Auto-segmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017

• Autor(es): Yang, Jinzhong ; Veeraraghavan, Harini ; Armato, Samuel G. ; Farahani, Keyvan ; Kirby, Justin S. ; Kalpathy-Kramer, Jayashree ; van Elmpt, Wouter ; Dekker, Andre ; Han, Xiao ; Feng, Xue ; Aljabar, Paul ; Oliveira, Bruno ; van der Heyden, Brent ; Zamdborg, Leonid ; Lam, Dao ; Gooding, Mark ; Sharp, Gregory C.
• Data: 2018
• Identificador Persistente: https://hdl.handle.net/1822/58905
• Origem: RepositóriUM - Universidade do Minho
• Assunto(s): Algorithms; Humans; Organs at Risk; Radiotherapy Planning, Computer-Assisted; Radiotherapy, Image-Guided; Thorax; Tomography, X-Ray Computed; automatic segmentation; grand challenge; lung cancer; radiation therapy
• Tipo de Documento: Artigo científico
• Idioma: Inglês
• Contribuidor(es): Universidade do Minho

Descrição

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.