Lung CT Segmentation Challenge 2017

<p>This data set was provided in association with an AAPM Thoracic Auto-segmentation challenge competition and a <a href="http://www.aapm.org/meetings/2017AM/PRAbs.asp?mid=127&aid=35318">related conference session called Auto-Segmentation for Thoracic Radiation Treatment Planning: A Grand Challenge</a> conducted at the <a href="http://www.aapm.org/meetings/2017AM/">AAPM 2017 Annual Meeting</a>. The initial winners were announced at the AAPM meeting, but the competition website remains open to others who wish to see how their algorithms perform.</p><p>Numerous auto-segmentation methods exist for Organs at Risk in radiotherapy. The overall objective of this auto-segmentation grand challenge is to provide a platform for comparison of various auto-segmentation algorithms when they are used to delineate organs at risk (OARs) from CT images for thoracic patients in radiation treatment planning. The results will provide an indication of the performances achieved by various auto-segmentation algorithms and can be used to guide the selection of these algorithms for clinic use if desirable.</p>

本数据集作为AAPM胸部分割挑战赛及相关会议环节“胸部放射治疗计划中的自动分割：一项重大挑战”的配套资料提供，该会议环节于AAPM 2017年度会议上举行。初赛优胜者于AAPM会议期间揭晓，然而竞赛网站持续开放，供有兴趣者观摩其算法的表现。在放射治疗中，针对高风险器官的自动分割方法众多。此次自动分割重大挑战的总体目标是构建一个平台，以便比较不同自动分割算法在从胸部患者的CT图像中分割高风险器官（OARs）时的性能。研究结果将揭示各种自动分割算法所达到的性能水平，如若适用，亦可用于指导临床选择这些算法。