Literature DB >> 21962822

Performance of an atlas-based autosegmentation software for delineation of target volumes for radiotherapy of breast and anorectal cancer.

Lisanne C Anders1, Florian Stieler, Kerstin Siebenlist, Jörg Schäfer, Frank Lohr, Frederik Wenz.   

Abstract

BACKGROUND AND
PURPOSE: To validate atlas-based autosegmentation for contouring breast/anorectal targets. METHODS AND MATERIALS: ABAS uses atlases with defined CTVs as template cases to automatically delineate target volumes in other patient CT-datasets. Results are compared with manually contoured CTVs of breast/anorectal cancer according to RTOG-guidelines. The impact of using specific atlases matched to individual patient geometry was evaluated. Results were quantified by analyzing Dice Similarity Coefficient (DSC), logit(DSC) and Percent Overlap (PO). DSC >0.700 and logit(DSC) >0.847 are acceptable. In addition a new algorithm (STAPLE) was evaluated.
RESULTS: ABAS produced good results for the CTV of breast/anorectal cancer targets. Delineation of inguinal lymphatic drainage, however, was insufficient. Results for breast CTV were (DSC: 0.86-0.91 ([0,1]), logit(DSC): 1.82-2.36 ([-∞,∞]), PO: 75.5-82.89%) and for anorectal CTVA (DSC: 0.79-0.85, logit(DSC): 1.40-1.77, PO: 68-73.67%).
CONCLUSIONS: ABAS produced satisfactory results for these clinical target volumes that are defined by more complex tissue interface geometry, thus streamlining and facilitating the radiotherapy workflow which is essential to face increasing demand and limited resources. STAPLE improved contouring outcome. Small target volumes not clearly defined are still to be delineated manually. Based on these results, ABAS has been clinically introduced for precontouring of CTVs/OARs.
Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

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Mesh:

Year:  2011        PMID: 21962822     DOI: 10.1016/j.radonc.2011.08.043

Source DB:  PubMed          Journal:  Radiother Oncol        ISSN: 0167-8140            Impact factor:   6.280


  27 in total

1.  The utility of atlas-assisted segmentation in the male pelvis is dependent on the interobserver agreement of the structures segmented.

Authors:  K A Langmack; C Perry; C Sinstead; J Mills; D Saunders
Journal:  Br J Radiol       Date:  2014-08-29       Impact factor: 3.039

2.  Consequences of anorectal cancer atlas implementation in the cooperative group setting: radiobiologic analysis of a prospective randomized in silico target delineation study.

Authors:  Panayiotis Mavroidis; Drosoula Giantsoudis; Musaddiq J Awan; Jasper Nijkamp; Coen R N Rasch; Joop C Duppen; Charles R Thomas; Paul Okunieff; William E Jones; Lisa A Kachnic; Niko Papanikolaou; Clifton D Fuller
Journal:  Radiother Oncol       Date:  2014-07-01       Impact factor: 6.280

3.  A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists' delineations and with the surgical specimen.

Authors:  Emmanuel Rios Velazquez; Hugo J W L Aerts; Yuhua Gu; Dmitry B Goldgof; Dirk De Ruysscher; Andre Dekker; René Korn; Robert J Gillies; Philippe Lambin
Journal:  Radiother Oncol       Date:  2012-11-15       Impact factor: 6.280

4.  Prospective randomized double-blind study of atlas-based organ-at-risk autosegmentation-assisted radiation planning in head and neck cancer.

Authors:  Gary V Walker; Musaddiq Awan; Randa Tao; Eugene J Koay; Nicholas S Boehling; Jonathan D Grant; Dean F Sittig; Gary Brandon Gunn; Adam S Garden; Jack Phan; William H Morrison; David I Rosenthal; Abdallah Sherif Radwan Mohamed; Clifton David Fuller
Journal:  Radiother Oncol       Date:  2014-09-09       Impact factor: 6.280

5.  Cascaded atrous convolution and spatial pyramid pooling for more accurate tumor target segmentation for rectal cancer radiotherapy.

Authors:  Kuo Men; Pamela Boimel; James Janopaul-Naylor; Haoyu Zhong; Mi Huang; Huaizhi Geng; Chingyun Cheng; Yong Fan; John P Plastaras; Edgar Ben-Josef; Ying Xiao
Journal:  Phys Med Biol       Date:  2018-09-17       Impact factor: 3.609

6.  PeriorbitAI: Artificial Intelligence Automation of Eyelid and Periorbital Measurements.

Authors:  Alexandra Van Brummen; Julia P Owen; Theodore Spaide; Colin Froines; Randy Lu; Megan Lacy; Marian Blazes; Emily Li; Cecilia S Lee; Aaron Y Lee; Matthew Zhang
Journal:  Am J Ophthalmol       Date:  2021-05-16       Impact factor: 5.258

7.  Manual versus semiautomatic segmentation of soft-tissue sarcomas on magnetic resonance imaging: evaluation of similarity and comparison of segmentation times.

Authors:  Fernando Carrasco Ferreira Dionisio; Larissa Santos Oliveira; Mateus de Andrade Hernandes; Edgard Eduard Engel; Paulo Mazzoncini de Azevedo-Marques; Marcello Henrique Nogueira-Barbosa
Journal:  Radiol Bras       Date:  2021 May-Jun

8.  A Preliminary Experience of Implementing Deep-Learning Based Auto-Segmentation in Head and Neck Cancer: A Study on Real-World Clinical Cases.

Authors:  Yang Zhong; Yanju Yang; Yingtao Fang; Jiazhou Wang; Weigang Hu
Journal:  Front Oncol       Date:  2021-05-05       Impact factor: 6.244

Review 9.  Radiomics in radiation oncology for gynecological malignancies: a review of literature.

Authors:  Morgan Michalet; David Azria; Marion Tardieu; Hichem Tibermacine; Stéphanie Nougaret
Journal:  Br J Radiol       Date:  2021-05-07       Impact factor: 3.629

10.  Creation of RTOG compliant patient CT-atlases for automated atlas based contouring of local regional breast and high-risk prostate cancers.

Authors:  Vikram M Velker; George B Rodrigues; Robert Dinniwell; Jeremiah Hwee; Alexander V Louie
Journal:  Radiat Oncol       Date:  2013-07-25       Impact factor: 3.481

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