Literature DB >> 17498571

Automatic segmentation of pelvic structures from magnetic resonance images for prostate cancer radiotherapy.

David Pasquier1, Thomas Lacornerie, Maximilien Vermandel, Jean Rousseau, Eric Lartigau, Nacim Betrouni.   

Abstract

PURPOSE: Target-volume and organ-at-risk delineation is a time-consuming task in radiotherapy planning. The development of automated segmentation tools remains problematic, because of pelvic organ shape variability. We evaluate a three-dimensional (3D), deformable-model approach and a seeded region-growing algorithm for automatic delineation of the prostate and organs-at-risk on magnetic resonance images. METHODS AND MATERIALS: Manual and automatic delineation were compared in 24 patients using a sagittal T2-weighted (T2-w) turbo spin echo (TSE) sequence and an axial T1-weighted (T1-w) 3D fast-field echo (FFE) or TSE sequence. For automatic prostate delineation, an organ model-based method was used. Prostates without seminal vesicles were delineated as the clinical target volume (CTV). For automatic bladder and rectum delineation, a seeded region-growing method was used. Manual contouring was considered the reference method. The following parameters were measured: volume ratio (Vr) (automatic/manual), volume overlap (Vo) (ratio of the volume of intersection to the volume of union; optimal value = 1), and correctly delineated volume (Vc) (percent ratio of the volume of intersection to the manually defined volume; optimal value = 100).
RESULTS: For the CTV, the Vr, Vo, and Vc were 1.13 (+/-0.1 SD), 0.78 (+/-0.05 SD), and 94.75 (+/-3.3 SD), respectively. For the rectum, the Vr, Vo, and Vc were 0.97 (+/-0.1 SD), 0.78 (+/-0.06 SD), and 86.52 (+/-5 SD), respectively. For the bladder, the Vr, Vo, and Vc were 0.95 (+/-0.03 SD), 0.88 (+/-0.03 SD), and 91.29 (+/-3.1 SD), respectively.
CONCLUSIONS: Our results show that the organ-model method is robust, and results in reproducible prostate segmentation with minor interactive corrections. For automatic bladder and rectum delineation, magnetic resonance imaging soft-tissue contrast enables the use of region-growing methods.

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Year:  2007        PMID: 17498571     DOI: 10.1016/j.ijrobp.2007.02.005

Source DB:  PubMed          Journal:  Int J Radiat Oncol Biol Phys        ISSN: 0360-3016            Impact factor:   7.038


  25 in total

1.  Bladder segmentation in MRI images using active region growing model.

Authors:  Carole Garnier; Wu Ke; Jean-Louis Dillenseger
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2011

2.  Assessment of accuracy and efficiency of atlas-based autosegmentation for prostate radiotherapy in a variety of clinical conditions.

Authors:  I Simmat; P Georg; D Georg; W Birkfellner; G Goldner; M Stock
Journal:  Strahlenther Onkol       Date:  2012-06-07       Impact factor: 3.621

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.  Segmenting CT prostate images using population and patient-specific statistics for radiotherapy.

Authors:  Qianjin Feng; Mark Foskey; Wufan Chen; Dinggang Shen
Journal:  Med Phys       Date:  2010-08       Impact factor: 4.071

Review 5.  MR-guided prostate interventions.

Authors:  Clare Tempany; Sarah Straus; Nobuhiko Hata; Steven Haker
Journal:  J Magn Reson Imaging       Date:  2008-02       Impact factor: 4.813

6.  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

7.  Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study.

Authors:  M A Deeley; A Chen; R Datteri; J H Noble; A J Cmelak; E F Donnelly; A W Malcolm; L Moretti; J Jaboin; K Niermann; Eddy S Yang; David S Yu; F Yei; T Koyama; G X Ding; B M Dawant
Journal:  Phys Med Biol       Date:  2011-07-01       Impact factor: 3.609

8.  Iterative-cuts: longitudinal and scale-invariant segmentation via user-defined templates for rectosigmoid colon in gynecological brachytherapy.

Authors:  Tobias Lüddemann; Jan Egger
Journal:  J Med Imaging (Bellingham)       Date:  2016-06-20

9.  Visual saliency-based active learning for prostate magnetic resonance imaging segmentation.

Authors:  Dwarikanath Mahapatra; Joachim M Buhmann
Journal:  J Med Imaging (Bellingham)       Date:  2016-02-19

10.  A hierarchical method based on active shape models and directed Hough transform for segmentation of noisy biomedical images; application in segmentation of pelvic X-ray images.

Authors:  Rebecca Smith; Kayvan Najarian; Kevin Ward
Journal:  BMC Med Inform Decis Mak       Date:  2009-11-03       Impact factor: 2.796

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