Literature DB >> 27872873

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

Maysam Shahedi1, Derek W Cool2, Cesare Romagnoli3, Glenn S Bauman4, Matthew Bastian-Jordan3, George Rodrigues5, Belal Ahmad5, Michael Lock5, Aaron Fenster6, Aaron D Ward7.   

Abstract

Prostate segmentation on T2w MRI is important for several diagnostic and therapeutic procedures for prostate cancer. Manual segmentation is time-consuming, labor-intensive, and subject to high interobserver variability. This study investigated the suitability of computer-assisted segmentation algorithms for clinical translation, based on measurements of interoperator variability and measurements of the editing time required to yield clinically acceptable segmentations. A multioperator pilot study was performed under three pre- and postediting conditions: manual, semiautomatic, and automatic segmentation. We recorded the required editing time for each segmentation and measured the editing magnitude based on five different spatial metrics. We recorded average editing times of 213, 328, and 393 s for manual, semiautomatic, and automatic segmentation respectively, while an average fully manual segmentation time of 564 s was recorded. The reduced measured postediting interoperator variability of semiautomatic and automatic segmentations compared to the manual approach indicates the potential of computer-assisted segmentation for generating a clinically acceptable segmentation faster with higher consistency. The lack of strong correlation between editing time and the values of typically used error metrics ([Formula: see text]) implies that the necessary postsegmentation editing time needs to be measured directly in order to evaluate an algorithm's suitability for clinical translation.

Entities:  

Keywords:  editing time; image segmentation; magnetic resonance imaging; observer study; prostate; repeatability

Year:  2016        PMID: 27872873      PMCID: PMC5097979          DOI: 10.1117/1.JMI.3.4.046002

Source DB:  PubMed          Journal:  J Med Imaging (Bellingham)        ISSN: 2329-4302


  28 in total

1.  Automatic prostate MR image segmentation with sparse label propagation and domain-specific manifold regularization.

Authors:  Shu Liao; Yaozong Gao; Yinghuan Shi; Ambereen Yousuf; Ibrahim Karademir; Aytekin Oto; Dinggang Shen
Journal:  Inf Process Med Imaging       Date:  2013

2.  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

3.  Spatially varying accuracy and reproducibility of prostate segmentation in magnetic resonance images using manual and semiautomated methods.

Authors:  Maysam Shahedi; Derek W Cool; Cesare Romagnoli; Glenn S Bauman; Matthew Bastian-Jordan; Eli Gibson; George Rodrigues; Belal Ahmad; Michael Lock; Aaron Fenster; Aaron D Ward
Journal:  Med Phys       Date:  2014-11       Impact factor: 4.071

4.  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

5.  Transition zone prostate cancers: features, detection, localization, and staging at endorectal MR imaging.

Authors:  Oguz Akin; Evis Sala; Chaya S Moskowitz; Kentaro Kuroiwa; Nicole M Ishill; Darko Pucar; Peter T Scardino; Hedvig Hricak
Journal:  Radiology       Date:  2006-03-28       Impact factor: 11.105

Review 6.  Current role of MR imaging in the staging of adenocarcinoma of the prostate.

Authors:  M L Schiebler; M D Schnall; H M Pollack; R E Lenkinski; J E Tomaszewski; A J Wein; R Whittington; W Rauschning; H Y Kressel
Journal:  Radiology       Date:  1993-11       Impact factor: 11.105

Review 7.  State-of-the-art uroradiologic imaging in the diagnosis of prostate cancer.

Authors:  Stijn W T P J Heijmink; Jurgen J Fütterer; Stephen S Strum; Wim J G Oyen; Ferdinand Frauscher; J Alfred Witjes; Jelle O Barentsz
Journal:  Acta Oncol       Date:  2011-06       Impact factor: 4.089

8.  Semi-automatic segmentation for prostate interventions.

Authors:  S Sara Mahdavi; Nick Chng; Ingrid Spadinger; William J Morris; Septimiu E Salcudean
Journal:  Med Image Anal       Date:  2010-10-26       Impact factor: 8.545

9.  Prostate cancer: local staging with endorectal surface coil MR imaging.

Authors:  M D Schnall; Y Imai; J Tomaszewski; H M Pollack; R E Lenkinski; H Y Kressel
Journal:  Radiology       Date:  1991-03       Impact factor: 11.105

10.  A multiphase validation of atlas-based automatic and semiautomatic segmentation strategies for prostate MRI.

Authors:  Spencer Martin; George Rodrigues; Nikhilesh Patil; Glenn Bauman; David D'Souza; Tracy Sexton; David Palma; Alexander V Louie; Farzad Khalvati; Hamid R Tizhoosh; Stewart Gaede
Journal:  Int J Radiat Oncol Biol Phys       Date:  2012-05-08       Impact factor: 7.038

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  1 in total

1.  A semiautomatic approach for prostate segmentation in MR images using local texture classification and statistical shape modeling.

Authors:  Maysam Shahedi; Martin Halicek; Qinmei Li; Lizhi Liu; Zhenfeng Zhang; Sadhna Verma; David M Schuster; Baowei Fei
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2019-03-08
  1 in total

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