Autosegmentation for thoracic radiation treatment planning

Accepted manuscript
This report presents the methods and results of the Thoracic Auto-Segmentation Challenge organized at the 2017 Annual Meeting of American Association of Physicists in Medicine. The purpose of the challenge was to provide a benchmark dataset and platform for evaluating performance of autosegmentation methods of organs at risk (OARs) in thoracic CT images.
The challenge organizers would like to thank Artem Mamonov and Andrew Beers from Harvard Medical School for providing valuable support in creating the challenge scoring system on the challenge website, Kirk Smith and Tracy Nolan from University of Arkansas for Medical Sciences for data curation to TCIA, Tim Lustberg from Maastro Clinic for data collection, and AAPM for sponsoring this challenge. Of the challenge participants, Brent van der Heyden would like to thank Frank Verhaegen and Mark Podesta for valuable discussions; Bruno Oliveira would like to thank Sandro Queirós, Pedro Morais, Helena R. Torres, Jaime C. Fonseca, João Gomes-Fonseca, and João L. Vilaça for all the contributions to his work; Leonid Zamdborg would like to acknowledge Thomas M Guerrero and Edward Castillo. This project was support in part by the CPRIT (Cancer Prevention Research Institute of Texas) grant No. RP110562-P2, the National Institutes of Health Cancer Center Support (Core) grant No. CA016672 to the University of Texas MD Anderson Cancer Center, and in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government
info:eu-repo/semantics/publishedVersion