Literature DB >> 11338100

Image segmentation in treatment planning for prostate cancer using the region growing technique.

M Mazonakis1, J Damilakis, H Varveris, P Prassopoulos, N Gourtsoyiannis.   

Abstract

The purpose of this study was to evaluate the performance of a region growing technique for segmenting prostate, bladder and rectum in CT images of prostate cancer patients. Prostate, bladder and rectum were segmented in all CT images of 10 patients using the region growing technique and manual tracing. Volumes of the above organs computed with the region growing technique were compared with those from manually traced images on a slice-by-slice basis. Measurement reproducibility of both segmentation techniques was evaluated using the data obtained from four independent observers. The region growing technique was 1.5 times faster than manual tracing. There was no statistical difference between the slice volumes of prostate, bladder and rectum obtained by the two segmentation techniques (p > 0.05, paired Student's t-test). Correlation between slice volumes of all organs of interest provided both by region growing and by manual tracing was very good (prostate r2 = 0.84; bladder r2 = 0.93; rectum r2 = 0.85). An overall reasonable agreement was found between the two segmentation techniques. The intraobserver and interobserver variations for prostate, bladder and rectum volume segmentation were found to be lower with the region growing technique than with manual tracing. The suggested semi-automatic technique allows the possibility of generating accurate and reproducible segmentation of prostate, bladder and rectum from CT data with great saving in labour.

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Year:  2001        PMID: 11338100     DOI: 10.1259/bjr.74.879.740243

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  8 in total

1.  Concurrent segmentation of the prostate on MRI and CT via linked statistical shape models for radiotherapy planning.

Authors:  Najeeb Chowdhury; Robert Toth; Jonathan Chappelow; Sung Kim; Sabin Motwani; Salman Punekar; Haibo Lin; Stefan Both; Neha Vapiwala; Stephen Hahn; Anant Madabhushi
Journal:  Med Phys       Date:  2012-04       Impact factor: 4.071

2.  A geodesic deformable model for automatic segmentation of image sequences applied to radiation therapy.

Authors:  G Bueno; O Déniz; J Salido; C Carrascosa; J M Delgado
Journal:  Int J Comput Assist Radiol Surg       Date:  2010-07-20       Impact factor: 2.924

3.  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

4.  The effect of concurrent androgen deprivation and 3D conformal radiotherapy on prostate volume and clinical organ doses during treatment for prostate cancer.

Authors:  C Onal; E Topkan; E Efe; M Yavuz; G Arslan; A Yavuz
Journal:  Br J Radiol       Date:  2009-07-06       Impact factor: 3.039

5.  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

6.  Learning image context for segmentation of the prostate in CT-guided radiotherapy.

Authors:  Wei Li; Shu Liao; Qianjin Feng; Wufan Chen; Dinggang Shen
Journal:  Phys Med Biol       Date:  2012-02-17       Impact factor: 3.609

Review 7.  Overview of radiomics in prostate imaging and future directions.

Authors:  Hwan-Ho Cho; Chan Kyo Kim; Hyunjin Park
Journal:  Br J Radiol       Date:  2021-11-29       Impact factor: 3.039

8.  Accumulating daily-varied dose distributions of prostate radiation therapy with soft-tissue-based kV CT guidance.

Authors:  Andrew Godley; Ergun Ahunbay; Cheng Peng; X Allen Li
Journal:  J Appl Clin Med Phys       Date:  2012-05-10       Impact factor: 2.102

  8 in total

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