Ghanian Zahra, Pezeshk Aria, Petrick Nicholas, Sahiner Berkman
U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, Maryland, United States.
J Med Imaging (Bellingham). 2018 Oct;5(4):044502. doi: 10.1117/1.JMI.5.4.044502. Epub 2018 Nov 19.
Mammographic computer-aided detection (CADe) devices are typically first developed and assessed for a specific "original" acquisition system. When developers are ready to apply their CADe device to a mammographic acquisition system, they typically assess the device with images acquired using the system. Collecting large repositories of clinical images containing verified lesion locations acquired by a system is costly and time consuming. We previously developed an image blending technique that allows users to seamlessly insert regions of interest (ROIs) from one medical image into another image. Our goal is to assess the performance of this technique for inserting microcalcification clusters from one mammogram into another, with the idea that when fully developed, our technique may be useful for reducing the clinical data burden in the assessment of a CADe device for use with an image acquisition system. We first perform a reader study to assess whether experienced observers can distinguish between computationally inserted and native clusters. For this purpose, we apply our insertion technique to 55 clinical cases. ROIs containing microcalcification clusters from one breast of a patient are inserted into the contralateral breast of the same patient. The analysis of the reader ratings using receiver operating characteristic (ROC) methodology indicates that inserted clusters cannot be reliably distinguished from native clusters (area under the ROC ). Furthermore, CADe sensitivity is evaluated on mammograms of 68 clinical cases with native and inserted microcalcification clusters using a commercial CADe system. The average by-case sensitivities for native and inserted clusters are equal, 85.3% (58/68). The average by-image sensitivities for native and inserted clusters are 72.3% and 67.6%, respectively, with a difference of 4.7% and a 95% confidence interval of [ 11.6]. These results demonstrate the potential for using the inserted microcalcification clusters for assessing mammographic CADe devices.
乳腺X线计算机辅助检测（CADe）设备通常首先针对特定的“原始”采集系统进行开发和评估。当开发者准备将其CADe设备应用于乳腺X线采集系统时，他们通常会使用该系统采集的图像对设备进行评估。收集包含经系统采集且已验证病变位置的大量临床图像库既昂贵又耗时。我们之前开发了一种图像融合技术，该技术允许用户将一幅医学图像中的感兴趣区域（ROI）无缝插入到另一幅图像中。我们的目标是评估将微钙化簇从一幅乳腺X线图像插入到另一幅图像中的这项技术的性能，我们认为，当该技术完全成熟后，可能有助于减轻在评估用于图像采集系统的CADe设备时的临床数据负担。我们首先进行了一项阅片者研究，以评估有经验的观察者能否区分通过计算插入的簇和天然簇。为此，我们将插入技术应用于55例临床病例。将包含患者一侧乳房微钙化簇的ROI插入到同一患者的对侧乳房中。使用接收者操作特征（ROC）方法对阅片者评分进行分析表明，无法可靠地区分插入的簇和天然簇（ROC曲线下面积）。此外，使用商用CADe系统对68例含有天然和插入微钙化簇的临床病例的乳腺X线图像进行CADe敏感性评估。天然簇和插入簇的平均病例敏感性相等，为85.3%（58/68）。天然簇和插入簇的平均图像敏感性分别为72.3%和67.6%，差异为4.7%，95%置信区间为[ 11.6]。这些结果证明了使用插入的微钙化簇评估乳腺X线CADe设备的潜力。
