Literature DB >> 18804333

Automatic segmentation of whole breast using atlas approach and deformable image registration.

Valerie K Reed1, Wendy A Woodward, Lifei Zhang, Eric A Strom, George H Perkins, Welela Tereffe, Julia L Oh, T Kuan Yu, Isabelle Bedrosian, Gary J Whitman, Thomas A Buchholz, Lei Dong.   

Abstract

PURPOSE: To compare interobserver variations in delineating the whole breast for treatment planning using two contouring methods. METHODS AND MATERIALS: Autosegmented contours were generated by a deformable image registration-based breast segmentation method (DEF-SEG) by mapping the whole breast clinical target volume (CTVwb) from a template case to a new patient case. Eight breast radiation oncologists modified the autosegmented contours as necessary to achieve a clinically appropriate CTVwb and then recontoured the same case from scratch for comparison. The times to complete each approach, as well as the interobserver variations, were analyzed. The template case was also mapped to 10 breast cancer patients with a body mass index of 19.1-35.9 kg/m(2). The three-dimensional surface-to-surface distances and volume overlapping analyses were computed to quantify contour variations.
RESULTS: The median time to edit the DEF-SEG-generated CTVwb was 12.9 min (range, 3.4-35.9) compared with 18.6 min (range, 8.9-45.2) to contour the CTVwb from scratch (30% faster, p = 0.028). The mean surface-to-surface distance was noticeably reduced from 1.6 mm among the contours generated from scratch to 1.0 mm using the DEF-SEG method (p = 0.047). The deformed contours in 10 patients achieved 94% volume overlap before correction and required editing of 5% (range, 1-10%) of the contoured volume.
CONCLUSION: Significant interobserver variations suggested a lack of consensus regarding the CTVwb, even among breast cancer specialists. Using the DEF-SEG method produced more consistent results and required less time. The DEF-SEG method can be successfully applied to patients with different body mass indexes.

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Year:  2008        PMID: 18804333      PMCID: PMC2729433          DOI: 10.1016/j.ijrobp.2008.07.001

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


  12 in total

1.  A comparison of different intensity modulation treatment techniques for tangential breast irradiation.

Authors:  S X Chang; K M Deschesne; T J Cullip; S A Parker; J Earnhart
Journal:  Int J Radiat Oncol Biol Phys       Date:  1999-12-01       Impact factor: 7.038

2.  Geometric atlas: modeling the cortex as an organized surface.

Authors:  Roberto Toro; Yves Burnod
Journal:  Neuroimage       Date:  2003-11       Impact factor: 6.556

3.  Reduce in variation and improve efficiency of target volume delineation by a computer-assisted system using a deformable image registration approach.

Authors:  K S Clifford Chao; Shreerang Bhide; Hansen Chen; Joshua Asper; Steven Bush; Gregg Franklin; Vivek Kavadi; Vichaivood Liengswangwong; William Gordon; Adam Raben; Jon Strasser; Christopher Koprowski; Steven Frank; Gregory Chronowski; Anesa Ahamad; Robert Malyapa; Lifei Zhang; Lei Dong
Journal:  Int J Radiat Oncol Biol Phys       Date:  2007-08-01       Impact factor: 7.038

4.  Multileaf field-in-field forward-planned intensity-modulated dose compensation for whole-breast irradiation is associated with reduced contralateral breast dose: a phantom model comparison.

Authors:  Yerko O Borghero; Mohammad Salehpour; Marsha D McNeese; Marilyn Stovall; Susan A Smith; Jennifer Johnson; George H Perkins; Eric A Strom; Julia L Oh; Steven M Kirsner; Wendy A Woodward; Tse-Kuan Yu; Thomas A Buchholz
Journal:  Radiother Oncol       Date:  2006-12-08       Impact factor: 6.280

5.  Variability among breast radiation oncologists in delineation of the postsurgical lumpectomy cavity.

Authors:  Daniel M Landis; Weixiu Luo; Jun Song; Jennifer R Bellon; Rinaa S Punglia; Julia S Wong; Joseph H Killoran; Rebecca Gelman; Jay R Harris
Journal:  Int J Radiat Oncol Biol Phys       Date:  2007-02-01       Impact factor: 7.038

6.  A novel method for comparing 3D target volume delineations in radiotherapy.

Authors:  R W van der Put; B W Raaymakers; E M Kerkhof; M van Vulpen; J J W Lagendijk
Journal:  Phys Med Biol       Date:  2008-04-01       Impact factor: 3.609

