Literature DB >> 21281897

Atlas-based segmentation improves consistency and decreases time required for contouring postoperative endometrial cancer nodal volumes.

Amy V Young1, Angela Wortham, Iddo Wernick, Andrew Evans, Ronald D Ennis.   

Abstract

PURPOSE: Accurate target delineation of the nodal volumes is essential for three-dimensional conformal and intensity-modulated radiotherapy planning for endometrial cancer adjuvant therapy. We hypothesized that atlas-based segmentation ("autocontouring") would lead to time savings and more consistent contours among physicians. METHODS AND MATERIALS: A reference anatomy atlas was constructed using the data from 15 postoperative endometrial cancer patients by contouring the pelvic nodal clinical target volume on the simulation computed tomography scan according to the Radiation Therapy Oncology Group 0418 trial using commercially available software. On the simulation computed tomography scans from 10 additional endometrial cancer patients, the nodal clinical target volume autocontours were generated. Three radiation oncologists corrected the autocontours and delineated the manual nodal contours under timed conditions while unaware of the other contours. The time difference was determined, and the overlap of the contours was calculated using Dice's coefficient.
RESULTS: For all physicians, manual contouring of the pelvic nodal target volumes and editing the autocontours required a mean±standard deviation of 32±9 vs. 23±7 minutes, respectively (p=.000001), a 26% time savings. For each physician, the time required to delineate the manual contours vs. correcting the autocontours was 30±3 vs. 21±5 min (p=.003), 39±12 vs. 30±5 min (p=.055), and 29±5 vs. 20±5 min (p=.0002). The mean overlap increased from manual contouring (0.77) to correcting the autocontours (0.79; p=.038).
CONCLUSION: The results of our study have shown that autocontouring leads to increased consistency and time savings when contouring the nodal target volumes for adjuvant treatment of endometrial cancer, although the autocontours still required careful editing to ensure that the lymph nodes at risk of recurrence are properly included in the target volume.
Copyright © 2011 Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 21281897     DOI: 10.1016/j.ijrobp.2010.04.063

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


  25 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

Review 2.  Automated Radiation Treatment Planning for Cervical Cancer.

Authors:  Dong Joo Rhee; Anuja Jhingran; Kelly Kisling; Carlos Cardenas; Hannah Simonds; Laurence Court
Journal:  Semin Radiat Oncol       Date:  2020-10       Impact factor: 5.934

3.  Cardiac Substructure Segmentation and Dosimetry Using a Novel Hybrid Magnetic Resonance and Computed Tomography Cardiac Atlas.

Authors:  Eric D Morris; Ahmed I Ghanem; Milan V Pantelic; Eleanor M Walker; Xiaoxia Han; Carri K Glide-Hurst
Journal:  Int J Radiat Oncol Biol Phys       Date:  2018-11-22       Impact factor: 7.038

4.  Automatic contour segmentation of cervical cancer using artificial intelligence.

Authors:  Yosuke Kano; Hitoshi Ikushima; Motoharu Sasaki; Akihiro Haga
Journal:  J Radiat Res       Date:  2021-09-13       Impact factor: 2.724

5.  Efficacy evaluation of 2D, 3D U-Net semantic segmentation and atlas-based segmentation of normal lungs excluding the trachea and main bronchi.

Authors:  Takafumi Nemoto; Natsumi Futakami; Masamichi Yagi; Atsuhiro Kumabe; Atsuya Takeda; Etsuo Kunieda; Naoyuki Shigematsu
Journal:  J Radiat Res       Date:  2020-03-23       Impact factor: 2.724

6.  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

7.  MEMRI-based imaging pipeline for guiding preclinical studies in mouse models of sporadic medulloblastoma.

Authors:  Harikrishna Rallapalli; I-Li Tan; Eugenia Volkova; Alexandre Wojcinski; Benjamin C Darwin; Jason P Lerch; Alexandra L Joyner; Daniel H Turnbull
Journal:  Magn Reson Med       Date:  2019-08-12       Impact factor: 4.668

8.  Technology assessment of automated atlas based segmentation in prostate bed contouring.

Authors:  Jeremiah Hwee; Alexander V Louie; Stewart Gaede; Glenn Bauman; David D'Souza; Tracy Sexton; Michael Lock; Belal Ahmad; George Rodrigues
Journal:  Radiat Oncol       Date:  2011-09-09       Impact factor: 3.481

Review 9.  Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review.

Authors:  Michael V Sherer; Diana Lin; Sharif Elguindi; Simon Duke; Li-Tee Tan; Jon Cacicedo; Max Dahele; Erin F Gillespie
Journal:  Radiother Oncol       Date:  2021-05-11       Impact factor: 6.901

10.  Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer.

Authors:  Mariangela La Macchia; Francesco Fellin; Maurizio Amichetti; Marco Cianchetti; Stefano Gianolini; Vitali Paola; Antony J Lomax; Lamberto Widesott
Journal:  Radiat Oncol       Date:  2012-09-18       Impact factor: 3.481

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