Data from Lung CT Segmentation Challenge 2017 (LCTSC)

This data set was provided in association with a challenge competition and related conference session conducted at the AAPM 2017 Annual Meeting. 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.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.本数据集与2017年美国医学物理师协会（American Association of Physicists in Medicine, AAPM）年会中举办的挑战赛及配套会议环节联合发布。首批优胜者已于AAPM年会期间揭晓，但该竞赛官网仍对外开放，供其他研究者验证自身算法的实际性能。放疗领域针对危及器官（Organs at Risk, OARs）已存在多种自动分割方法。本次自动分割大赛的核心目标，是搭建一个可供各类自动分割算法比对的平台，这些算法需应用于放射治疗计划制定环节，从胸部患者的计算机断层扫描（CT）图像中勾勒危及器官（OARs）。大赛结果可直观反映各类自动分割算法的性能表现，若有需求，还可用于指导临床场景中此类算法的选型工作。

搜集汇总

背景与挑战

背景概述

该数据集源自2017年肺部CT分割挑战赛（LCTSC），与AAPM年度会议相关，主要用于比较放疗中自动分割算法在胸部CT图像上勾画危及器官的性能。其核心目的是提供一个评估平台，帮助指导临床实践中算法的选择，以提升放疗治疗规划的准确性和效率。