Arora Rachna, Kundel Harold L, Beam Craig A
Biostatistics Core, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, Florida 33612, USA.
Acad Radiol. 2005 Dec;12(12):1567-74. doi: 10.1016/j.acra.2005.06.015.
Analysis of reading data when cases have multiple targets and/or the reader is required to localize targets is difficult. One approach to this free-response operating characteristic (FROC) problem is for images to be segmented (eg, with quadrants) by the investigator and a segment-level analysis be conducted with the case as a nesting factor. In this report, we introduce an alternative method that uses the visual scan path of the reader to segment the image. We evaluate the new method by applying it to data from a mammography reading experiment.
The gaze scan path of one radiologist was recorded as she scanned 40 mammograms for masses and microcalcifications. The observer is an experienced mammographer and was not one of the authors. In addition, the reader provided a rating indicating the degree of suspicion for any suspected targets she identified and localized. We then established "perceptual regions" by using a clustering algorithm on the visual fixations. We combined ratings given to specific locations indicated by the reader with the segmentation from the visual scan to generate a series of ratings classified for whether the perceptually based region associated with the rating contained or did not contain a known target. We analyzed data generated by our method from all 40 cases by using the conventional maximum-likelihood method based on the binormal model. Finally, we tested goodness-of-fit of the binormal model to the data by using chi-square.
Maximum-likelihood estimation led to a model that did not fit the data (P < .001). However, examination of the observed and expected counts suggests that the binormal assumption does not hold for segments that contain targets and a bimodal distribution model might be preferred.
Our new method provides an alternative approach to analysis of the FROC experiment. It needs to be developed further. Specifically, we propose that a mixture model extension of the binormal model be developed for ratings data arising from perceptually based FROC experiments. A disadvantage to our method is the requirement to record the scan path of the reader. However, we believe that adding such information to receiver operating characteristic (ROC) curve analysis will pay off when appropriate statistical models have been identified because we believe our data support our hypothesis that the perceptual scanning of images by humans deconvolves interpretation correlation. If true, this hypothesis implies that conventional statistical methods for ROC analysis based on independent data can be applied to the analysis of FROC data after conditioning on the scan path of the observer.
当病例有多个目标和/或要求阅片者对目标进行定位时，阅读数据的分析很困难。解决这个自由反应操作特征（FROC）问题的一种方法是由研究者对图像进行分割（例如，划分为象限），并以病例作为嵌套因素进行片段水平的分析。在本报告中，我们介绍了一种利用阅片者的视觉扫描路径对图像进行分割的替代方法。我们将该新方法应用于乳腺X线摄影阅读实验的数据，以对其进行评估。
记录了一位放射科医生在扫描40幅乳腺X线片以查找肿块和微钙化时的注视扫描路径。该观察者是一位经验丰富的乳腺造影技师，并非本文作者。此外，阅片者对她识别和定位的任何可疑目标给出了表示怀疑程度的评分。然后，我们通过对视觉注视点使用聚类算法来建立“感知区域”。我们将阅片者对特定位置给出的评分与视觉扫描的分割结果相结合，以生成一系列根据基于感知的区域是否包含已知目标进行分类的评分。我们使用基于双正态模型的传统最大似然法分析了我们的方法从所有40个病例中生成的数据。最后，我们使用卡方检验双正态模型对数据的拟合优度。
最大似然估计得到的模型与数据不拟合（P <.001）。然而，对观察值和期望值的检查表明，双正态假设对于包含目标的片段不成立，可能更适合采用双峰分布模型。
我们的新方法为FROC实验的分析提供了一种替代方法。它需要进一步发展。具体而言，我们建议为基于感知的FROC实验产生的评分数据开发双正态模型的混合模型扩展。我们方法的一个缺点是需要记录阅片者的扫描路径。然而，我们认为，当确定了合适的统计模型后，将此类信息添加到接收者操作特征（ROC）曲线分析中会有回报，因为我们相信我们的数据支持我们的假设，即人类对图像的感知扫描消除了解释相关性。如果这是真的，这个假设意味着基于独立数据的传统ROC分析统计方法可以在以观察者的扫描路径为条件后应用于FROC数据的分析。
Zotero 插件