Literature DB >> 23685866

Segmentation editing improves efficiency while reducing inter-expert variation and maintaining accuracy for normal brain tissues in the presence of space-occupying lesions.

M A Deeley1, A Chen, R D Datteri, J Noble, A Cmelak, E Donnelly, A Malcolm, L Moretti, J Jaboin, K Niermann, Eddy S Yang, David S Yu, B M Dawant.   

Abstract

Image segmentation has become a vital and often rate-limiting step in modern radiotherapy treatment planning. In recent years, the pace and scope of algorithm development, and even introduction into the clinic, have far exceeded evaluative studies. In this work we build upon our previous evaluation of a registration driven segmentation algorithm in the context of 8 expert raters and 20 patients who underwent radiotherapy for large space-occupying tumours in the brain. In this work we tested four hypotheses concerning the impact of manual segmentation editing in a randomized single-blinded study. We tested these hypotheses on the normal structures of the brainstem, optic chiasm, eyes and optic nerves using the Dice similarity coefficient, volume, and signed Euclidean distance error to evaluate the impact of editing on inter-rater variance and accuracy. Accuracy analyses relied on two simulated ground truth estimation methods: simultaneous truth and performance level estimation and a novel implementation of probability maps. The experts were presented with automatic, their own, and their peers' segmentations from our previous study to edit. We found, independent of source, editing reduced inter-rater variance while maintaining or improving accuracy and improving efficiency with at least 60% reduction in contouring time. In areas where raters performed poorly contouring from scratch, editing of the automatic segmentations reduced the prevalence of total anatomical miss from approximately 16% to 8% of the total slices contained within the ground truth estimations. These findings suggest that contour editing could be useful for consensus building such as in developing delineation standards, and that both automated methods and even perhaps less sophisticated atlases could improve efficiency, inter-rater variance, and accuracy.

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Year:  2013        PMID: 23685866      PMCID: PMC3744837          DOI: 10.1088/0031-9155/58/12/4071

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  22 in total

1.  The adaptive bases algorithm for intensity-based nonrigid image registration.

Authors:  Gustavo K Rohde; Akram Aldroubi; Benoit M Dawant
Journal:  IEEE Trans Med Imaging       Date:  2003-11       Impact factor: 10.048

2.  Variations in the contouring of organs at risk: test case from a patient with oropharyngeal cancer.

Authors:  Benjamin E Nelms; Wolfgang A Tomé; Greg Robinson; James Wheeler
Journal:  Int J Radiat Oncol Biol Phys       Date:  2010-12-01       Impact factor: 7.038

3.  Toward a generic evaluation of image segmentation.

Authors:  Jaime S Cardoso; Luís Corte-Real
Journal:  IEEE Trans Image Process       Date:  2005-11       Impact factor: 10.856

4.  Evaluation of lung MDCT nodule annotation across radiologists and methods.

Authors:  Charles R Meyer; Timothy D Johnson; Geoffrey McLennan; Denise R Aberle; Ella A Kazerooni; Heber Macmahon; Brian F Mullan; David F Yankelevitz; Edwin J R van Beek; Samuel G Armato; Michael F McNitt-Gray; Anthony P Reeves; David Gur; Claudia I Henschke; Eric A Hoffman; Peyton H Bland; Gary Laderach; Richie Pais; David Qing; Chris Piker; Junfeng Guo; Adam Starkey; Daniel Max; Barbara Y Croft; Laurence P Clarke
Journal:  Acad Radiol       Date:  2006-10       Impact factor: 3.173

5.  Comparison of intensity-modulated radiotherapy planning based on manual and automatically generated contours using deformable image registration in four-dimensional computed tomography of lung cancer patients.

Authors:  Elisabeth Weiss; Krishni Wijesooriya; Viswanathan Ramakrishnan; Paul J Keall
Journal:  Int J Radiat Oncol Biol Phys       Date:  2007-12-19       Impact factor: 7.038

6.  A comparison of ground truth estimation methods.

Authors:  Alberto M Biancardi; Artit C Jirapatnakul; Anthony P Reeves
Journal:  Int J Comput Assist Radiol Surg       Date:  2009-12-09       Impact factor: 2.924

7.  Analysis of treatment planning time among systems and planners for intensity-modulated radiation therapy.

Authors:  Indra J Das; Vadim Moskvin; Peter A Johnstone
Journal:  J Am Coll Radiol       Date:  2009-07       Impact factor: 5.532

8.  Evaluation of automatic atlas-based lymph node segmentation for head-and-neck cancer.

Authors:  Liza J Stapleford; Joshua D Lawson; Charles Perkins; Scott Edelman; Lawrence Davis; Mark W McDonald; Anthony Waller; Eduard Schreibmann; Tim Fox
Journal:  Int J Radiat Oncol Biol Phys       Date:  2010-03-16       Impact factor: 7.038

9.  Dosimetric evaluation of automatic segmentation for adaptive IMRT for head-and-neck cancer.

Authors:  Stuart Y Tsuji; Andrew Hwang; Vivian Weinberg; Sue S Yom; Jeanne M Quivey; Ping Xia
Journal:  Int J Radiat Oncol Biol Phys       Date:  2010-03-16       Impact factor: 7.038

10.  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

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  4 in total

Review 1.  Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review.

Authors:  Haomin Chen; Catalina Gomez; Chien-Ming Huang; Mathias Unberath
Journal:  NPJ Digit Med       Date:  2022-10-19

2.  A comparative study of auto-contouring softwares in delineation of organs at risk in lung cancer and rectal cancer.

Authors:  Weijun Chen; Cheng Wang; Wenming Zhan; Yongshi Jia; Fangfang Ruan; Lingyun Qiu; Shuangyan Yang; Yucheng Li
Journal:  Sci Rep       Date:  2021-11-26       Impact factor: 4.379

3.  Multi-modal glioblastoma segmentation: man versus machine.

Authors:  Nicole Porz; Stefan Bauer; Alessia Pica; Philippe Schucht; Jürgen Beck; Rajeev Kumar Verma; Johannes Slotboom; Mauricio Reyes; Roland Wiest
Journal:  PLoS One       Date:  2014-05-07       Impact factor: 3.240

4.  Automatic Intracranial Segmentation: Is the Clinician Still Needed?

Authors:  Nicolas Meillan; Jean-Emmanuel Bibault; Julien Vautier; Caroline Daveau-Bergerault; Sarah Kreps; Hélène Tournat; Catherine Durdux; Philippe Giraud
Journal:  Technol Cancer Res Treat       Date:  2018-01-01
  4 in total

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