured segmentation time is 14 ± 1s with an additional 32 ± 14s for initialization. By assuming 1-3 min for modification of the contours, if necessary, a total segmentation time of less than 4 min is required, with no additional time required prior to treatment planning. This compares favorably to the 5-15 min manual segmentation time required for experienced individuals. The method is currently used at the British Columbia Cancer Agency (BCCA) Vancouver Cancer Centre as part of the standard treatment routine in low dose rate prostate brachytherapy and is found to be a fast, consistent and accurate tool for the delineation of the prostate gland in ultrasound images.
Copyright © 2010 Elsevier B.V. All rights reserved.

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Year:  2010        PMID: 21084216      PMCID: PMC3142944          DOI: 10.1016/j.media.2010.10.002

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  24 in total

1.  Intraoperative planning and evaluation of permanent prostate brachytherapy: report of the American Brachytherapy Society.

Authors:  S Nag; J P Ciezki; R Cormack; S Doggett; K DeWyngaert; G K Edmundson; R G Stock; N N Stone; Y Yu; M J Zelefsky
Journal:  Int J Radiat Oncol Biol Phys       Date:  2001-12-01       Impact factor: 7.038

2.  Prostate boundary segmentation from 3D ultrasound images.

Authors:  Ning Hu; Dónal B Downey; Aaron Fenster; Hanif M Ladak
Journal:  Med Phys       Date:  2003-07       Impact factor: 4.071

3.  An interacting multiple model probabilistic data association filter for cavity boundary extraction from ultrasound images.

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4.  STACS: new active contour scheme for cardiac MR image segmentation.

Authors:  Chamchai Pluempitiwiriyawej; José M F Moura; Yi-Jen Lin Wu; Chien Ho
Journal:  IEEE Trans Med Imaging       Date:  2005-05       Impact factor: 10.048

5.  Modeling prostate anatomy from multiple view TRUS images for image-guided HIFU therapy.

Authors:  Michael A Penna; Kris A Dines; Ralf Seip; Roy F Carlson; Narendra T Sanghvi
Journal:  IEEE Trans Ultrason Ferroelectr Freq Control       Date:  2007-01       Impact factor: 2.725

6.  Prostate segmentation in 2D ultrasound images using image warping and ellipse fitting.

Authors:  Sara Badiei; Septimiu E Salcudean; Jim Varah; W James Morris
Journal:  Med Image Comput Comput Assist Interv       Date:  2006

7.  Targeted prostate biopsy using statistical image analysis.

Authors:  Yiqiang Zhan; Dinggang Shen; Jianchao Zeng; Leon Sun; Gabor Fichtinger; Judd Moul; Christos Davatzikos
Journal:  IEEE Trans Med Imaging       Date:  2007-06       Impact factor: 10.048

8.  Robust shape tracking with multiple models in ultrasound images.

Authors:  Jacinto C Nascimento; Jorge S Marques
Journal:  IEEE Trans Image Process       Date:  2008-03       Impact factor: 10.856

9.  A geometric snake model for segmentation of medical imagery.

Authors:  A Yezzi; S Kichenassamy; A Kumar; P Olver; A Tannenbaum
Journal:  IEEE Trans Med Imaging       Date:  1997-04       Impact factor: 10.048

10.  Parametric shape modeling using deformable superellipses for prostate segmentation.

Authors:  Lixin Gong; Sayan D Pathak; David R Haynor; Paul S Cho; Yongmin Kim
Journal:  IEEE Trans Med Imaging       Date:  2004-03       Impact factor: 10.048

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  8 in total

1.  Three-dimensional nonrigid landmark-based magnetic resonance to transrectal ultrasound registration for image-guided prostate biopsy.

Authors:  Yue Sun; Wu Qiu; Jing Yuan; Cesare Romagnoli; Aaron Fenster
Journal:  J Med Imaging (Bellingham)       Date:  2015-06-24

2.  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

3.  Interactive Prostate Shape Reconstruction from 3D TRUS Images.

Authors:  Tomotake Furuhata; Inho Song; Hong Zhang; Yoed Rabin; Kenji Shimada
Journal:  J Comput Des Eng       Date:  2014-12-18

4.  Ultrasound prostate segmentation based on multidirectional deeply supervised V-Net.

Authors:  Yang Lei; Sibo Tian; Xiuxiu He; Tonghe Wang; Bo Wang; Pretesh Patel; Ashesh B Jani; Hui Mao; Walter J Curran; Tian Liu; Xiaofeng Yang
Journal:  Med Phys       Date:  2019-05-29       Impact factor: 4.071

5.  Automatic zonal segmentation of the prostate from 2D and 3D T2-weighted MRI and evaluation for clinical use.

Authors:  Dimitri Hamzaoui; Sarah Montagne; Raphaële Renard-Penna; Nicholas Ayache; Hervé Delingette
Journal:  J Med Imaging (Bellingham)       Date:  2022-03-14

6.  Prostate segmentation in transrectal ultrasound using magnetic resonance imaging priors.

Authors:  Qi Zeng; Golnoosh Samei; Davood Karimi; Claudia Kesch; Sara S Mahdavi; Purang Abolmaesumi; Septimiu E Salcudean
Journal:  Int J Comput Assist Radiol Surg       Date:  2018-03-27       Impact factor: 2.924

7.  A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net.

Authors:  Nicola Altini; Antonio Brunetti; Valeria Pia Napoletano; Francesca Girardi; Emanuela Allegretti; Sardar Mehboob Hussain; Gioacchino Brunetti; Vito Triggiani; Vitoantonio Bevilacqua; Domenico Buongiorno
Journal:  Bioengineering (Basel)       Date:  2022-07-26

8.  A semiautomatic tool for prostate segmentation in radiotherapy treatment planning.

Authors:  Jörn Schulz; Stein Olav Skrøvseth; Veronika Kristine Tømmerås; Kirsten Marienhagen; Fred Godtliebsen
Journal:  BMC Med Imaging       Date:  2014-01-25       Impact factor: 1.930

  8 in total

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