AAPM RT-MAC Grand Challenge 2019

<p>This data set was provided in association with a <a href="https://www.aapm.org/GrandChallenge/RT-MAC/">challenge competition</a> and <a href="https://w3.aapm.org/meetings/2019AM/programInfo/programSessions.php?t=all&sid=8284">related conference session</a> conducted at the <a href="https://w3.aapm.org/meetings/2019AM/">AAPM 2019 Annual Meeting</a>. </p><p>MRI is popular in radiation oncology because of its excellent imaging quality of soft tissue and tumor. With the advent of MR-Linac and MR-guided radiation therapy, there is a trend toward a MR-based radiation treatment planning. Contouring is an important task in modern radiation treatment planning and frequently introduces uncertainties in radiation therapy due to observer variabilities. Auto-segmentation has been demonstrated as an effective approach to reduce this uncertainty. The overall objective of this grand challenge is to provide a platform for comparison of various auto-segmentation algorithms when they are used to delineate organs at risk (OARs) or tumors from MR images for head and neck patients for 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><p>The data for this challenge contains a total of 55 MRI cases, each from a single examination from a distinct patient, with each case consisting of a T2-weighted MRI images in DICOM format. The MRI scanning protocol was designed for radiation treatment simulation. Thirty-one of these will be provided as training cases, with the parotid glands, submandibular glands, level 2 and level 3 lymph nodes contoured. The images and contours were acquired from MD Anderson Cancer Center.</p><p>More details on accessing the various challenge subsets (training, off-site test, and live test) can be found on the Detailed Description tab below. </p>

本数据集与AAPM 2019年度会议期间举办的《放疗医学物理挑战赛》及其相关会议环节相关联。核磁共振成像（MRI）因其对软组织和肿瘤卓越的成像质量而在放射肿瘤学领域受到青睐。随着磁共振-直线加速器（MR-Linac）和磁共振引导放射治疗的问世，基于MRI的放射治疗计划正成为一种趋势。轮廓绘制是现代放射治疗计划中的一个关键任务，由于观察者之间的差异，经常导致放射治疗的不确定性。自动分割已被证明是减少这种不确定性的有效方法。本次重大挑战的总体目标是提供一个平台，用于比较各种自动分割算法在从头部和颈部患者的MRI图像中勾勒出风险器官（OARs）或肿瘤时的情况，以指导放射治疗计划。这些结果将表明不同自动分割算法所取得的性能，并在必要时可用于指导这些算法在临床应用中的选择。该挑战的数据包括总计55个MRI病例，每个病例均来自单一患者的单独检查，每个病例包含一幅T2加权MRI图像，格式为DICOM。MRI扫描协议是为放射治疗模拟设计的。其中31个将作为训练案例提供，包括腮腺、下颌下腺、第2级和第3级淋巴结的轮廓。图像和轮廓数据由MD Anderson癌症中心提供。有关访问各种挑战子集（训练、远程测试和现场测试）的更多详细信息，请参阅下方的详细描述标签。

搜集汇总

背景与挑战

背景概述

AAPM RT-MAC Grand Challenge 2019数据集包含55例头部和颈部患者的T2加权MRI图像，旨在评估自动分割算法在描绘风险器官或肿瘤方面的性能。其中31例作为训练集，带有特定器官的轮廓，用于辐射治疗计划的研究和算法比较。