7.  Validation of an accelerated 'demons' algorithm for deformable image registration in radiation therapy.

Authors:  He Wang; Lei Dong; Jennifer O'Daniel; Radhe Mohan; Adam S Garden; K Kian Ang; Deborah A Kuban; Mark Bonnen; Joe Y Chang; Rex Cheung
Journal:  Phys Med Biol       Date:  2005-06-01       Impact factor: 3.609

8.  Variability in target volume delineation on CT scans of the breast.

Authors:  C W Hurkmans; J H Borger; B R Pieters; N S Russell; E P Jansen; B J Mijnheer
Journal:  Int J Radiat Oncol Biol Phys       Date:  2001-08-01       Impact factor: 7.038

9.  Statistical validation of image segmentation quality based on a spatial overlap index.

Authors:  Kelly H Zou; Simon K Warfield; Aditya Bharatha; Clare M C Tempany; Michael R Kaus; Steven J Haker; William M Wells; Ferenc A Jolesz; Ron Kikinis
Journal:  Acad Radiol       Date:  2004-02       Impact factor: 3.173

10.  Variability of target and normal structure delineation for breast cancer radiotherapy: an RTOG Multi-Institutional and Multiobserver Study.

Authors:  X Allen Li; An Tai; Douglas W Arthur; Thomas A Buchholz; Shannon Macdonald; Lawrence B Marks; Jean M Moran; Lori J Pierce; Rachel Rabinovitch; Alphonse Taghian; Frank Vicini; Wendy Woodward; Julia R White
Journal:  Int J Radiat Oncol Biol Phys       Date:  2009-03-01       Impact factor: 7.038

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  34 in total

1.  Template-based automatic breast segmentation on MRI by excluding the chest region.

Authors:  Muqing Lin; Jeon-Hor Chen; Xiaoyong Wang; Siwa Chan; Siping Chen; Min-Ying Su
Journal:  Med Phys       Date:  2013-12       Impact factor: 4.071

2.  Impact of positional difference on the measurement of breast density using MRI.

Authors:  Jeon-Hor Chen; Siwa Chan; Yi-Ting Tang; Jia Shen Hon; Po-Chuan Tseng; Angela T Cheriyan; Nikita Rakesh Shah; Dah-Cherng Yeh; San-Kan Lee; Wen-Pin Chen; Christine E McLaren; Min-Ying Su
Journal:  Med Phys       Date:  2015-05       Impact factor: 4.071

3.  Accuracy of patient-specific organ dose estimates obtained using an automated image segmentation algorithm.

Authors:  Taly Gilat Schmidt; Adam S Wang; Thomas Coradi; Benjamin Haas; Josh Star-Lack
Journal:  J Med Imaging (Bellingham)       Date:  2016-11-29

4.  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

Review 5.  Interobserver variation in parotid gland delineation: a study of its impact on intensity-modulated radiotherapy solutions with a systematic review of the literature.

Authors:  S W Loo; W M C Martin; P Smith; S Cherian; T W Roques
Journal:  Br J Radiol       Date:  2012-08       Impact factor: 3.039

6.  Automated 3D closed surface segmentation: application to vertebral body segmentation in CT images.

Authors:  Shuang Liu; Yiting Xie; Anthony P Reeves
Journal:  Int J Comput Assist Radiol Surg       Date:  2015-11-11       Impact factor: 2.924

7.  Statistical modeling approach to quantitative analysis of interobserver variability in breast contouring.

Authors:  Jinzhong Yang; Wendy A Woodward; Valerie K Reed; Eric A Strom; George H Perkins; Welela Tereffe; Thomas A Buchholz; Lifei Zhang; Peter Balter; Laurence E Court; X Allen Li; Lei Dong
Journal:  Int J Radiat Oncol Biol Phys       Date:  2014-03-07       Impact factor: 7.038

8.  Atlas ranking and selection for automatic segmentation of the esophagus from CT scans.

Authors:  Jinzhong Yang; Benjamin Haas; Raymond Fang; Beth M Beadle; Adam S Garden; Zhongxing Liao; Lifei Zhang; Peter Balter; Laurence Court
Journal:  Phys Med Biol       Date:  2017-11-14       Impact factor: 3.609

Review 9.  Radiological images and machine learning: Trends, perspectives, and prospects.

Authors:  Zhenwei Zhang; Ervin Sejdić
Journal:  Comput Biol Med       Date:  2019-02-27       Impact factor: 4.589

10.  Head and neck lymph node region delineation with image registration.

Authors:  Chia-Chi Teng; Linda G Shapiro; Ira J Kalet
Journal:  Biomed Eng Online       Date:  2010-06-22       Impact factor: 2.819

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