An Interactive Web-Based Game for Collaborative Labeling of Medical Images

描述（由申请人提供）：已证明局部脑解剖学的统计图谱在表征人类神经系统的结构和功能之间的关系方面非常有用。典型地，专家人类评定者手动检查三维体积的每个切片。这种方法可能非常耗费时间和资源，因此成本严重限制了特定受试者标签可行的临床研究。提高人工标记的效率和可靠性的方法对于脑功能的形态学相关性的临床研究将是巨大的益处。建议的工作的目标是通过统计分析的协作努力，许多，最低限度的训练评分，使替代专家评分的医学图像标记。拟议的研究调查扩展既定的做法，体积标签和基于网络的合作，以创建一个创新的基础设施，标签。一个用户界面将被开发为一个互动的基于网络的游戏，用户将解决小难题。每个谜题将代表整个标签挑战的一部分。将在一个统计框架内重新组合来自许多评分者的重叠和互补结果，该框架估计并最大限度地减少标记区域的不确定性。鉴于不同的培训经验、工作环境、计算机硬件和动机，评分员的真实的世界表现很难预测。为了研究该系统的实用性，我们将建立评分者内部、评分者之间的标签的信度测量以及统计重组结果的重测测量，给出三种激励范式：1）将招募带薪实习生进行试点研究; 2）鼓励公众参与竞争性的在线游戏;和3）将进行一项有限的试验1，根据1在1标记1游戏1中的1表现1支付1。1这种标记方法的优势将在脊髓小脑性共济失调的小脑和阿尔茨海默病的小脑中的初步研究中得到证明。1该系统将免费提供。拟议的系统将大大降低创建人工标签的成本，使协作社区的参与，同时提供积极的监测和控制的准确性和不确定性的结果。我们将为标签语言奠定基础，这是一种新颖的，一致的框架，用于以分层，程序化的方式描述标签目标。增加高可靠性手动标签的可用性将提高健康和疾病大脑中定量MRI分析的质量，一致性和效率。公共卫生相关性：拟议的研究调查了一种替代专家评分员手动标记的医学图像，通过统计分析的协作努力，最低限度的培训评分员。基于网络的系统将通过使许多人之间的协作，同时提供对结果的准确性和不确定性的主动监测和控制，大大降低研究感兴趣的解剖区域的成本。

DESCRIPTION (provided by applicant): Statistical atlases of regional brain anatomy have proven to be extremely useful in characterizing the relationship between the structure and function of the human nervous system. Typically, an expert human rater manually examines each slice of a three-dimensional volume. This approach can be exceptionally time and resource intensive, so cost severely limits the clinical studies where subject-specific labeling is feasible. Methods for improved efficiency and reliability of manual labeling would be of immense benefit for clinical investigation into morphological correlates of brain function. The goal of the proposed work is to enable an alternative to expert raters for medical image labeling through statistical analysis of the collaborative efforts of many, minimally-trained raters. The proposed research investigates extension of established practices for volumetric labeling and web- based collaboration to create an innovative infrastructure for labeling. A user interface will be developed as an interactive web-based game in which users will solve small puzzles. Each puzzle will represent a portion of the overall labeling challenge. Overlapping and complementary results from many raters will be recombined within a statistical framework that estimates and minimize the uncertainty of labeled regions. Real world performance of raters is difficult to predict given varied training experience, work environment, computer hardware, and motivation. To investigate the practical utility of this system, we will establish reliability measures for intra-rater, inter-rater labels as well as test-retest metrics for statistically recombined results given three motivational paradigms: 1) Paid interns will be recruited for a pilot study; 2) The public will be encourage to participate in a competitive, online game; and 3) A limited trial will be conducted1 with1 payments1 based1 on1 performance1 in1 the1 labeling1 game .1 The advantages of this labeling approach will be demonstrated in pilot studies of the cerebellum in spinocerebellar ataxia and of the hippocampus1in1Alzheimer s1Disease.1The system will be made freely available. The proposed system will dramatically reduce the cost of creating manual labels by enabling collaborative community involvement, while providing active monitoring and control of the accuracy and uncertainty of the results. We will lay the foundation for a language of labeling, which is a novel, consistent framework for describing labeling objectives in a hierarchical, programmatic manner. Increased availability of high reliability manual labels will improve the quality, consistency, and efficiency of quantitative MRI analyses in the brain in health and disease. PUBLIC HEALTH RELEVANCE: The proposed research investigates an alternative to expert raters for manual labeling of medical images through statistical analysis of the collaborative efforts of many, minimally- trained raters. The web-based system will dramatically reduce the cost of studying anatomical regions of interest by enabling collaboration among many individuals, while providing active monitoring and control of the accuracy and uncertainty of the results.